big w1 (2)

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Q-1

This book is a classic in the world of Big Data. You are to read the book “Big_Data_Now_2012_Edition”w and provide your instructor with a two-page technical annotation of the book in a word or PDF document to be submitted to your instructor.

Q-2

When considering the research that was conducted for the article, and the information that the researchers are trying to convey, and it is crucial to have as much data as possible to support the intent of the research. In this case study, you are to read the article and present your point of view as to whether the data supports the findings, or the results are skewed. When considering your point of view it is necessary to identify what the researcher’s conclusion is attempting to accomplish. Are the researchers trying to support a hypothesis or they constructing a solution to a situation that needs to be open to discussion?

Your response should be a minimum of four paragraphs and should be a minimum of 400 and 450 words. The paragraphs are single-spaced. There should be a minimum of three scholarly references supporting your observations. Citations are to follow 7.0.

NOTE: 
All written assignments must conform to the guidelines set forth by the American Psychological Association.
According to APA 7, the following font styles and size are now accepted:

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Editorial—Big Data, Data Science, and Analytics: The
Opportunity and Challenge for IS Research
Ritu Agarwal, Vasant Dhar

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Ritu Agarwal, Vasant Dhar (2014) Editorial—Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS
Research. Information Systems Research 25(3):443-448. https://doi.org/10.1287/isre.2014.0546

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Information Systems Research
Vol. 25, No. 3, September 2014, pp. 443–448
ISSN 1047-7047 (print) � ISSN 1526-5536 (online) http://dx.doi.org/10.1287/isre.2014.0546

© 2014 INFORMS

Editorial

Big Data, Data Science, and Analytics:
The Opportunity and Challenge for IS Research

Ritu Agarwal
Center for Health Information and Decision Systems, Robert H. Smith School of Business, University of Maryland,

College Park, Maryland 20742, [email protected]

Vasant Dhar
Center for Business Analytics, Stern School of Business, New York University, New York, New York 10012,

[email protected]

We address key questions related to the explosion of interest in the emerging fields of big data, analytics, and
data science. We discuss the novelty of the fields and whether the underlying questions are fundamentally

different, the strengths that the information systems (IS) community brings to this discourse, interesting research
questions for IS scholars, the role of predictive and explanatory modeling, and how research in this emerging
area should be evaluated for contribution and significance.

Introduction
It is difficult, nay, impossible to open a popular pub-
lication today, online or in the physical world and not
run into a reference to data science, analytics, big data,
or some combination thereof. To use a Twitter-esque
phrase, that’s what’s trending now. A search on
Google in the middle of August 2014 for the phrases
“Big data,” “Analytics,” and “Data science” yielded
822 million, 154 million, and 461 million results,
respectively. Our major journals (Management Science,
MIS Quarterly) have commissioned special issues on
these topics, and a large number of position announce-
ments in the information systems field are specifying
knowledge of, and skills in, one or more of these areas
as desirable, if not a requirement for the job. A new
journal called Big Data,1 launched just over a year ago,
is already seeing thousands of downloads of articles. It
would not be hyperbole to claim that big data is possi-
bly the most significant “tech” disruption in business
and academic ecosystems since the meteoric rise of the
Internet and the digital economy.

What does this tsunami mean for information sys-
tems researchers? Rather than simply rely on our own
view of the world, we invited other accomplished
scholars with a history of publication in this arena to
participate in the conversation. We posed five ques-
tions to frame our thinking about the domain:

1 See http://www.liebertpub.com/overview/big-data/611.

1. Are big data, analytics, and data science, as being
described in the popular outlets, old wine in new bot-
tles or is it something new?

2. What are the strengths that the information
systems (IS) community brings to the discourse on
business analytics? In other words, what is our com-
petitive advantage?

3. What are important and interesting research
questions and domains that may “fit” with on-going
research in our community? How might we push
the envelope by extending or modifying our existing
research agendas? What about new areas of inquiry?

4. To what extent should robust prediction prowess
be used as a criterion in evaluating data-driven mod-
els versus current criteria that favor “explanatory”
models without subjecting them to rigorous tests of
future predictability?

5. As editors and reviewers, how should we evalu-
ate research in this domain? What constitutes a “sig-
nificant” contribution?

We would like to acknowledge the contributions
of Galit Shmueli and Martin Bichler whose thought-
ful responses to these questions gave us much food
for contemplation. In the rest of this commentary we
offer a synthesis of our collective reflection on the five
questions.

On the Novelty of Big Data,
Analytics and Data Science
We believe that some components of data science and
business analytics have been around for a long time,

443

Agarwal and Dhar: The Opportunity and Challenge for IS Research
444 Information Systems Research 25(3), pp. 443–448, © 2014 INFORMS

but there are significant new questions and oppor-
tunities created by the availability of big data and
major advancements in machine intelligence.2 While
the notion that analytical techniques can be used to
make sense of and derive insights from data is as
old as the field of statistics, and dates back to the
18th century, one obvious difference today is the rapid
pace at which economic and social transactions are
moving online, allowing for the digital capture of big
data. The ability to understand the structure and con-
tent of human discourse has considerably expanded
the dimensionality of data sets available. As a result,
the set of opportunities for inquiry has exploded
exponentially with readily available large and com-
plex data sets related to any type of phenomenon
researchers want to study, ranging from deconstruct-
ing the human genome, to understanding the pathol-
ogy of Alzheimer’s disease across millions of patients,
to observing consumer response to different market-
ing offers in large scale field experiments. And, easy
(and relatively inexpensive) access to computational
capacity and user-friendly analytical software have
democratized the field of data science allowing many
more scholars (and practitioners) to participate in the
opportunities enabled by big data.

In some ways in could be argued that the nature of
inquiry has also changed, turbocharged by machines
becoming a lot smarter through better algorithms, and
by information technologies that enable people and
things to be inherently instrumented for observation
and interaction that feeds the algorithms. Increasingly,
data are collected not with the aim of solely testing
a human-generated hypotheses or essential record-
keeping, but to the extent that data torrents are cap-
tured inexpensively, often for the possibility of testing
hypotheses that have not yet been envisioned at the
time of collection. When such data are gathered on a
scale that observes every part of the joint distributions
of the observed variables (behaviors, demographics,
etc.), the computer becomes an active question ask-
ing machine as opposed to a pure analytic servant.
By initiating interesting questions and refining them
without active human intervention, it becomes capa-
ble of creating new knowledge and making discover-
ies on its own (Dhar 2013). It can, for example, dis-
cover automatically from a large swath of healthcare
system data that younger people in a specific region
of the world are becoming increasingly diabetic and
then conjecture and test whether the trend is due to
specific habits, diet, specific types of drugs, and a

2 Arguably, for the first time in history, a machine passed the
famous Turing test by defeating human champions at Jeopardy
where topics are not known in advance and questions are posed in
natural language of considerable complexity and nuance.

range of factors we may not have hypothesized as
humans. This is powerful. As scientists, we have not
seriously entertained the possibility of theory origi-
nating in the computer, and as science-fiction-like as
that may sound, we are in principle already there.

New challenging problems and inquiry also lead
to research on better algorithms and systems. Since
the torrent of data being generated is increasingly
unstructured and coming from networks of people or
devices, we are seeing the emergence of more pow-
erful algorithms and better knowledge representation
schemes for making sense of all of this heteroge-
neous and fragmented information. Text and image
processing capability are one frontier of research, with
systems such as IBM’s Watson being on the cutting
edge in natural language processing, albeit with a
long way to go in terms of their capability for ingest-
ing and interpreting big data across the Internet.

Networks, such as those created by connections
between individuals and/or products, further create
significant and unique challenges at a fundamental
level such as how we sample them or infer treat-
ment effects. For example, in A/B testing, a “standard
approach” for estimating the average treatment effect
of a new feature or condition by exposing a sam-
ple of the overall population to it, the treatment of
individuals can spill over to neighboring individu-
als along the structure of the underlying network.
To address this type of “social interference,” newer
algorithms are required that support valid sampling
and estimation of treatment effects (Ugander et al.
2013). This is but one example of how “relational” and
“networked” data necessitates new development in
algorithms. Developments may emerge not only from
computer science but also from IS or other disciplines
where researchers are “closer” to the problem being
studied than pure methods researchers tend to be.

Finally, the Internet has fueled our ability to con-
duct large scale experiments on social phenomena.
As of this writing, Facebook researchers conducted
a massive study to determine whether the mood of
users could be manipulated and found that it could
(Kramer et al. 2014). By conducting controlled exper-
iments in large numbers of people such studies can
extract the causal structure among variables. While
the study raised important questions about privacy
and the ethical implications of conducting experi-
ments without informed consent, the broader point
is that researchers now have a medium for theory
development through massive experimentation in the
social, health, urban, and other sciences.

A Comparative Advantage for IS?
Arguably, this is the golden age for IS researchers.
Data science and big data research from IS has

Agarwal and Dhar: The Opportunity and Challenge for IS Research
Information Systems Research 25(3), pp. 443–448, © 2014 INFORMS 445

attracted attention at the level of widely read scien-
tific outlets such as PNAS and Science (Aral et al.
2009, Aral and Walker 2012) because of the impor-
tance and generic nature of the questions asked, such
as “are choices in social networks a result of influ-
ence or homophily?” Such a question has profound
implications for phenomena too numerous to mention
that involve how we communicate, persuade, moni-
tor, and more. In other words, perhaps it is time to
set our sights higher, beyond our traditional journals,
to communicate with the larger community of scien-
tists and businesses. It is a time of opportunity for
social scientists that have heretofore been hamstrung
by the lack of data. For the first time we are able
to observe and measure human behavior on a global
scale. IS researchers are, quite incredibly, at the center
of the digital world unfolding before us.

To put things in historical perspective, one could
reasonably assert that the IS discipline has the longest
history of conducting research at the nexus of com-
puting technology and data in business and society.
In fact, it is widely recognized that the discipline of
“MIS” emerged when computers enabled the automa-
tion of business processes and the digital capture of
business transactions. Understanding how to design
and implement systems to provide the “right infor-
mation to the right person at the right time” was
the raison d’être of IS programs and defined much
of early IS research. These endeavors entailed under-
standing what individuals’ and executives data needs
are, designing structures to capture and manage data,
and conceptualizing interfaces that made the data
accessible and usable. So, it is not unreasonable to
claim that IS scholars started off with a comparative
advantage with respect to big data: we knew how to
store, manage, process data; and about the complex-
ity of data structures very early on in the history of
computing. We also understand the challenges associ-
ated with the infrastructure needed for handling the
volume of data being generated today.

Armed with these skills and tools, IS scholars have
been catalyzed to focus on problems and outcomes.
As has been suggested elsewhere (Agarwal and Lucas
2005) among all functional areas of business, IS re-
searchers perhaps have the broadest perspective on
the enterprise as a whole, and how different pieces fit
together. This focus creates a tighter linkage between
data and business models: we care deeply about
business transformation and value creation through
data, and less for algorithms or frameworks without
a linkage to business value. Our research has been
alternately praised and criticized for being too cross-
disciplinary, but we believe this is strength and not a
weakness in today’s data rich environment. IS scholars
have invoked theories from the fields of economics,

sociology, psychology, and political science to name a
few, and studied phenomena such as electronic mar-
kets, consumer behavior, crowdsourcing, information
security, and online retailing from a diversity of per-
spectives. This cross- and transdisciplinary nature of
IS research to date positions us uniquely to exploit
the big data opportunity. We can do so by addressing
the same types of questions as we have in the past
but with significantly richer data sets, or we can begin
to initiate new inquiries that represent questions that
were not possible to ask and answer previously.

On Research Questions
The wealth of possibilities enabled by big data is too
many to enumerate in a brief commentary. Nonethe-
less, we offer some illustrations of fruitful research
projects that IS scholars could begin to explore (and,
evidence suggests that many already are). First, the
ability to observe and measure micro, individual-level
data on a comprehensive scale enables us to address
grand problems on a societal level with deep pol-
icy implications that go beyond the confines of a
single organization. Examples include using micro-
level technology usage data to ask if ubiquitous dig-
itization exacerbates or diminishes social inequities,
exploring how labor markets are evolving by exam-
ining the actions of workers on social networking
and employment sites, and investigating if the quan-
tified self-movement with sensors tracking exercise
and nutrition information during daily living is, in
fact, producing a measurable effect on the incidence
of disease or health problems. Indeed, the entire field
of personalized medicine (and, the heretofore under-
studied opportunity of personalized technology inter-
ventions) is enabled by big data.

Second, following the call to focus on the transfor-
mational aspects of IT (Lucas et al. 2013), big data
allows for the design and execution of studies related
to the profound changes our own profession is expe-
riencing. We could extend our existing research agen-
das that have explored the effects of technology medi-
ation on learning outcomes, learner satisfaction, etc.,
to examine individual-level effects of MOOCs, online
courses, blended learning, and the like on an unprece-
dented scale.

Third, and this is an area where perhaps IS re-
search has a robust set of studies to build on, is big
data research in the context of social networks and
marketing outcomes. Geo-coded social media interac-
tions coupled with extensive demographic and socioe-
conomic data allow us to quantify how networks
affect micro (individual behavior), meso (organiza-
tional value), and societal outcomes (economic and
social value). Pervasive mobile devices and the rapid
rise in commercial transactions (banking, purchase,

Agarwal and Dhar: The Opportunity and Challenge for IS Research
446 Information Systems Research 25(3), pp. 443–448, © 2014 INFORMS

etc.) on mobile platforms that can be captured and
recorded enable novel insights into the classic market-
ing problems of advertising and promotions, and their
effects on sales. And of course, the ability to run field
experiments in these settings, varying interventions
on a grand scale, substantially enhances the scope of
causal relationships we can extract from the data.

Big Data, and Prediction vs.
Explanation
At the time of writing this editorial, two big data
stories were receiving substantial media attention.
One, the scathing review of Google flu trends (Lazer
et al. 2014) (that uses search terms to predict the inci-
dence of flu) with respect to its accuracy as com-
pared with estimates produced by the Centers for
Disease Control and Prevention, and two, the ethi-
cal debate ignited by experiments conducted on Face-
book. In some ways both stories are related to the
issue of whether big data are useful solely for pre-
diction or also for an understanding of the causal
processes that are yielding the observed outcomes.
In the case of Google flu trends, the algorithm has
been criticized for overfitting a small number of cases
and masking a simple question, namely, does it pre-
dict flu or is it merely reflecting the incidence of
winter, and for not taking into account the fact that
technologies like Google’s search engine are profit-
driven and changing and therefore limited for sci-
entific inquiry. While the Facebook study raises the
specter of widespread experimentation on the Inter-
net without adequate protection of individual rights
and privacy, ironically, the experiment is a great
example of where causal claims can be made with
some confidence. This tension between correlational
analysis and causal testing of hypotheses represents a
fundamental dilemma in the use of big data for expla-
nation versus prediction.

But we should not be too hasty in dismissing pre-
diction. Indeed, as has been argued by the philoso-
pher of science Karl Popper, prediction is a key epis-
temic criterion for assessing how seriously we should
entertain a theory or a new insight: a good theory
makes “bold” predictions that stand repeated effects
as falsification (Popper 1963). Popper goes on to criti-
cize certain social science theories like those of Adler
or Marx that can conveniently “bend” the theory to
accommodate even contradictory data without any
onus on prediction whatsoever.3 In this regard, we

3 Popper used opposite cases of a man who pushes a child into the
water with the intention of drowning the child and that of a man
who sacrifices his life in an attempt to save the child. In Adler’s
view, the first man suffered from feelings of inferiority (produc-
ing perhaps the need to prove to himself that he dared to commit

should encourage additional rigorous testing of mod-
els on data that were collected later in time than those
used to construct the models.

Prediction as an initial basis for theory building also
has particular value in a world where patterns often
emerge before the reasons for them become apparent
(Dhar 2013). While the scientific process is predicated
on a cycle of hypothesis generation, experimentation,
hypothesis testing, and inference, there are multiple
starting points. Big data are as, and increasingly more
so, useful at the hypothesis generation stage as they
are at the hypothesis testing phase. A study that seeks
to predict rather than explain may reveal associa-
tions between variables that form the foundation for
the development of theory that can be subsequently
subject to rigorous testing. In this context, Shmueli
(2010) provides an elegant summary of when predic-
tive modeling can be particularly useful in scientific
endeavors, including newly available rich data sets
with newly measured concepts for which theory is yet
to be developed (as can be the case with big data), and
for the discovery of new measures. Scientific progress
does not rely on a single study, and big data-based
studies offer the promise of novel discoveries.

Some domains may often view prediction to be as,
if not more, valuable than explanation. A compelling
example of this is in healthcare, where the cost of
delaying action based on a good predictive model
until the construction and testing of explanatory mod-
els is complete, is measured in lives that may be
lost. This is not to say that clinical and biomedi-
cal researchers do not seek to build causal models,
quite the contrary. The far-reaching human genome
project and recently launched efforts to understand
the human brain in greater detail aim to unravel the
underlying causal structures of disease. But in many
instances prediction with big data in and of itself is of
immense value such as determining the probability of
hospital re-admittances, or the risk of development of
hepatocellular carcinoma among patients with cirrho-
sis (Waljee et al. 2014). The biomedical community has
begun to acknowledge that big data provides a criti-
cal complement to the gold standard of randomized
controlled trials by supporting massive observational
studies that were not feasible before Weil (2014).

Evaluating Knowledge Claims
Based on Big Data
There is little doubt that the IS community will in-
creasingly look for ways to conduct innovative re-
search that leverages the power of big data. Some of

the crime), and so did the second man (whose need was to prove
to himself that he dared to rescue the child at the expense of his
own life).

Agarwal and Dhar: The Opportunity and Challenge for IS Research
Information Systems Research 25(3), pp. 443–448, © 2014 INFORMS 447

this research may be “non-traditional” in that it devel-
ops predictive models of phenomena for their own
sake or as a first step towards theory building. Some
of it may use new variables and measures enabled
by the digital capture of social and economic activ-
ity, and user generated context creatively combined
with external data sets such as that obtained from the
Census Bureau or the Bureau of Labor Statistics. How
should we, as editors and reviewers, evaluate such
research?

There is no simple answer to this question and, to
a large extent, our standards and methods of eval-
uation will inevitably evolve as our experience with
such research grows. With respect to ISR in particu-
lar, we revert to the general criteria used by editors
and reviewers when assessing research: Fit, Interest-
ingness, Rigor, Story, Theory (Agarwal 2012). From
the standpoint of fit, certainly novel and original
research that uses big data to address a phenomenon
of interest to the IS community “fits” with the mis-
sion of ISR, and is welcomed. Among the other cri-
teria, while it is difficult to provide a rank ordering,
we suspect that interestingness and rigor will be, at
the margin, more important in evaluating research
based on big data, at least in these early stages. Above
all, the research must pose an interesting and rel-
evant question. We expect that questions that chal-
lenge conventional wisdom and intuition or those that
pose a hitherto unexplored puzzle, i.e., those that are
novel, will be received more positively by review-
ers than those that have been examined extensively
in prior work. But we should also encourage testing
and replication of prior results since such research is
key to scientific advancement, as argued eloquently
by Popper (1963). Unfortunately, such work is often
given short-shrift relative to research that claims new
results based on data sets that are not shared and
do not provide opportunity for falsification or con-
firmation. Data sharing and transparency must be
encouraged.

Interesting is hard to define explicitly since it could
arise in many different forms. Part of the interesting-
ness of a question might well derive solely from the
power of big data, where it becomes possible to inves-
tigate a previously formulated research question with
a novel and extensive data set that integrates observa-
tions from diverse sources. Indeed, it is entirely pos-
sible that the contribution of a study lies primarily in
the uniqueness of the data set and the rigor of the
empirical methods used to analyze the data.

With respect to rigor, we do not expect the stan-
dards for evaluating knowledge claims in studies
using big data to be substantially different from those
using more traditional data sets. Reviewers would
expect the usual threats to inferential validity and

causal claims including self-selection and identifica-
tion to be adequately accounted for. Researchers must
also pay attention to the special challenges of working
with very large data sets and reflect thoughtfully on
the economic significance of their findings. In contrast
to studies that utilize archival data collected by “trust-
worthy” entities such as businesses, governmental
and other agencies, or studies based on primary data
collection by researchers, big data could originate
from unknown sources with questionable at best or
unknown at worst credentials. It is incumbent upon
authors to convince readers that their “big” data set
was validly generated from appropriate foundations.

Second, and this challenge is not unique to big data
but may be compounded by sheer size and variety in
variables, data that the researcher does not generate
herself is often an imperfect observation for the real
world concept that is being referred to. For exam-
ple, if we are not able to capture individuals’ income
in a social network, is the zip code in which they
reside an appropriate proxy for socioeconomic sta-
tus? The answer, of course, depends on a variety of
interrelated factors including the nature of the study,
its research questions, other variables the researcher
has, etc. Nonetheless, this example serves to illustrate
the special difficulty that may be amplified as a func-
tion of the number of variables in the data set, espe-
cially when they are integrated from multiple sources.

Closing Thoughts
As a community of scholars we would be remiss not
to take full advantage of the scientific possibilities cre-
ated by the availability of big data, sophisticated ana-
lytical tools, and powerful computing infrastructures.
Indeed, for reasons mentioned above, this is an excit-
ing time to be an IS researcher and to think beyond IS
to science in general. Big data still aims in large part
to deliver the right information to the right person at
the right time in the right form, but is now able to
do so in a significantly more sophisticated form. The
IS discipline has been thinking and researching ques-
tions at the intersection of technology, data, business,
and society for five decades and should leverage its
thought leadership to become a centerpiece of educa-
tion, business, and policy.

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Table of Contents

1. Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2. Getting Up to Speed with Big Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
What Is Big Data? 3

What Does Big Data Look Like? 4
In Practice 8

What Is Apache Hadoop? 10
The Core of Hadoop: MapReduce 11
Hadoop’s Lower Levels: HDFS and MapReduce 11
Improving Programmability: Pig and Hive 12
Improving Data Access: HBase, Sqoop, and Flume 12
Coordination and Workflow: Zookeeper and Oozie 14
Management and Deployment: Ambari and Whirr 14
Machine Learning: Mahout 14
Using Hadoop 15

Why Big Data Is Big: The Digital Nervous System 15
From Exoskeleton to Nervous System 15
Charting the Transition 16
Coming, Ready or Not 17

3. Big Data Tools, Techniques, and Strategies. . . . . . . . . . . . . . . . . . . . . 19
Designing Great Data Products 19

Objective-based Data Products 20
The Model Assembly Line: A Case Study of Optimal

Decisions Group 21
Drivetrain Approach to Recommender Systems 25
Optimizing Lifetime Customer Value 28
Best Practices from Physical Data Products 31
The Future for Data Products 35

iii

What It Takes to Build Great Machine Learning Products 35
Progress in Machine Learning 36
Interesting Problems Are Never Off the Shelf 37
Defining the Problem 39

4. The Application of Big Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
Stories over Spreadsheets 41

A Thought on Dashboards 43
Full Interview 43

Mining the Astronomical Literature 43
Interview with Robert Simpson: Behind the Project and

What Lies Ahead 48
Science between the Cracks 51

The Dark Side of Data 51
The Digital Publishing Landscape 52
Privacy by Design 53

5. What to Watch for in Big Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Big Data Is Our Generation’s Civil Rights Issue, and We

Don’t Know It 55
Three Kinds of Big Data 60

Enterprise BI 2.0 60
Civil Engineering 62
Customer Relationship Optimization 63
Headlong into the Trough 64

Automated Science, Deep Data, and the Paradox of
Information 64
(Semi)Automated Science 65
Deep Data 67
The Paradox of Information 69

The Chicken and Egg of Big Data Solutions 71
Walking the Tightrope of Visualization Criticism 73

The Visualization Ecosystem 74
The Irrationality of Needs: Fast Food to Fine Dining 76
Grown-up Criticism 78
Final Thoughts 80

6. Big Data and Health Care. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
Solving the Wanamaker Problem for Health Care 83

Making Health Care More Effective 85
More Data, More Sources 89

iv | Table of Contents

Paying for Results 90
Enabling Data 91
Building the Health Care System We Want 94
Recommended Reading 95

Dr. Farzad Mostashari on Building the Health Information
Infrastructure for the Modern ePatient 96

John Wilbanks Discusses the Risks and Rewards of a Health
Data Commons 100

Esther Dyson on Health Data, “Preemptive Healthcare,” and
the Next Big Thing 106

A Marriage of Data and Caregivers Gives Dr. Atul Gawande
Hope for Health Care 112

Five Elements of Reform that Health Providers Would
Rather Not Hear About 119

Table of Contents | v

CHAPTER 1

Introduction

In the first edition of Big Data Now, the O’Reilly team tracked the birth
and early development of data tools and data science. Now, with this
second edition, we’re seeing what happens when big data grows up:
how it’s being applied, where it’s playing a role, and the conse‐
quences — good and bad alike — of data’s ascendance.

We’ve organized the 2012 edition of Big Data Now into five areas:

Getting Up to Speed With Big Data — Essential information on the
structures and definitions of big data.

Big Data Tools, Techniques, and Strategies — Expert guidance for
turning big data theories into big data products.

The Application of Big Data — Examples of big data in action, in‐
cluding a look at the downside of data.

What to Watch for in Big Data — Thoughts on how big data will
evolve and the role it will play across industries and domains.

Big Data and Health Care — A special section exploring the possi‐
bilities that arise when data and health care come together.

In addition to Big Data Now, you can stay on top of the latest data
developments with our ongoing analysis on O’Reilly Radar and
through our Strata coverage and events series.

1

CHAPTER 2

Getting Up to Speed with Big Data

What Is Big Data?
By Edd Dumbill

Big data is data that exceeds the processing capacity of conventional
database systems. The data is too big, moves too fast, or doesn’t fit the
strictures of your database architectures. To gain value from this data,
you must choose an alternative way to process it.

The hot IT buzzword of 2012, big data has become viable as cost-
effective approaches have emerged to tame the volume, velocity, and
variability of massive data. Within this data lie valuable patterns and
information, previously hidden because of the amount of work re‐
quired to extract them. To leading corporations, such as Walmart or
Google, this power has been in reach for some time, but at fantastic
cost. Today’s commodity hardware, cloud architectures and open
source software bring big data processing into the reach of the less
well-resourced. Big data processing is eminently feasible for even the
small garage startups, who can cheaply rent server time in the cloud.

The value of big data to an organization falls into two categories: an‐
alytical use and enabling new products. Big data analytics can reveal
insights hidden previously by data too costly to process, such as peer
influence among customers, revealed by analyzing shoppers’ transac‐
tions and social and geographical data. Being able to process every
item of data in reasonable time removes the troublesome need for
sampling and promotes an investigative approach to data, in contrast
to the somewhat static nature of running predetermined reports.

3

The past decade’s successful web startups are prime examples of big
data used as an enabler of new products and services. For example, by
combining a large number of signals from a user’s actions and those
of their friends, Facebook has been able to craft a highly personalized
user experience and create a new kind of advertising business. It’s no
coincidence that the lion’s share of ideas and tools underpinning big
data have emerged from Google, Yahoo, Amazon, and Facebook.

The emergence of big data into the enterprise brings with it a necessary
counterpart: agility. Successfully exploiting the value in big data re‐
quires experimentation and exploration. Whether creating new prod‐
ucts or looking for ways to gain competitive advantage, the job calls
for curiosity and an entrepreneurial outlook.

What Does Big Data Look Like?
As a catch-all term, “big data” can be pretty nebulous, in the same way
that the term “cloud” covers diverse technologies. Input data to big
data systems could be chatter from social networks, web server logs,
traffic flow sensors, satellite imagery, broadcast audio streams, bank‐
ing transactions, MP3s of rock music, the content of web pages, scans
of government documents, GPS trails, telemetry from automobiles,
financial market data, the list goes on. Are these all really the same
thing?

To clarify matters, the three Vs of volume, velocity, and variety are
commonly used to characterize different aspects of big data. They’re
a helpful lens through which to view and understand the nature of the
data and the software platforms available to exploit them. Most prob‐
ably you will contend with each of the Vs to one degree or another.

Volume

The benefit gained from the ability to process large amounts of infor‐
mation is the main attraction of big data analytics. Having more data
beats out having better models: simple bits of math can be unreason‐
ably effective given large amounts of data. If you could run that forecast
taking into account 300 factors rather than 6, could you predict de‐
mand better? This volume presents the most immediate challenge to
conventional IT structures. It calls for scalable storage, and a distribut‐
ed approach to querying. Many companies already have large amounts
of archived data, perhaps in the form of logs, but not the capacity to
process it.

4 | Chapter 2: Getting Up to Speed with Big Data

Assuming that the volumes of data are larger than those conventional
relational database infrastructures can cope with, processing options
break down broadly into a choice between massively parallel process‐
ing architectures — data warehouses or databases such as Green‐
plum — and Apache Hadoop-based solutions. This choice is often in‐
formed by the degree to which one of the other “Vs” — variety —
comes into play. Typically, data warehousing approaches involve pre‐
determined schemas, suiting a regular and slowly evolving dataset.
Apache Hadoop, on the other hand, places no conditions on the struc‐
ture of the data it can process.

At its core, Hadoop is a platform for distributing computing problems
across a number of servers. First developed and released as open source
by Yahoo, it implements the MapReduce approach pioneered by Goo‐
gle in compiling its search indexes. Hadoop’s MapReduce involves
distributing a dataset among multiple servers and operating on the
data: the “map” stage. The partial results are then recombined: the
“reduce” stage.

To store data, Hadoop utilizes its own distributed filesystem, HDFS,
which makes data available to multiple computing nodes. A typical
Hadoop usage pattern involves three stages:

• loading data into HDFS,
• MapReduce operations, and
• retrieving results from HDFS.

This process is by nature a batch operation, suited for analytical or
non-interactive computing tasks. Because of this, Hadoop is not itself
a database or data warehouse solution, but can act as an analytical
adjunct to one.

One of the most well-known Hadoop users is Facebook, whose model
follows this pattern. A MySQL database stores the core data. This is
then reflected into Hadoop, where computations occur, such as cre‐
ating recommendations for you based on your friends’ interests. Face‐
book then transfers the results back into MySQL, for use in pages
served to users.

Velocity

The importance of data’s velocity — the increasing rate at which data
flows into an organization — has followed a similar pattern to that of

What Is Big Data? | 5

volume. Problems previously restricted to segments of industry are
now presenting themselves in a much broader setting. Specialized
companies such as financial traders have long turned systems that cope
with fast moving data to their advantage. Now it’s our turn.

Why is that so? The Internet and mobile era means that the way we
deliver and consume products and services is increasingly instrumen‐
ted, generating a data flow back to the provider. Online retailers are
able to compile large histories of customers’ every click and interaction:
not just the final sales. Those who are able to quickly utilize that in‐
formation, by recommending additional purchases, for instance, gain
competitive advantage. The smartphone era increases again the rate
of data inflow, as consumers carry with them a streaming source of
geolocated imagery and audio data.

It’s not just the velocity of the incoming data that’s the issue: it’s possible
to stream fast-moving data into bulk storage for later batch processing,
for example. The importance lies in the speed of the feedback loop,
taking data from input through to decision. A commercial from
IBM makes the point that you wouldn’t cross the road if all you had
was a five-minute old snapshot of traffic location. There are times
when you simply won’t be able to wait for a report to run or a Hadoop
job to complete.

Industry terminology for such fast-moving data tends to be either
“streaming data” or “complex event processing.” This latter term was
more established in product categories before streaming processing
data gained more widespread relevance, and seems likely to diminish
in favor of streaming.

There are two main reasons to consider streaming processing. The first
is when the input data are too fast to store in their entirety: in order to
keep storage requirements practical, some level of analysis must occur
as the data streams in. At the extreme end of the scale, the Large Ha‐
dron Collider at CERN generates so much data that scientists must
discard the overwhelming majority of it — hoping hard they’ve not
thrown away anything useful. The second reason to consider stream‐
ing is where the application mandates immediate response to the data.
Thanks to the rise of mobile applications and online gaming this is an
increasingly common situation.

6 | Chapter 2: Getting Up to Speed with Big Data

Product categories for handling streaming data divide into established
proprietary products such as IBM’s InfoSphere Streams and the less-
polished and still emergent open source frameworks originating in the
web industry: Twitter’s Storm and Yahoo S4.

As mentioned above, it’s not just about input data. The velocity of a
system’s outputs can matter too. The tighter the feedback loop, the
greater the competitive advantage. The results might go directly into
a product, such as Facebook’s recommendations, or into dashboards
used to drive decision-making. It’s this need for speed, particularly on
the Web, that has driven the development of key-value stores and col‐
umnar databases, optimized for the fast retrieval of precomputed in‐
formation. These databases form part of an umbrella category known
as NoSQL, used when relational models aren’t the right fit.

Variety

Rarely does data present itself in a form perfectly ordered and ready
for processing. A common theme in big data systems is that the source
data is diverse, and doesn’t fall into neat relational structures. It could
be text from social networks, image data, a raw feed directly from a
sensor source. None of these things come ready for integration into an
application.

Even on the Web, where computer-to-computer communication
ought to bring some guarantees, the reality of data is messy. Different
browsers send different data, users withhold information, they may be
using differing software versions or vendors to communicate with you.
And you can bet that if part of the process involves a human, there will
be error and inconsistency.

A common use of big data processing is to take unstructured data and
extract ordered meaning, for consumption either by humans or as a
structured input to an application. One such example is entity reso‐
lution, the process of determining exactly what a name refers to. Is this
city London, England, or London, Texas? By the time your business
logic gets to it, you don’t want to be guessing.

The process of moving from source data to processed application data
involves the loss of information. When you tidy up, you end up throw‐
ing stuff away. This underlines a principle of big data: when you can,
keep everything. There may well be useful signals in the bits you throw
away. If you lose the source data, there’s no going back.

What Is Big Data? | 7

Despite the popularity and well understood nature of relational data‐
bases, it is not the case that they should always be the destination for
data, even when tidied up. Certain data types suit certain classes of
database better. For instance, documents encoded as XML are most
versatile when stored in a dedicated XML store such as MarkLogic.
Social network relations are graphs by nature, and graph databases
such as Neo4J make operations on them simpler and more efficient.

Even where there’s not a radical data type mismatch, a disadvantage
of the relational database is the static nature of its schemas. In an agile,
exploratory environment, the results of computations will evolve with
the detection and extraction of more signals. Semi-structured NoSQL
databases meet this need for flexibility: they provide enough structure
to organize data, but do not require the exact schema of the data before
storing it.

In Practice
We have explored the nature of big data and surveyed the landscape
of big data from a high level. As usual, when it comes to deployment
there are dimensions to consider over and above tool selection.

Cloud or in-house?

The majority of big data solutions are now provided in three forms:
software-only, as an appliance or cloud-based. Decisions between
which route to take will depend, among other things, on issues of data
locality, privacy and regulation, human resources and project require‐
ments. Many organizations opt for a hybrid solution: using on-
demand cloud resources to supplement in-house deployments.

Big data is big

It is a fundamental fact that data that is too big to process conven‐
tionally is also too big to transport anywhere. IT is undergoing an
inversion of priorities: it’s the program that needs to move, not the
data. If you want to analyze data from the U.S. Census, it’s a lot easier
to run your code on Amazon’s web services platform, which hosts such
data locally, and won’t cost you time or money to transfer it.

Even if the data isn’t too big to move, locality can still be an issue,
especially with rapidly updating data. Financial trading systems crowd
into data centers to get the fastest connection to source data, because
that millisecond difference in processing time equates to competitive
advantage.

8 | Chapter 2: Getting Up to Speed with Big Data

Big data is messy

It’s not all about infrastructure. Big data practitioners consistently re‐
port that 80% of the effort involved in dealing with data is cleaning it
up in the first place, as Pete Warden observes in his Big Data Glossa‐
ry: “I probably spend more time turning messy source data into some‐
thing usable than I do on the rest of the data analysis process com‐
bined.”

Because of the high cost of data acquisition and cleaning, it’s worth
considering what you actually need to source yourself. Data market‐
places are a means of obtaining common data, and you are often able
to contribute improvements back. Quality can of course be variable,
but will increasingly be a benchmark on which data marketplaces
compete.

Culture

The phenomenon of big data is closely tied to the emergence of data
science, a discipline that combines math, programming, and scientific
instinct. Benefiting from big data means investing in teams with this
skillset, and surrounding them with an organizational willingness to
understand and use data for advantage.

In his report, “Building Data Science Teams,” D.J. Patil characterizes
data scientists as having the following qualities:

• Technical expertise: the best data scientists typically have deep
expertise in some scientific discipline.

• Curiosity: a desire to go beneath the surface and discover and
distill a problem down into a very clear set of hypotheses that can
be tested.

• Storytelling: the ability to use data to tell a story and to be able to
communicate it effectively.

• Cleverness: the ability to look at a problem in different, creative
ways.

The far-reaching nature of big data analytics projects can have un‐
comfortable aspects: data must be broken out of silos in order to be
mined, and the organization must learn how to communicate and in‐
terpet the results of analysis.

What Is Big Data? | 9

Those skills of storytelling and cleverness are the gateway factors that
ultimately dictate whether the benefits of analytical labors are absor‐
bed by an organization. The art and practice of visualizing data is be‐
coming ever more important in bridging the human-computer gap to
mediate analytical insight in a meaningful way.

Know where you want to go

Finally, remember that big data is no panacea. You can find patterns
and clues in your data, but then what? Christer Johnson, IBM’s leader
for advanced analytics in North America, gives this advice to busi‐
nesses starting out with big data: first, decide what problem you want
to solve.

If you pick a real business problem, such as how you can change your
advertising strategy to increase spend per customer, it will guide your
implementation. While big data work benefits from an enterprising
spirit, it also benefits strongly from a concrete goal.

What Is Apache Hadoop?
By Edd Dumbill

Apache Hadoop has been the driving force behind the growth of the
big data industry. You’ll hear it mentioned often, along with associated
technologies such as Hive and Pig. But what does it do, and why do
you need all its strangely named friends, such as Oozie, Zookeeper,
and Flume?

Hadoop brings the ability to cheaply process large amounts of data,
regardless of its structure. By large, we mean from 10-100 gigabytes
and above. How is this different from what went before?

Existing enterprise data warehouses and relational databases excel at
processing structured data and can store massive amounts of data,
though at a cost: This requirement for structure restricts the kinds of
data that can be processed, and it imposes an inertia that makes data
warehouses unsuited for agile exploration of massive heterogenous
data. The amount of effort required to warehouse data often means
that valuable data sources in organizations are never mined. This is
where Hadoop can make a big difference.

This article examines the components of the Hadoop ecosystem and
explains the functions of each.

10 | Chapter 2: Getting Up to Speed with Big Data

The Core of Hadoop: MapReduce
Created at Google in response to the problem of creating web search
indexes, the MapReduce framework is the powerhouse behind most
of today’s big data processing. In addition to Hadoop, you’ll find Map‐
Reduce inside MPP and NoSQL databases, such as Vertica or Mon‐
goDB.

The important innovation of MapReduce is the ability to take a query
over a dataset, divide it, and run it in parallel over multiple nodes.
Distributing the computation solves the issue of data too large to fit
onto a single machine. Combine this technique with commodity Linux
servers and you have a cost-effective alternative to massive computing
arrays.

At its core, Hadoop is an open source MapReduce implementation.
Funded by Yahoo, it emerged in 2006 and, according to its creator
Doug Cutting, reached “web scale” capability in early 2008.

As the Hadoop project matured, it acquired further components to
enhance its usability and functionality. The name “Hadoop” has come
to represent this entire ecosystem. There are parallels with the emer‐
gence of Linux: The name refers strictly to the Linux kernel, but it has
gained acceptance as referring to a complete operating system.

Hadoop’s Lower Levels: HDFS and MapReduce
Above, we discussed the ability of MapReduce to distribute computa‐
tion over multiple servers. For that computation to take place, each
server must have access to the data. This is the role of HDFS, the Ha‐
doop Distributed File System.

HDFS and MapReduce are robust. Servers in a Hadoop cluster can fail
and not abort the computation process. HDFS ensures data is repli‐
cated with redundancy across the cluster. On completion of a calcu‐
lation, a node will write its results back into HDFS.

There are no restrictions on the data that HDFS stores. Data may be
unstructured and schemaless. By contrast, relational databases require
that data be structured and schemas be defined before storing the data.
With HDFS, making sense of the data is the responsibility of the de‐
veloper’s code.

Programming Hadoop at the MapReduce level is a case of working
with the Java APIs, and manually loading data files into HDFS.

What Is Apache Hadoop? | 11

Improving Programmability: Pig and Hive
Working directly with Java APIs can be tedious and error prone. It also
restricts usage of Hadoop to Java programmers. Hadoop offers two
solutions for making Hadoop programming easier.

• Pig is a programming language that simplifies the common tasks
of working with Hadoop: loading data, expressing transforma‐
tions on the data, and storing the final results. Pig’s built-in oper‐
ations can make sense of semi-structured data, such as log files,
and the language is extensible using Java to add support for custom
data types and transformations.

• Hive enables Hadoop to operate as a data warehouse. It superim‐
poses structure on data in HDFS and then permits queries over
the data using a familiar SQL-like syntax. As with Pig, Hive’s core
capabilities are extensible.

Choosing between Hive and Pig can be confusing. Hive is more suit‐
able for data warehousing tasks, with predominantly static structure
and the need for frequent analysis. Hive’s closeness to SQL makes it an
ideal point of integration between Hadoop and other business intelli‐
gence tools.

Pig gives the developer more agility for the exploration of large data‐
sets, allowing the development of succinct scripts for transforming
data flows for incorporation into larger applications. Pig is a thinner
layer over Hadoop than Hive, and its main advantage is to drastically
cut the amount of code needed compared to direct use of Hadoop’s
Java APIs. As such, Pig’s intended audience remains primarily the
software developer.

Improving Data Access: HBase, Sqoop, and Flume
At its heart, Hadoop is a batch-oriented system. Data are loaded into
HDFS, processed, and then retrieved. This is somewhat of a computing
throwback, and often, interactive and random access to data is re‐
quired.

Enter HBase, a column-oriented database that runs on top of HDFS.
Modeled after Google’s BigTable, the project’s goal is to host billions
of rows of data for rapid access. MapReduce can use HBase as both a
source and a destination for its computations, and Hive and Pig can
be used in combination with HBase.

12 | Chapter 2: Getting Up to Speed with Big Data

In order to grant random access to the data, HBase does impose a few
restrictions: Hive performance with HBase is 4-5 times slower than
with plain HDFS, and the maximum amount of data you can store in
HBase is approximately a petabyte, versus HDFS’ limit of over 30PB.

HBase is ill-suited to ad-hoc analytics and more appropriate for inte‐
grating big data as part of a larger application. Use cases include log‐
ging, counting, and storing time-series data.

The Hadoop Bestiary

Ambari Deployment, configuration and monitoring

Flume Collection and import of log and event data

HBase Column-oriented database scaling to billions of rows

HCatalog Schema and data type sharing over Pig, Hive and MapReduce

HDFS Distributed redundant file system for Hadoop

Hive Data warehouse with SQL-like access

Mahout Library of machine learning and data mining algorithms

MapReduce Parallel computation on server clusters

Pig High-level programming language for Hadoop computations

Oozie Orchestration and workflow management

Sqoop Imports data from relational databases

Whirr Cloud-agnostic deployment of clusters

Zookeeper Configuration management and coordination

Getting data in and out

Improved interoperability with the rest of the data world is provided
by Sqoop and Flume. Sqoop is a tool designed to import data from
relational databases into Hadoop, either directly into HDFS or into
Hive. Flume is designed to import streaming flows of log data directly
into HDFS.

Hive’s SQL friendliness means that it can be used as a point of inte‐
gration with the vast universe of database tools capable of making
connections via JBDC or ODBC database drivers.

What Is Apache Hadoop? | 13

Coordination and Workflow: Zookeeper and Oozie
With a growing family of services running as part of a Hadoop cluster,
there’s a need for coordination and naming services. As computing
nodes can come and go, members of the cluster need to synchronize
with each other, know where to access services, and know how they
should be configured. This is the purpose of Zookeeper.

Production systems utilizing Hadoop can often contain complex pipe‐
lines of transformations, each with dependencies on each other. For
example, the arrival of a new batch of data will trigger an import, which
must then trigger recalculations in dependent datasets. The Oozie
component provides features to manage the workflow and dependen‐
cies, removing the need for developers to code custom solutions.

Management and Deployment: Ambari and Whirr
One of the commonly added features incorporated into Hadoop by
distributors such as IBM and Microsoft is monitoring and adminis‐
tration. Though in an early stage, Ambari aims to add these features
to the core Hadoop project. Ambari is intended to help system ad‐
ministrators deploy and configure Hadoop, upgrade clusters, and
monitor services. Through an API, it may be integrated with other
system management tools.

Though not strictly part of Hadoop, Whirr is a highly complementary
component. It offers a way of running services, including Hadoop, on
cloud platforms. Whirr is cloud neutral and currently supports the
Amazon EC2 and Rackspace services.

Machine Learning: Mahout
Every organization’s data are diverse and particular to their needs.
However, there is much less diversity in the kinds of analyses per‐
formed on that data. The Mahout project is a library of Hadoop im‐
plementations of common analytical computations. Use cases include
user collaborative filtering, user recommendations, clustering, and
classification.

14 | Chapter 2: Getting Up to Speed with Big Data

Using Hadoop
Normally, you will use Hadoop in the form of a distribution. Much as
with Linux before it, vendors integrate and test the components of the
Apache Hadoop ecosystem and add in tools and administrative fea‐
tures of their own.

Though not per se a distribution, a managed cloud installation of Ha‐
doop’s MapReduce is also available through Amazon’s Elastic MapRe‐
duce service.

Why Big Data Is Big: The Digital Nervous
System
By Edd Dumbill

Where does all the data in “big data” come from? And why isn’t big
data just a concern for companies such as Facebook and Google? The
answer is that the web companies are the forerunners. Driven by social,
mobile, and cloud technology, there is an important transition taking
place, leading us all to the data-enabled world that those companies
inhabit today.

From Exoskeleton to Nervous System
Until a few years ago, the main function of computer systems in society,
and business in particular, was as a digital support system. Applica‐
tions digitized existing real-world processes, such as word-processing,
payroll, and inventory. These systems had interfaces back out to the
real world through stores, people, telephone, shipping, and so on. The
now-quaint phrase “paperless office” alludes to this transfer of pre-
existing paper processes into the computer. These computer systems
formed a digital exoskeleton, supporting a business in the real world.

The arrival of the Internet and the Web has added a new dimension,
bringing in an era of entirely digital business. Customer interaction,
payments, and often product delivery can exist entirely within com‐
puter systems. Data doesn’t just stay inside the exoskeleton any more,
but is a key element in the operation. We’re in an era where business
and society are acquiring a digital nervous system.

Why Big Data Is Big: The Digital Nervous System | 15

As my sketch below shows, an organization with a digital nervous sys‐
tem is characterized by a large number of inflows and outflows of data,
a high level of networking, both internally and externally, increased
data flow, and consequent complexity.

This transition is why big data is important. Techniques developed to
deal with interlinked, heterogenous data acquired by massive web
companies will be our main tools as the rest of us transition to digital-
native operation. We see early examples of this, from catching fraud
in financial transactions to debugging and improving the hiring pro‐
cess in HR: and almost everybody already pays attention to the massive
flow of social network information concerning them.

Charting the Transition
As technology has progressed within business, each step taken has
resulted in a leap in data volume. To people looking at big data now, a
reasonable question is to ask why, when their business isn’t Google or
Facebook, does big data apply to them?

The answer lies in the ability of web businesses to conduct 100% of
their activities online. Their digital nervous system easily stretches
from the beginning to the end of their operations. If you have factories,
shops, and other parts of the real world within your business, you’ve
further to go in incorporating them into the digital nervous system.

But “further to go” doesn’t mean it won’t happen. The drive of the Web,
social media, mobile, and the cloud is bringing more of each business

16 | Chapter 2: Getting Up to Speed with Big Data

into a data-driven world. In the UK, the Government Digital Service
is unifying the delivery of services to citizens. The results are a radical
improvement of citizen experience, and for the first time many de‐
partments are able to get a real picture of how they’re doing. For any
retailer, companies such as Square, American Express, and Four‐
square are bringing payments into a social, responsive data ecosystem,
liberating that information from the silos of corporate accounting.

What does it mean to have a digital nervous system? The key trait is
to make an organization’s feedback loop entirely digital. That is, a di‐
rect connection from sensing and monitoring inputs through to prod‐
uct outputs. That’s straightforward on the Web. It’s getting increasingly
easier in retail. Perhaps the biggest shifts in our world will come as
sensors and robotics bring the advantages web companies have now
to domains such as industry, transport, and the military.

The reach of the digital nervous system has grown steadily over the
past 30 years, and each step brings gains in agility and flexibility, along
with an order of magnitude more data. First, from specific application
programs to general business use with the PC. Then, direct interaction
over the Web. Mobile adds awareness of time and place, along with
instant notification. The next step, to cloud, breaks down data silos
and adds storage and compute elasticity through cloud computing.
Now, we’re integrating smart agents, able to act on our behalf, and
connections to the real world through sensors and automation.

Coming, Ready or Not
If you’re not contemplating the advantages of taking more of your op‐
eration digital, you can bet your competitors are. As Marc Andreessen
wrote last year, “software is eating the world.” Everything is becoming
programmable.

It’s this growth of the digital nervous system that makes the techniques
and tools of big data relevant to us today. The challenges of massive
data flows, and the erosion of hierarchy and boundaries, will lead us
to the statistical approaches, systems thinking, and machine learning
we need to cope with the future we’re inventing.

Why Big Data Is Big: The Digital Nervous System | 17

CHAPTER 3

Big Data Tools, Techniques,
and Strategies

Designing Great Data Products
By Jeremy Howard, Margit Zwemer, and Mike Loukides

In the past few years, we’ve seen many data products based on predic‐
tive modeling. These products range from weather forecasting to rec‐
ommendation engines to services that predict airline flight times more
accurately than the airlines themselves. But these products are still just
making predictions, rather than asking what action they want some‐
one to take as a result of a prediction. Prediction technology can be
interesting and mathematically elegant, but we need to take the next
step. The technology exists to build data products that can revolu‐
tionize entire industries. So, why aren’t we building them?

To jump-start this process, we suggest a four-step approach that has
already transformed the insurance industry. We call it the Drivetrain
Approach, inspired by the emerging field of self-driving vehicles. En‐
gineers start by defining a clear objective: They want a car to drive safely
from point A to point B without human intervention. Great predictive
modeling is an important part of the solution, but it no longer stands
on its own; as products become more sophisticated, it disappears into
the plumbing. Someone using Google’s self-driving car is completely
unaware of the hundreds (if not thousands) of models and the peta‐
bytes of data that make it work. But as data scientists build increasingly

19

sophisticated products, they need a systematic design approach. We
don’t claim that the Drivetrain Approach is the best or only method;
our goal is to start a dialog within the data science and business com‐
munities to advance our collective vision.

Objective-based Data Products
We are entering the era of data as drivetrain, where we use data not
just to generate more data (in the form of predictions), but use data to
produce actionable outcomes. That is the goal of the Drivetrain Ap‐
proach. The best way to illustrate this process is with a familiar data
product: search engines. Back in 1997, AltaVista was king of the algo‐
rithmic search world. While their models were good at finding relevant
websites, the answer the user was most interested in was often buried
on page 100 of the search results. Then, Google came along and trans‐
formed online search by beginning with a simple question: What is
the user’s main objective in typing in a search query?

The four steps in the Drivetrain Approach.

Google realized that the objective was to show the most relevant search
result; for other companies, it might be increasing profit, improving
the customer experience, finding the best path for a robot, or balancing
the load in a data center. Once we have specified the goal, the second
step is to specify what inputs of the system we can control, the levers
we can pull to influence the final outcome. In Google’s case, they could
control the ranking of the search results. The third step was to consider
what new data they would need to produce such a ranking; they real‐
ized that the implicit information regarding which pages linked to
which other pages could be used for this purpose. Only after these first
three steps do we begin thinking about building the predictive mod‐
els. Our objective and available levers, what data we already have and
what additional data we will need to collect, determine the models we
can build. The models will take both the levers and any uncontrollable
variables as their inputs; the outputs from the models can be combined
to predict the final state for our objective.

20 | Chapter 3: Big Data Tools, Techniques, and Strategies

Step 4 of the Drivetrain Approach for Google is now part of tech his‐
tory: Larry Page and Sergey Brin invented the graph traversal algo‐
rithm PageRank and built an engine on top of it that revolutionized
search. But you don’t have to invent the next PageRank to build a great
data product. We will show a systematic approach to step 4 that doesn’t
require a PhD in computer science.

The Model Assembly Line: A Case Study of Optimal
Decisions Group
Optimizing for an actionable outcome over the right predictive models
can be a company’s most important strategic decision. For an insur‐
ance company, policy price is the product, so an optimal pricing model
is to them what the assembly line is to automobile manufacturing.
Insurers have centuries of experience in prediction, but as recently as
10 years ago, the insurance companies often failed to make optimal
business decisions about what price to charge each new customer.
Their actuaries could build models to predict a customer’s likelihood
of being in an accident and the expected value of claims. But those
models did not solve the pricing problem, so the insurance companies
would set a price based on a combination of guesswork and market
studies.

This situation changed in 1999 with a company called Optimal Deci‐
sions Group (ODG). ODG approached this problem with an early use
of the Drivetrain Approach and a practical take on step 4 that can be
applied to a wide range of problems. They began by defining the ob‐
jective that the insurance company was trying to achieve: setting a price
that maximizes the net-present value of the profit from a new customer
over a multi-year time horizon, subject to certain constraints such as
maintaining market share. From there, they developed an optimized
pricing process that added hundreds of millions of dollars to the in‐
surers’ bottom lines. [Note: Co-author Jeremy Howard founded ODG.]

ODG identified which levers the insurance company could control:
what price to charge each customer, what types of accidents to cover,
how much to spend on marketing and customer service, and how to
react to their competitors’ pricing decisions. They also considered in‐
puts outside of their control, like competitors’ strategies, macroeco‐
nomic conditions, natural disasters, and customer “stickiness.” They
considered what additional data they would need to predict a cus‐
tomer’s reaction to changes in price. It was necessary to build this da‐

Designing Great Data Products | 21

taset by randomly changing the prices of hundreds of thousands of
policies over many months. While the insurers were reluctant to con‐
duct these experiments on real customers, as they’d certainly lose some
customers as a result, they were swayed by the huge gains that opti‐
mized policy pricing might deliver. Finally, ODG started to design the
models that could be used to optimize the insurer’s profit.

Drivetrain Step 4: The Model Assembly Line. Picture a Model Assembly
Line for data products that transforms the raw data into an actionable
outcome. The Modeler takes the raw data and converts it into slightly
more refined predicted data.

The first component of ODG’s Modeler was a model of price elasticity
(the probability that a customer will accept a given price) for new pol‐
icies and for renewals. The price elasticity model is a curve of price
versus the probability of the customer accepting the policy conditional
on that price. This curve moves from almost certain acceptance at very
low prices to almost never at high prices.

The second component of ODG’s Modeler related price to the insur‐
ance company’s profit, conditional on the customer accepting this
price. The profit for a very low price will be in the red by the value of
expected claims in the first year, plus any overhead for acquiring and
servicing the new customer. Multiplying these two curves creates a
final curve that shows price versus expected profit (see Expected Profit
figure, below). The final curve has a clearly identifiable local maximum
that represents the best price to charge a customer for the first year.

22 | Chapter 3: Big Data Tools, Techniques, and Strategies

Expected profit.

ODG also built models for customer retention. These models predic‐
ted whether customers would renew their policies in one year, allowing
for changes in price and willingness to jump to a competitor. These
additional models allow the annual models to be combined to predict
profit from a new customer over the next five years.

This new suite of models is not a final answer because it only identifies
the outcome for a given set of inputs. The next machine on the as‐
sembly line is a Simulator, which lets ODG ask the “what if ” questions
to see how the levers affect the distribution of the final outcome. The
expected profit curve is just a slice of the surface of possible outcomes.
To build that entire surface, the Simulator runs the models over a wide
range of inputs. The operator can adjust the input levers to answer
specific questions like, “What will happen if our company offers the
customer a low teaser price in year one but then raises the premiums
in year two?” They can also explore how the distribution of profit is
shaped by the inputs outside of the insurer’s control: “What if the
economy crashes and the customer loses his job? What if a 100-year
flood hits his home? If a new competitor enters the market and our

Designing Great Data Products | 23

company does not react, what will be the impact on our bottom line?”
Because the simulation is at a per-policy level, the insurer can view the
impact of a given set of price changes on revenue, market share, and
other metrics over time.

The Simulator’s result is fed to an Optimizer, which takes the surface
of possible outcomes and identifies the highest point. The Optimizer
not only finds the best outcomes, it can also identify catastrophic out‐
comes and show how to avoid them. There are many different opti‐
mization techniques to choose from (see “Optimization in the Real
World” (page 24)), but it is a well-understood field with robust and
accessible solutions. ODG’s competitors use different techniques to
find an optimal price, but they are shipping the same over-all data
product. What matters is that using a Drivetrain Approach combined
with a Model Assembly Line bridges the gap between predictive mod‐
els and actionable outcomes. Irfan Ahmed of CloudPhysics provides
a good taxonomy of predictive modeling that describes this entire as‐
sembly line process:

When dealing with hundreds or thousands of individual components
models to understand the behavior of the full-system, a search has to
be done. I think of this as a complicated machine (full-system) where
the curtain is withdrawn and you get to model each significant part
of the machine under controlled experiments and then simulate the
interactions. Note here the different levels: models of individual com‐
ponents, tied together in a simulation given a set of inputs, iterated
through over different input sets in a search optimizer.

Optimization in the Real World
Optimization is a classic problem that has been studied by Newton and
Gauss all the way up to mathematicians and engineers in the present
day. Many optimization procedures are iterative; they can be thought
of as taking a small step, checking our elevation and then taking another
small uphill step until we reach a point from which there is no direction
in which we can climb any higher. The danger in this hill-climbing
approach is that if the steps are too small, we may get stuck at one of
the many local maxima in the foothills, which will not tell us the best
set of controllable inputs. There are many techniques to avoid this
problem, some based on statistics and spreading our bets widely, and
others based on systems seen in nature, like biological evolution or the
cooling of atoms in glass.

24 | Chapter 3: Big Data Tools, Techniques, and Strategies

Optimization is a process we are all familiar with in our daily lives, even
if we have never used algorithms like gradient descent or simulated
annealing. A great image for optimization in the real world comes up
in a recent TechZing podcast with the co-founders of data-mining
competition platform Kaggle. One of the authors of this paper was
explaining an iterative optimization technique, and the host says, “So,
in a sense Jeremy, your approach was like that of doing a startup, which
is just get something out there and iterate and iterate and iterate.” The
takeaway, whether you are a tiny startup or a giant insurance company,
is that we unconsciously use optimization whenever we decide how to
get to where we want to go.

Drivetrain Approach to Recommender Systems
Let’s look at how we could apply this process to another industry:
marketing. We begin by applying the Drivetrain Approach to a familiar
example, recommendation engines, and then building this up into an
entire optimized marketing strategy.

Recommendation engines are a familiar example of a data product
based on well-built predictive models that do not achieve an optimal
objective. The current algorithms predict what products a customer
will like, based on purchase history and the histories of similar cus‐
tomers. A company like Amazon represents every purchase that has
ever been made as a giant sparse matrix, with customers as the rows
and products as the columns. Once they have the data in this format,
data scientists apply some form of collaborative filtering to “fill in the
matrix.” For example, if customer A buys products 1 and 10, and cus‐
tomer B buys products 1, 2, 4, and 10, the engine will recommend that
A buy 2 and 4. These models are good at predicting whether a customer
will like a given product, but they often suggest products that the cus‐

Designing Great Data Products | 25

tomer already knows about or has already decided not to buy. Amazon’s
recommendation engine is probably the best one out there, but it’s easy
to get it to show its warts. Here is a screenshot of the “Customers Who
Bought This Item Also Bought” feed on Amazon from a search for the
latest book in Terry Pratchett’s “Discworld series:”

All of the recommendations are for other books in the same series, but
it’s a good assumption that a customer who searched for “Terry Pratch‐
ett” is already aware of these books. There may be some unexpected
recommendations on pages 2 through 14 of the feed, but how many
customers are going to bother clicking through?

Instead, let’s design an improved recommendation engine using the
Drivetrain Approach, starting by reconsidering our objective. The ob‐
jective of a recommendation engine is to drive additional sales by sur‐
prising and delighting the customer with books he or she would not
have purchased without the recommendation. What we would really
like to do is emulate the experience of Mark Johnson, CEO of Zite,
who gave a perfect example of what a customer’s recommendation
experience should be like in a recent TOC talk. He went into Strand
bookstore in New York City and asked for a book similar to Toni Mor‐
rison’s Beloved. The girl behind the counter recommended William
Faulkner’s Absolom Absolom. On Amazon, the top results for a similar
query leads to another book by Toni Morrison and several books by
well-known female authors of color. The Strand bookseller made a
brilliant but far-fetched recommendation probably based more on the
character of Morrison’s writing than superficial similarities between
Morrison and other authors. She cut through the chaff of the obvious
to make a recommendation that will send the customer home with a
new book, and returning to Strand again and again in the future.

This is not to say that Amazon’s recommendation engine could not
have made the same connection; the problem is that this helpful rec‐
ommendation will be buried far down in the recommendation feed,
beneath books that have more obvious similarities to Beloved. The

26 | Chapter 3: Big Data Tools, Techniques, and Strategies

objective is to escape a recommendation filter bubble, a term which
was originally coined by Eli Pariser to describe the tendency of per‐
sonalized news feeds to only display articles that are blandly popular
or further confirm the readers’ existing biases.

As with the AltaVista-Google example, the lever a bookseller can con‐
trol is the ranking of the recommendations. New data must also be
collected to generate recommendations that will cause new sales. This
will require conducting many randomized experiments in order to
collect data about a wide range of recommendations for a wide range
of customers.

The final step in the drivetrain process is to build the Model Assembly
Line. One way to escape the recommendation bubble would be to build
a Modeler containing two models for purchase probabilities, condi‐
tional on seeing or not seeing a recommendation. The difference be‐
tween these two probabilities is a utility function for a given recom‐
mendation to a customer (see Recommendation Engine figure, be‐
low). It will be low in cases where the algorithm recommends a familiar
book that the customer has already rejected (both components are
small) or a book that he or she would have bought even without the
recommendation (both components are large and cancel each other
out). We can build a Simulator to test the utility of each of the many
possible books we have in stock, or perhaps just over all the outputs of
a collaborative filtering model of similar customer purchases, and then
build a simple Optimizer that ranks and displays the recommended
books based on their simulated utility. In general, when choosing an
objective function to optimize, we need less emphasis on the “function”
and more on the “objective.” What is the objective of the person using
our data product? What choice are we actually helping him or her
make?

Designing Great Data Products | 27

Recommendation Engine.

Optimizing Lifetime Customer Value
This same systematic approach can be used to optimize the entire
marketing strategy. This encompasses all the interactions that a retailer
has with its customers outside of the actual buy-sell transaction,
whether making a product recommendation, encouraging the cus‐
tomer to check out a new feature of the online store, or sending sales
promotions. Making the wrong choices comes at a cost to the retailer
in the form of reduced margins (discounts that do not drive extra
sales), opportunity costs for the scarce real-estate on their homepage
(taking up space in the recommendation feed with products the cus‐
tomer doesn’t like or would have bought without a recommendation)
or the customer tuning out (sending so many unhelpful email pro‐
motions that the customer filters all future communications as spam).
We will show how to go about building an optimized marketing strat‐
egy that mitigates these effects.

28 | Chapter 3: Big Data Tools, Techniques, and Strategies

As in each of the previous examples, we begin by asking: “What ob‐
jective is the marketing strategy trying to achieve?” Simple: we want
to optimize the lifetime value from each customer. Second question:
“What levers do we have at our disposal to achieve this objective?”
Quite a few. For example:

1. We can make product recommendations that surprise and delight
(using the optimized recommendation outlined in the previous
section).

2. We could offer tailored discounts or special offers on products the
customer was not quite ready to buy or would have bought else‐
where.

3. We can even make customer-care calls just to see how the user is
enjoying our site and make them feel that their feedback is valued.

What new data do we need to collect? This can vary case by case, but
a few online retailers are taking creative approaches to this step. Online
fashion retailer Zafu shows how to encourage the customer to partic‐
ipate in this collection process. Plenty of websites sell designer denim,
but for many women, high-end jeans are the one item of clothing they
never buy online because it’s hard to find the right pair without trying
them on. Zafu’s approach is not to send their customers directly to the
clothes, but to begin by asking a series of simple questions about the
customers’ body type, how well their other jeans fit, and their fashion
preferences. Only then does the customer get to browse a recom‐
mended selection of Zafu’s inventory. The data collection and recom‐
mendation steps are not an add-on; they are Zafu’s entire business
model — women’s jeans are now a data product. Zafu can tailor their
recommendations to fit as well as their jeans because their system is
asking the right questions.

Designing Great Data Products | 29

Starting with the objective forces data scientists to consider what ad‐
ditional models they need to build for the Modeler. We can keep the
“like” model that we have already built as well as the causality model
for purchases with and without recommendations, and then take a
staged approach to adding additional models that we think will im‐
prove the marketing effectiveness. We could add a price elasticity
model to test how offering a discount might change the probability
that the customer will buy the item. We could construct a patience
model for the customers’ tolerance for poorly targeted communica‐
tions: When do they tune them out and filter our messages straight to
spam? (“If Hulu shows me that same dog food ad one more time, I’m
gonna stop watching!”) A purchase sequence causality model can be
used to identify key “entry products.” For example, a pair of jeans that
is often paired with a particular top, or the first part of a series of novels
that often leads to a sale of the whole set.

Once we have these models, we construct a Simulator and an Opti‐
mizer and run them over the combined models to find out what rec‐
ommendations will achieve our objectives: driving sales and improv‐
ing the customer experience.

30 | Chapter 3: Big Data Tools, Techniques, and Strategies

A look inside the Modeler.

Best Practices from Physical Data Products
It is easy to stumble into the trap of thinking that since data exists
somewhere abstract, on a spreadsheet or in the cloud, that data prod‐
ucts are just abstract algorithms. So, we would like to conclude by
showing you how objective-based data products are already a part of
the tangible world. What is most important about these examples is
that the engineers who designed these data products didn’t start by
building a neato robot and then looking for something to do with it.
They started with an objective like, “I want my car to drive me places,”
and then designed a covert data product to accomplish that task. En‐
gineers are often quietly on the leading edge of algorithmic applica‐
tions because they have long been thinking about their own modeling
challenges in an objective-based way. Industrial engineers were among
the first to begin using neural networks, applying them to problems
like the optimal design of assembly lines and quality control. Brian
Ripley’s seminal book on pattern recognition gives credit for many
ideas and techniques to largely forgotten engineering papers from the
1970s.

When designing a product or manufacturing process, a drivetrain-like
process followed by model integration, simulation and optimization
is a familiar part of the toolkit of systems engineers. In engineering, it

Designing Great Data Products | 31

is often necessary to link many component models together so that
they can be simulated and optimized in tandem. These firms have
plenty of experience building models of each of the components and
systems in their final product, whether they’re building a server farm
or a fighter jet. There may be one detailed model for mechanical sys‐
tems, a separate model for thermal systems, and yet another for elec‐
trical systems, etc. All of these systems have critical interactions. For
example, resistance in the electrical system produces heat, which needs
to be included as an input for the thermal diffusion and cooling model.
That excess heat could cause mechanical components to warp, pro‐
ducing stresses that should be inputs to the mechanical models.

The screenshot below is taken from a model integration tool designed
by Phoenix Integration. Although it’s from a completely different en‐
gineering discipline, this diagram is very similar to the Drivetrain Ap‐
proach we’ve recommended for data products. The objective is clearly
defined: build an airplane wing. The wing box includes the design
levers like span, taper ratio, and sweep. The data is in the wing mate‐
rials’ physical properties; costs are listed in another tab of the appli‐
cation. There is a Modeler for aerodynamics and mechanical structure
that can then be fed to a Simulator to produce the Key Wing Outputs
of cost, weight, lift coefficient, and induced drag. These outcomes can
be fed to an Optimizer to build a functioning and cost-effective air‐
plane wing.

32 | Chapter 3: Big Data Tools, Techniques, and Strategies

Screenshot from a model integration tool designed by Phoenix Integra‐
tion.

As predictive modeling and optimization become more vital to a wide
variety of activities, look out for the engineers to disrupt industries
that wouldn’t immediately appear to be in the data business. The in‐
spiration for the phrase “Drivetrain Approach,” for example, is already
on the streets of Mountain View. Instead of being data driven, we can
now let the data drive us.

Suppose we wanted to get from San Francisco to the Strata 2012 Con‐
ference in Santa Clara. We could just build a simple model of distance /
speed-limit to predict arrival time with little more than a ruler and a
road map. If we want a more sophisticated system, we can build an‐
other model for traffic congestion and yet another model to forecast
weather conditions and their effect on the safest maximum speed.
There are plenty of cool challenges in building these models, but by
themselves, they do not take us to our destination. These days, it is
trivial to use some type of heuristic search algorithm to predict the
drive times along various routes (a Simulator) and then pick the short‐
est one (an Optimizer) subject to constraints like avoiding bridge tolls
or maximizing gas mileage. But why not think bigger? Instead of the
femme-bot voice of the GPS unit telling us which route to take and
where to turn, what would it take to build a car that would make those
decisions by itself? Why not bundle simulation and optimization en‐
gines with a physical engine, all inside the black box of a car?

Let’s consider how this is an application of the Drivetrain Approach.
We have already defined our objective: building a car that drives itself.
The levers are the vehicle controls we are all familiar with: steering
wheel, accelerator, brakes, etc. Next, we consider what data the car
needs to collect; it needs sensors that gather data about the road as well
as cameras that can detect road signs, red or green lights, and unex‐
pected obstacles (including pedestrians). We need to define the mod‐
els we will need, such as physics models to predict the effects of steer‐
ing, braking and acceleration, and pattern recognition algorithms to
interpret data from the road signs.

As one engineer on the Google self-driving car project put it in a recent
Wired article, “We’re analyzing and predicting the world 20 times a
second.” What gets lost in the quote is what happens as a result of that
prediction. The vehicle needs to use a simulator to examine the results
of the possible actions it could take. If it turns left now, will it hit that

Designing Great Data Products | 33

pedestrian? If it makes a right turn at 55 mph in these weather condi‐
tions, will it skid off the road? Merely predicting what will happen isn’t
good enough. The self-driving car needs to take the next step: after
simulating all the possibilities, it must optimize the results of the sim‐
ulation to pick the best combination of acceleration and braking,
steering and signaling, to get us safely to Santa Clara. Prediction only
tells us that there is going to be an accident. An optimizer tells us how
to avoid accidents.

Improving the data collection and predictive models is very important,
but we want to emphasize the importance of beginning by defining a
clear objective with levers that produce actionable outcomes. Data
science is beginning to pervade even the most bricks-and-mortar el‐
ements of our lives. As scientists and engineers become more adept at
applying prediction and optimization to everyday problems, they are
expanding the art of the possible, optimizing everything from our
personal health to the houses and cities we live in. Models developed
to simulate fluid dynamics and turbulence have been applied to im‐
proving traffic and pedestrian flows by using the placement of exits
and crowd control barriers as levers. This has improved emergency
evacuation procedures for subway stations and reduced the danger of
crowd stampedes and trampling during sporting events. Nest is de‐
signing smart thermostats that learn the home-owner’s temperature
preferences and then optimizes their energy consumption. For motor
vehicle traffic, IBM performed a project with the city of Stockholm to
optimize traffic flows that reduced congestion by nearly a quarter, and
increased the air quality in the inner city by 25%. What is particularly
interesting is that there was no need to build an elaborate new data
collection system. Any city with metered stoplights already has all the
necessary information; they just haven’t found a way to suck the
meaning out of it.

In another area where objective-based data products have the power
to change lives, the CMU extension in Silicon Valley has an active
project for building data products to help first responders after natural
or man-made disasters. Jeannie Stamberger of Carnegie Mellon Uni‐
versity Silicon Valley explained to us many of the possible applications
of predictive algorithms to disaster response, from text-mining and
sentiment analysis of tweets to determine the extent of the damage, to
swarms of autonomous robots for reconnaissance and rescue, to lo‐
gistic optimization tools that help multiple jurisdictions coordinate
their responses. These disaster applications are a particularly good

34 | Chapter 3: Big Data Tools, Techniques, and Strategies

example of why data products need simple, well-designed interfaces
that produce concrete recommendations. In an emergency, a data
product that just produces more data is of little use. Data scientists
now have the predictive tools to build products that increase the com‐
mon good, but they need to be aware that building the models is not
enough if they do not also produce optimized, implementable out‐
comes.

The Future for Data Products
We introduced the Drivetrain Approach to provide a framework for
designing the next generation of great data products and described
how it relies at its heart on optimization. In the future, we hope to see
optimization taught in business schools as well as in statistics depart‐
ments. We hope to see data scientists ship products that are designed
to produce desirable business outcomes. This is still the dawn of data
science. We don’t know what design approaches will be developed in
the future, but right now, there is a need for the data science community
to coalesce around a shared vocabulary and product design process
that can be used to educate others on how to derive value from their
predictive models. If we do not do this, we will find that our models
only use data to create more data, rather than using data to create
actions, disrupt industries, and transform lives.

Do we want products that deliver data, or do we want products that
deliver results based on data? Jeremy Howard examined these questions
in his Strata California 12 session, “From Predictive Modelling to Opti‐
mization: The Next Frontier.” Full video from that session is available
here.

What It Takes to Build Great Machine Learning
Products
By Aria Haghighi

Machine learning (ML) is all the rage, riding tight on the coattails of
the “big data” wave. Like most technology hype, the enthusiasm far
exceeds the realization of actual products. Arguably, not since Google’s
tremendous innovations in the late ’90s/early 2000s has algorithmic
technology led to a product that has permeated the popular culture.
That’s not to say there haven’t been great ML wins since, but none have
as been as impactful or had computational algorithms at their core.

What It Takes to Build Great Machine Learning Products | 35

1. Although MCMC is a much older statistical technique, its broad use in large-scale
machine learning applications is relatively recent.

Netflix may use recommendation technology, but Netflix is still Netflix
without it. There would be no Google if Page, Brin, et al., hadn’t ex‐
ploited the graph structure of the Web and anchor text to improve
search.

So why is this? It’s not for lack of trying. How many startups have aimed
to bring natural language processing (NLP) technology to the masses,
only to fade into oblivion after people actually try their products? The
challenge in building great products with ML lies not in just under‐
standing basic ML theory, but in understanding the domain and prob‐
lem sufficiently to operationalize intuitions into model design. Inter‐
esting problems don’t have simple off-the-shelf ML solutions. Progress
in important ML application areas, like NLP, come from insights spe‐
cific to these problems, rather than generic ML machinery. Often,
specific insights into a problem and careful model design make the
difference between a system that doesn’t work at all and one that people
will actually use.

The goal of this essay is not to discourage people from building amaz‐
ing products with ML at their cores, but to be clear about where I think
the difficulty lies.

Progress in Machine Learning
Machine learning has come a long way over the last decade. Before I
started grad school, training a large-margin classifier (e.g., SVM) was
done via John Platt’s batch SMO algorithm. In that case, training time
scaled poorly with the amount of training data. Writing the algorithm
itself required understanding quadratic programming and was riddled
with heuristics for selecting active constraints and black-art parameter
tuning. Now, we know how to train a nearly performance-equivalent
large-margin classifier in linear time using a (relatively) simple online
algorithm (PDF). Similar strides have been made in (probabilistic)
graphical models: Markov-chain Monte Carlo (MCMC) and varia‐
tional methods have facilitated inference for arbitrarily complex
graphical models.1 Anecdotally, take at look at papers over the last

36 | Chapter 3: Big Data Tools, Techniques, and Strategies

eight years in the proceedings of the Association for Computational
Linguistics (ACL), the premiere natural language processing publica‐
tion. A top paper from 2011 has orders of magnitude more technical
ML sophistication than one from 2003.

On the education front, we’ve come a long way as well. As an undergrad
at Stanford in the early-to-mid 2000s, I took Andrew Ng’s ML
course and Daphne Koller’s probabilistic graphical model course. Both
of these classes were among the best I took at Stanford and were only
available to about 100 students a year. Koller’s course in particular was
not only the best course I took at Stanford, but the one that taught me
the most about teaching. Now, anyone can take these courses online.

As an applied ML person — specifically, natural language processing
— much of this progress has made aspects of research significantly
easier. However, the core decisions I make are not which abstract ML
algorithm, loss-function, or objective to use, but what features and
structure are relevant to solving my problem. This skill only comes
with practice. So, while it’s great that a much wider audience will have
an understanding of basic ML, it’s not the most difficult part of build‐
ing intelligent systems.

Interesting Problems Are Never Off the Shelf
The interesting problems that you’d actually want to solve are far
messier than the abstractions used to describe standard ML problems.
Take machine translation (MT), for example. Naively, MT looks like a
statistical classification problem: You get an input foreign sentence and
have to predict a target English sentence. Unfortunately, because the
space of possible English is combinatorially large, you can’t treat MT
as a black-box classification problem. Instead, like most interesting
ML applications, MT problems have a lot of structure and part of the
job of a good researcher is decomposing the problem into smaller
pieces that can be learned or encoded deterministically. My claim is
that progress in complex problems like MT comes mostly from how
we decompose and structure the solution space, rather than ML tech‐
niques used to learn within this space.

Machine translation has improved by leaps and bounds throughout
the last decade. I think this progress has largely, but not entirely, come
from keen insights into the specific problem, rather than generic ML
improvements. Modern statistical MT originates from an amazing
paper, “The mathematics of statistical machine translation” (PDF),

What It Takes to Build Great Machine Learning Products | 37

2. The model is generative, so what’s being described here is from the point-of-view of
inference; the model’s generative story works in reverse.

3. IBM model 3 introduced the concept of fertility to allow a given word to generate
multiple independent target translation words. While this could generate the required
translation, the probability of the model doing so is relatively low.

which introduced the noisy-channel architecture on which future MT
systems would be based. At a very simplistic level, this is how the model
works:2 For each foreign word, there are potential English translations
(including the null word for foreign words that have no English equiv‐
alent). Think of this as a probabilistic dictionary. These candidate
translation words are then re-ordered to create a plausible English
translation. There are many intricacies being glossed over: how to ef‐
ficiently consider candidate English sentences and their permutations,
what model is used to learn the systematic ways in which reordering
occurs between languages, and the details about how to score the
plausibility of the English candidate (the language model).

The core improvement in MT came from changing this model. So,
rather than learning translation probabilities of individual words, to
instead learn models of how to translate foreign phrases to English
phrases. For instance, the German word “abends” translates roughly
to the English prepositional phrase “in the evening.” Before phrase-
based translation (PDF), a word-based model would only get to trans‐
late to a single English word, making it unlikely to arrive at the correct
English translation.3 Phrase-based translation generally results in
more accurate translations with fluid, idiomatic English output. Of
course, adding phrased-based emissions introduces several additional
complexities, including how to how to estimate phrase-emissions giv‐
en that we never observe phrase segmentation; no one tells us that “in
the evening” is a phrase that should match up to some foreign phrase.
What’s surprising here is that there aren’t general ML improvements
that are making this difference, but problem-specific model design.
People can and have implemented more sophisticated ML techniques
for various pieces of an MT system. And these do yield improvements,
but typically far smaller than good problem-specific research insights.

Franz Och, one of the authors of the original Phrase-based papers,
went on to Google and became the principle person behind the search
company’s translation efforts. While the intellectual underpinnings of
Google’s system go back to Och’s days as a research scientist at the
Information Sciences Institute (and earlier as a graduate student),

38 | Chapter 3: Big Data Tools, Techniques, and Strategies

much of the gains beyond the insights underlying phrase-based trans‐
lation (and minimum-error rate training, another of Och’s innova‐
tions) came from a massive software engineering effort to scale these
ideas to the Web. That effort itself yielded impressive research into
large-scale language models and other areas of NLP. It’s important to
note that Och, in addition to being a world-class researcher, is also, by
all accounts, an incredibly impressive hacker and builder. It’s this rare
combination of skill that can bring ideas all the way from a research
project to where Google Translate is today.

Defining the Problem
But I think there’s an even bigger barrier beyond ingenious model
design and engineering skills. In the case of machine translation and
speech recognition, the problem being solved is straightforward to
understand and well-specified. Many of the NLP technologies that I
think will revolutionize consumer products over the next decade are
much more vague. How, exactly, can we take the excellent research in
structured topic models, discourse processing, or sentiment analysis
and make a mass-appeal consumer product?

Consider summarization. We all know that in some way, we’ll want
products that summarize and structure content. However, for com‐
putational and research reasons, you need to restrict the scope of this
problem to something for which you can build a model, an algorithm,
and ultimately evaluate. For instance, in the summarization literature,
the problem of multi-document summarization is typically formula‐
ted as selecting a subset of sentences from the document collection
and ordering them. Is this the right problem to be solving? Is the best
way to summarize a piece of text a handful of full-length sentences?
Even if a summarization is accurate, does the Franken-sentence struc‐
ture yield summaries that feel inorganic to users?

Or, consider sentiment analysis. Do people really just want a coarse-
grained thumbs-up or thumbs-down on a product or event? Or do
they want a richer picture of sentiments toward individual aspects of
an item (e.g., loved the food, hated the decor)? Do people care about
determining sentiment attitudes of individual reviewers/utterances, or
producing an accurate assessment of aggregate sentiment?

Typically, these decisions are made by a product person and are passed
off to researchers and engineers to implement. The problem with this
approach is that ML-core products are intimately constrained by what

What It Takes to Build Great Machine Learning Products | 39

is technically and algorithmically feasible. In my experience, having a
technical understanding of the range of related ML problems can in‐
spire product ideas that might not occur to someone without this un‐
derstanding. To draw a loose analogy, it’s like architecture. So much of
the construction of a bridge is constrained by material resources and
physics that it doesn’t make sense to have people without that technical
background design a bridge.

The goal of all this is to say that if you want to build a rich ML product,
you need to have a rich product/design/research/engineering team.
All the way from the nitty gritty of how ML theory works to building
systems to domain knowledge to higher-level product thinking to
technical interaction and graphic design; preferably people who are
world-class in one of these areas but also good in several. Small talented
teams with all of these skills are better equipped to navigate the joint
uncertainty with respect to product vision as well as model design.
Large companies that have research and product people in entirely
different buildings are ill-equipped to tackle these kinds of problems.
The ML products of the future will come from startups with small
founding teams that have this full context and can all fit in the prov‐
erbial garage.

40 | Chapter 3: Big Data Tools, Techniques, and Strategies

CHAPTER 4

The Application of Big Data

Stories over Spreadsheets
By Mac Slocum

I didn’t realize how much I dislike spreadsheets until I was presented
with a vision of the future where their dominance isn’t guaranteed.

That eye-opening was offered by Narrative Science CTO Kris Ham‐
mond (@whisperspace) during a recent interview. Hammond’s com‐
pany turns data into stories: They provide sentences and paragraphs
instead of rows and columns. To date, much of the attention Narrative
Science has received has focused on the media applications. That’s a
natural starting point. Heck, I asked him about those very same things
when I first met Hammond at Strata in New York last fall. But during
our most recent chat, Hammond explored the other applications of
narrative-driven data analysis.

“Companies, God bless them, had a great insight: They wanted to make
decisions based upon the data that’s out there and the evidence in front
of them,” Hammond said. “So they started gathering that data up. It
quickly exploded. And they ended up with huge data repositories they
had to manage. A lot of their effort ended up being focused on gath‐
ering that data, managing that data, doing analytics across that data,
and then the question was: What do we do with it?”

Hammond sees an opportunity to extract and communicate the in‐
sights locked within company data. “We’ll be the bridge between the
data you have, the insights that are in there, or insights we can gather,

41

and communicating that information to your clients, to your man‐
agement, and to your different product teams. We’ll turn it into some‐
thing that’s intelligible instead of a list of numbers, a spreadsheet, or a
graph or two. You get a real narrative; a real story in that data.”

My takeaway: The journalism applications of this are intriguing, but
these other use cases are empowering.

Why? Because most people don’t speak fluent “spreadsheet.” They see
all those neat rows and columns and charts, and they know something
important is tucked in there, but what that something is and how to
extract it aren’t immediately clear. Spreadsheets require effort. That’s
doubly true if you don’t know what you’re looking for. And if data
analysis is an adjacent part of a person’s job, more effort means those
spreadsheets will always be pushed to the side. “I’ll get to those next
week when I’ve got more time…”

We all know how that plays out.

But what if the spreadsheet wasn’t our default output anymore? What
if we could take things most of us are hard-wired to understand —
stories, sentences, clear guidance — and layer it over all that vital data?
Hammond touched on that:

For some people, a spreadsheet is a great device. For most people, not
so much so. The story. The paragraph. The report. The prediction.
The advisory. Those are much more powerful objects in our world,
and they’re what we’re used to.

He’s right. Spreadsheets push us (well, most of us) into a cognitive
corner. Open a spreadsheet and you’re forced to recalibrate your focus
to see the data. Then you have to work even harder to extract meaning.
This is the best we can do?

With that in mind, I asked Hammond if the spreadsheet’s days are
numbered.

“There will always be someone who uses a spreadsheet,” Hammond
said. “But, I think what we’re finding is that the story is really going to
be the endpoint. If you think about it, the spreadsheet is for somebody
who really embraces the data. And usually what that person does is
they reduce that data down to something that they’re going to use to
communicate with someone else.”

42 | Chapter 4: The Application of Big Data

A Thought on Dashboards
I used to view dashboards as the logical step beyond raw data and
spreadsheets. I’m not so sure about that anymore, at least in terms of
broad adoption. Dashboards are good tools, and I anticipate we’ll have
them from now until the end of time, but they’re still weighed down
by a complexity that makes them inaccessible.

It’s not that people can’t master the buttons and custom reports in
dashboards; they simply don’t have time. These people — and I include
myself among them — need something faster and knob-free. Simplic‐
ity is the thing that will ultimately democratize data reporting and data
insights. That’s why the expansion of data analysis requires a refine‐
ment beyond our current dashboards. There’s a next step that hasn’t
been addressed.

Does the answer lie in narrative? Will visualizations lead the way? Will
a hybrid format take root? I don’t know what the final outputs will look
like, but the importance of data reporting means someone will even‐
tually crack the problem.

Full Interview
You can see the entire discussion with Hammond in this interview.

Mining the Astronomical Literature
By Alasdair Allan

There is a huge debate right now about making academic literature
freely accessible and moving toward open access. But what would be
possible if people stopped talking about it and just dug in and got on
with it?

NASA’s Astrophysics Data System (ADS), hosted by the Smithsonian
Astrophysical Observatory (SAO), has quietly been working away
since the mid-’90s. Without much, if any, fanfare amongst the other
disciplines, it has moved astronomers into a world where access to the
literature is just a given. It’s something they don’t have to think about
all that much.

The ADS service provides access to abstracts for virtually all of the
astronomical literature. But it also provides access to the full text of

Mining the Astronomical Literature | 43

more than half a million papers, going right back to the start of peer-
reviewed journals in the 1800s. The service has links to online data
archives, along with reference and citation information for each of the
papers, and it’s all searchable and downloadable.

Number of papers published in the three main astronomy journals each
year. Credit: Robert Simpson

The existence of the ADS, along with the arXiv pre-print server, has
meant that most astronomers haven’t seen the inside of a brick-built
library since the late 1990s.

It also makes astronomy almost uniquely well placed for interesting
data mining experiments, experiments that hint at what the rest of
academia could do if they followed astronomy’s lead. The fact that the
discipline’s literature has been scanned, archived, indexed and cata‐
logued, and placed behind a RESTful API makes it a treasure trove,
both for hypothesis generation and sociological research.

For example, the .Astronomy series of conferences is a small workshop
that brings together the best and brightest of the technical community:
researchers, developers, educators, and communicators. Billed as
“20% time for astronomers,” it gives these people space to think about
how the new technologies affect both how research and communicat‐
ing research to their peers and to the public is done.

[Disclosure: I’m a member of the advisory board to the .Astronomy con‐
ference, and I previously served as a member of the programme organ‐
ising committee for the conference series.]

44 | Chapter 4: The Application of Big Data

It should perhaps come as little surprise that one of the more inter‐
esting projects to come out of a hack day held as part of this year’s .As‐
tronomy meeting in Heidelberg was work by Robert Simpson, Karen
Masters and Sarah Kendrew that focused on data mining the astro‐
nomical literature.

The team grabbed and processed the titles and abstracts of all the pa‐
pers from the Astrophysical Journal (ApJ), Astronomy & Astrophy‐
sics (A&A), and the Monthly Notices of the Royal Astronomical So‐
ciety (MNRAS) since each of those journals started publication — and
that’s 1827 in the case of MNRAS.

By the end of the day, they’d found some interesting results showing
how various terms have trended over time. The results were similar to
what’s found in Google Books’ Ngram Viewer.

The relative popularity of the names of telescopes in the literature. Hub‐
ble, Chandra, and Spitzer seem to have taken turns in hogging the lime‐
light, much as COBE, WMAP, and Planck have each contributed to our
knowledge of the cosmic microwave background in successive decades.
References to Planck are still on the rise. Credit: Robert Simpson.

After the meeting, however, Robert took his initial results and explored
the astronomical literature and his new corpus of data on the literature.
He has explored various visualisations of the data, including word
matrixes for related terms and for various astro-chemistry.

Mining the Astronomical Literature | 45

Correlation between terms related to Active Galactic Nuclei (AGN). The
opacity of each square represents the strength of the correlation between
the terms. Credit: Robert Simpson.

He has also taken a look at authorship in astronomy and is starting to
find some interesting trends.

46 | Chapter 4: The Application of Big Data

Fraction of astronomical papers published with one, two, three, four, or
more authors. Credit: Robert Simpson

You can see that single-author papers dominated for most of the 20th
century. Around 1960, we see the decline begin, as two- and three-
author papers begin to become a significant chunk of the whole. In
1978, author papers become more prevalent than single-author pa‐
pers.

Compare the number of “active” research astronomers to the number of
papers published each year (across all the major journals). Credit: Robert
Simpson.

Mining the Astronomical Literature | 47

Here we see that people begin to outpace papers in the 1960s. This may
reflect the fact that as we get more technical as a field, and more spe‐
cialised, it takes more people to write the same number of papers,
which is a sort of interesting result all by itself.

Interview with Robert Simpson: Behind the Project and
What Lies Ahead
I recently talked with Rob about the work he, Karen Masters, and Sarah
Kendrew did at the meeting, and the work he has been doing since
with the newly gathered data.

What made you think about data mining the ADS?

Robert Simpson: At the .Astronomy 4 Hack Day in July, Sarah Ken‐
drew had the idea to try to do an astronomy version of BrainSCANr,
a project that generates new hypotheses in the neuroscience literature.
I’ve had a go at mining ADS and arXiv before, so it seemed like a great
excuse to dive back in.

Do you think there might be actual science that could be done here?

Robert Simpson: Yes, in the form of finding questions that were un‐
expected. With such large volumes of peer-reviewed papers being pro‐
duced daily in astronomy, there is a lot being said. Most researchers
can only try to keep up with it all — my daily RSS feed from arXiv is
next to useless, it’s so bloated. In amongst all that text, there must be
connections and relationships that are being missed by the community
at large, hidden in the chatter. Maybe we can develop simple techni‐
ques to highlight potential missed links, i.e., generate new hypotheses
from the mass of words and data.

Are the results coming out of the work useful for auditing academics?

Robert Simpson: Well, perhaps, but that would be tricky territory in
my opinion. I’ve only just begun to explore the data around authorship
in astronomy. One thing that is clear is that we can see a big trend
toward collaborative work. In 2012, only 6% of papers were single-
author efforts, compared with 70+% in the 1950s.

48 | Chapter 4: The Application of Big Data

The above plot shows the average number of authors, per paper since
1827. Credit: Robert Simpson.

We can measure how large groups are becoming, and who is part of
which groups. In that sense, we can audit research groups, and maybe
individual people. The big issue is keeping track of people through
variations in their names and affiliations. Identifying authors is prob‐
ably a solved problem if we look at ORCID.

What about citations? Can you draw any comparisons with h-index
data?

Robert Simpson: I haven’t looked at h-index stuff specifically, at least
not yet, but citations are fun. I looked at the trends surrounding the
term dark matter and saw something interesting. Mentions of dark
matter rise steadily after it first appears in the late ’70s.

Mining the Astronomical Literature | 49

Compare the term “dark matter” with a few other related terms: “cos‐
mology,” “big bang,” “dark energy,” and “wmap.” You can see cosmology
has been getting more popular since the 1990s, and dark energy is a recent
addition. Credit: Robert Simpson.

In the data, astronomy becomes more and more obsessed with dark
matter — the term appears in 1% of all papers by the end of the ’80s
and 6% today.

Looking at citations changes the picture. The community is writing
papers about dark matter more and more each year, but they are getting
fewer citations than they used to (the peak for this was in the late ’90s).
These trends are normalised, so the only regency effect I can think of
is that dark matter papers take more than 10 years to become citable.
Either that or dark matter studies are currently in a trough for impact.

Can you see where work is dropped by parts of the community and
picked up again?

Robert Simpson: Not yet, but I see what you mean. I need to build a
better picture of the community and its components.

Can you build a social graph of astronomers out of this data? What
about (academic) family trees?

Robert Simpson: Identifying unique authors is my next step, followed
by creating fingerprints of individuals at a given point in time. When
do people create their first-author papers, when do they have the most
impact in their careers, stuff like that.

What tools did you use? In hindsight, would you do it differently?

50 | Chapter 4: The Application of Big Data

I’m using Ruby and Perl to grab the data, MySQL to store and query
it, JavaScript to display it (Google Charts and D3.js). I may still move
the database part to MongoDB because it was designed to store docu‐
ments. Similarly, I may switch from ADS to arXiv as the data source.
Using arXiv would allow me to grab the full text in many cases, even
if it does introduce a peer-review issue.

What’s next?

Robert Simpson: My aim is still to attempt real hypothesis generation.
I’ve begun the process by investigating correlations between terms in
the literature, but I think the power will be in being able to compare
all terms with all terms and looking for the unexpected. Terms may
correlate indirectly (via a third term, for example), so the entire corpus
needs to be processed and optimised to make it work comprehensively.

Science between the Cracks
I’m really looking forward to seeing more results coming out of Rob‐
ert’s work. This sort of analysis hasn’t really been possible before. It’s
showing a lot of promise both from a sociological angle, with the ability
to do research into how science is done and how that has changed, but
also ultimately as a hypothesis engine — something that can generate
new science in and of itself. This is just a hack day experiment. Imagine
what could be done if the literature were more open and this sort of
analysis could be done across fields?

Right now, a lot of the most interesting science is being done in the
cracks between disciplines, but the hardest part of that sort of work is
often trying to understand the literature of the discipline that isn’t your
own. Robert’s project offers a lot of hope that this may soon become
easier.

The Dark Side of Data
By Mike Loukides

Tom Slee’s “Seeing Like a Geek” is a thoughtful article on the dark side
of open data. He starts with the story of a Dalit community in India,
whose land was transferred to a group of higher cast Mudaliars
through bureaucratic manipulation under the guise of standardizing
and digitizing property records. While this sounds like a good idea, it
gave a wealthier, more powerful group a chance to erase older, tradi‐

The Dark Side of Data | 51

tional records that hadn’t been properly codified. One effect of passing
laws requiring standardized, digital data is to marginalize all data that
can’t be standardized or digitized, and to marginalize the people who
don’t control the process of standardization.

That’s a serious problem. It’s sad to see oppression and property theft
riding in under the guise of transparency and openness. But the issue
isn’t open data, but how data is used.

Jesus said “the poor are with you always” not because the poor aren’t
a legitimate area of concern (only an American fundamentalist would
say that), but because they’re an intractable problem that won’t go
away. The poor are going to be the victims of any changes in technol‐
ogy; it isn’t surprisingly that the wealthy in India used data to mar‐
ginalize the land holdings of the poor. In a similar vein, when Euro‐
peans came to North America, I imagine they asked the natives “So,
you got a deed to all this land?,” a narrative that’s still being played
out with indigenous people around the world.

The issue is how data is used. If the wealthy can manipulate legislators
to wipe out generations of records and folk knowledge as “inaccurate,”
then there’s a problem. A group like DataKind could go in and figure
out a way to codify that older generation of knowledge. Then at least,
if that isn’t acceptable to the government, it would be clear that the
problem lies in political manipulation, not in the data itself. And note
that a government could wipe out generations of “inaccurate records”
without any requirement that the new records be open. In years past
the monied classes would have just taken what they wanted, with the
government’s support. The availability of open data gives a plausible
pretext, but it’s certainly not a prerequisite (nor should it be blamed)
for manipulation by the 0.1%.

One can see the opposite happening, too: the recent legislation in
North Carolina that you can’t use data that shows sea level rise. Open
data may be the only possible resource against forces that are interested
in suppressing science. What we’re seeing here is a full-scale retreat
from data and what it can teach us: an attempt to push the furniture
against the door to prevent the data from getting in and changing the
way we act.

The Digital Publishing Landscape
Slee is on shakier ground when he claims that the digitization of books
has allowed Amazon to undermine publishers and booksellers. Yes,

52 | Chapter 4: The Application of Big Data

there’s technological upheaval, and that necessarily drives changes in
business models. Business models change; if they didn’t, we’d still have
the Pony Express and stagecoaches. O’Reilly Media is thriving, in part
because we have a viable digital publishing strategy; publishers without
a viable digital strategy are failing.

But what about booksellers? The demise of the local bookstore has, in
my observation, as much to do with Barnes & Noble superstores (and
the now-defunct Borders), as with Amazon, and it played out long
before the rise of ebooks.

I live in a town in southern Connecticut, roughly a half-hour’s drive
from the two nearest B&N outlets. Guilford and Madison, the town
immediately to the east, both have thriving independent bookstores.
One has a coffeeshop, stages many, many author events (roughly one
a day), and runs many other innovative programs (birthday parties,
book-of-the-month services, even ebook sales). The other is just a
small local bookstore with a good collection and knowledgeable staff.
The town to the west lost its bookstore several years ago, possibly be‐
fore Amazon even existed. Long before the Internet became a factor,
it had reduced itself to cheap gift items and soft porn magazines. So:
data may threaten middlemen, though it’s not at all clear to me that
middlemen can’t respond competitively. Or that they are really threat‐
ened by “data,” as opposed to large centralized competitors.

There are also countervailing benefits. With ebooks, access is demo‐
cratized. Anyone, anywhere has access to what used to be available
only in limited, mostly privileged locations. At O’Reilly, we now sell
ebooks in countries we were never able to reach in print. Our print
sales overseas never exceeded 30% of our sales; for ebooks, overseas
represents more than half the total, with customers as far away as
Azerbaijan.

Slee also points to the music labels as an industry that has been margi‐
nalized by open data. I really refuse to listen to whining about all the
money that the music labels are losing. We’ve had too many years of
crap product generated by marketing people who only care about
finding the next Justin Bieber to take the “creative industry” and its
sycophants seriously.

Privacy by Design
Data inevitably brings privacy issues into play. As Slee points out (and
as Jeff Jonas has before him), apparently insignificant pieces of data

The Dark Side of Data | 53

can be put together to form a surprisingly accurate picture of who you
are, a picture that can be sold. It’s useless to pretend that there won’t
be increased surveillance in any forseeable future, or that there won’t
be an increase in targeted advertising (which is, technically, much the
same thing).

We can bemoan that shift, celebrate it, or try to subvert it, but we can’t
pretend that it isn’t happening. We shouldn’t even pretend that it’s new,
or that it has anything to do with openness. What is a credit bureau if
not an organization that buys and sells data about your financial his‐
tory, with no pretense of openness?

Jonas’s concept of “privacy by design” is an important attempt to ad‐
dress privacy issues in big data. Jonas envisions a day when “I have
more privacy features than you” is a marketing advantage. It’s certainly
a claim I’d like to see Facebook make.

Absent a solution like Jonas’s, data is going to be collected, bought,
sold, and used for marketing and other purposes, whether it is “open”
or not. I do not think we can get to Jonas’s world, where privacy is
something consumers demand, without going through a stage where
data is open and public. It’s too easy to live with the illusion of privacy
that thrives in a closed world.

I agree that the notion that “open data” is an unalloyed public good is
mistaken, and Tom Slee has done a good job of pointing that out. It
underscores the importance of a still-nascent ethical consensus about
how to use data, along with the importance of data watchdogs, Data‐
Kind, and other organizations devoted to the public good. (I don’t
understand why he argues that Apple and Amazon “undermine com‐
munity activism”; that seems wrong, particularly in the light of Apple’s
re-joining the EPEAT green certification system for their products
after a net-driven consumer protest.) Data collection is going to hap‐
pen whether we like it or not, and whether it’s open or not. I am con‐
vinced that private data is a public bad, and I’m less afraid of data that’s
open. That doesn’t make it necessarily a good; that depends on how
the data is used, and the people who are using it.

54 | Chapter 4: The Application of Big Data

CHAPTER 5

What to Watch for in Big Data

Big Data Is Our Generation’s Civil Rights Issue,
and We Don’t Know It
By Alistair Croll

Data doesn’t invade people’s lives. Lack of control over how it’s used does.

What’s really driving so-called big data isn’t the volume of information.
It turns out big data doesn’t have to be all that big. Rather, it’s about a
reconsideration of the fundamental economics of analyzing data.

For decades, there’s been a fundamental tension between three at‐
tributes of databases. You can have the data fast; you can have it big;
or you can have it varied. The catch is, you can’t have all three at once.

55

I first heard this as the “three V’s of data”: Volume, Variety, and Ve‐
locity. Traditionally, getting two was easy but getting three was very,
very, very expensive.

The advent of clouds, platforms like Hadoop, and the inexorable march
of Moore’s Law means that now, analyzing data is trivially inexpensive.
And when things become so cheap that they’re practically free, big
changes happen — just look at the advent of steam power, or the copy‐
ing of digital music, or the rise of home printing. Abundance replaces
scarcity, and we invent new business models.

In the old, data-is-scarce model, companies had to decide what to col‐
lect first, and then collect it. A traditional enterprise data warehouse
might have tracked sales of widgets by color, region, and size. This act
of deciding what to store and how to store it is called designing the
schema, and in many ways, it’s the moment where someone decides
what the data is about. It’s the instant of context.

That needs repeating:

You decide what data is about the moment you define its schema.

With the new, data-is-abundant model, we collect first and ask ques‐
tions later. The schema comes after the collection. Indeed, big data
success stories like Splunk, Palantir, and others are prized because of
their ability to make sense of content well after it has been collected —
sometimes called a schema-less query. This means we collect infor‐
mation long before we decide what it’s for.

And this is a dangerous thing.

When bank managers tried to restrict loans to residents of certain areas
(known as redlining), Congress stepped in to stop it (with the Fair
Housing Act of 1968). They were able to legislate against discrimina‐
tion, making it illegal to change loan policy based on someone’s race.

56 | Chapter 5: What to Watch for in Big Data

Home Owners’ Loan Corporation map showing redlining of “hazardous”
districts in 1936. Credit: Wikipedia

“Personalization” is another word for discrimination. We’re not dis‐
criminating if we tailor things to you based on what we know about
you — right? That’s just better service.

In one case, American Express used purchase history to adjust credit
limits based on where a customer shopped, despite his excellent credit
limit:

Johnson says his jaw dropped when he read one of the reasons Amer‐
ican Express gave for lowering his credit limit: “Other customers who
have used their card at establishments where you recently shopped
have a poor repayment history with American Express.”

We’re seeing the start of this slippery slope everywhere from tailored
credit-card limits like this one to car insurance based on driver pro‐
files. In this regard, big data is a civil rights issue, but it’s one that society
in general is ill-equipped to deal with.

We’re great at using taste to predict things about people. OKcupid’s
2010 blog post “The Real Stuff White People Like” showed just how
easily we can use information to guess at race. It’s a real eye-opener

Big Data Is Our Generation’s Civil Rights Issue, and We Don’t Know It | 57

(and the guys who wrote it didn’t include everything they learned —
some of it was a bit too controversial). They simply looked at the words
one group used which others didn’t often use. The result was a list of
“trigger” words for a particular race or gender.

Now run this backwards. If I know you like these things, or see you
mention them in blog posts, on Facebook, or in tweets, then there’s a
good chance I know your gender and your race, and maybe even your
religion and your sexual orientation. And that I can personalize my
marketing efforts towards you.

That makes it a civil rights issue.

If I collect information on the music you listen to, you might assume
I will use that data in order to suggest new songs, or share it with your
friends. But instead, I could use it to guess at your racial background.
And then I could use that data to deny you a loan.

Want another example? Check out Private Data In Public Ways, some‐
thing I wrote a few months ago after seeing a talk at Big Data London,
which discusses how publicly available last name information can be
used to generate racial boundary maps:

Screen from the Mapping London project.

This TED talk by Malte Spitz does a great job of explaining the chal‐
lenges of tracking citizens today, and he speculates about whether the
Berlin Wall would ever have come down if the Stasi had access to phone
records in the way today’s governments do.

58 | Chapter 5: What to Watch for in Big Data

So how do we regulate the way data is used?

The only way to deal with this properly is to somehow link what the
data is with how it can be used. I might, for example, say that my musical
tastes should be used for song recommendation, but not for banking
decisions.

Tying data to permissions can be done through encryption, which is
slow, riddled with DRM, burdensome, hard to implement, and bad for
innovation. Or it can be done through legislation, which has about as
much chance of success as regulating spam: it feels great, but it’s
damned hard to enforce.

There are brilliant examples of how a quantified society can improve
the way we live, love, work, and play. Big data helps detect disease
outbreaks, improve how students learn, reveal political partisanship,
and save hundreds of millions of dollars for commuters — to pick just
four examples. These are benefits we simply can’t ignore as we try to
survive on a planet bursting with people and shaken by climate and
energy crises.

But governments need to balance reliance on data with checks and
balances about how this reliance erodes privacy and creates civil and
moral issues we haven’t thought through. It’s something that most of
the electorate isn’t thinking about, and yet it affects every purchase
they make.

This should be fun.

Big Data Is Our Generation’s Civil Rights Issue, and We Don’t Know It | 59

Three Kinds of Big Data
By Alistair Croll

In the past couple of years, marketers and pundits have spent a lot of
time labeling everything “big data.” The reasoning goes something like
this:

• Everything is on the Internet.
• The Internet has a lot of data.
• Therefore, everything is big data.

When you have a hammer, everything looks like a nail. When you have
a Hadoop deployment, everything looks like big data. And if you’re
trying to cloak your company in the mantle of a burgeoning industry,
big data will do just fine. But seeing big data everywhere is a sure way
to hasten the inevitable fall from the peak of high expectations to the
trough of disillusionment.

We saw this with cloud computing. From early idealists saying every‐
thing would live in a magical, limitless, free data center to today’s
pragmatism about virtualization and infrastructure, we soon took off
our rose-colored glasses and put on welding goggles so we could ac‐
tually build stuff.

So where will big data go to grow up?

Once we get over ourselves and start rolling up our sleeves, I think big
data will fall into three major buckets: Enterprise BI, Civil Engineering,
and Customer Relationship Optimization. This is where we’ll see most
IT spending, most government oversight, and most early adoption in
the next few years.

Enterprise BI 2.0
For decades, analysts have relied on business intelligence (BI) products
like Hyperion, Microstrategy and Cognos to crunch large amounts of
information and generate reports. Data warehouses and BI tools are
great at answering the same question — such as “what were Mary’s
sales this quarter?” — over and over again. But they’ve been less good

60 | Chapter 5: What to Watch for in Big Data

at the exploratory, what-if, unpredictable questions that matter for
planning and decision-making because that kind of fast exploration
of unstructured data is traditionally hard to do and therefore expen‐
sive.

Most “legacy” BI tools are constrained in two ways:

• First, they’ve been schema-then-capture tools in which the analyst
decides what to collect, then later captures that data for analysis.

• Second, they’ve typically focused on reporting what Avinash
Kaushik (channeling Donald Rumsfeld) refers to as “known un‐
knowns” — things we know we don’t know, and generate reports
for.

These tools are used for reporting and operational purposes, and are
usually focused on controlling costs, executing against an existing
plan, and reporting on how things are going.

As my Strata co-chair Edd Dumbill pointed out when I asked for
thoughts on this piece:

The predominant functional application of big data technologies to‐
day is in ETL (Extract, Transform, and Load). I’ve heard the figure
that it’s about 80% of Hadoop applications. Just the real grunt work
of log file or sensor processing before loading into an analytic database
like Vertica.

The availability of cheap, fast computers and storage, as well as open
source tools, have made it okay to capture first and ask questions later.
That changes how we use data because it lets analysts speculate beyond
the initial question that triggered the collection of data.

What’s more, the speed with which we can get results — sometimes as
fast as a human can ask them — makes data easier to explore interac‐
tively. This combination of interactivity and speculation takes BI into
the realm of “unknown unknowns,” the insights that can produce a
competitive advantage or an out-of-the-box differentiator.

Cloud computing underwent a transition from promise to compro‐
mise. First, big, public clouds wooed green-field startups. Then, a few
years later, incumbent IT vendors introduced private cloud offerings.
These private clouds included only a fraction of the benefits of their
public cousins — but were nevertheless a sufficient blend of smoke,

Three Kinds of Big Data | 61

mirrors, and features to delay the inevitable move to public resources
by a few years and appease the business. For better or worse, that’s
where most IT cloud budgets are being spent today according to IDC,
Gartner, and others. Big data adoption will undergo a similar cycle.

In the next few years, then, look for acquisitions and product intro‐
ductions — and not a little vaporware — as BI vendors that enterprises
trust bring them “big data lite”: enough innovation and disruption to
satisfy the CEO’s golf buddies, but not so much that enterprise IT’s
jobs are threatened. This, after all, is how change comes to big organ‐
izations.

Ultimately, we’ll see traditional “known unknowns” BI reporting living
alongside big-data-powered data import and cleanup, and fast, ex‐
ploratory data “unknown unknown” interactivity.

Civil Engineering
The second use of big data is in society and government. Already, data
mining can be used to predict disease outbreaks, understand traffic
patterns, and improve education.

Cities are facing budget crunches, infrastructure problems, and crowd‐
ing from rural citizens. Solving these problems is urgent, and cities are
perfect labs for big data initiatives. Take a metropolis like New York:
hackathons; open feeds of public data; and a population that generates
a flood of information as it shops, commutes, gets sick, eats, and just
goes about its daily life.

I think municipal data is one of the big three for several reasons: it’s a
good tie breaker for partisanship, we have new interfaces everyone
can understand, and we finally have a mostly-connected citizenry.

In an era of partisan bickering, hard numbers can settle the debate. So,
they’re not just good government; they’re good politics. Expect to see
big data applied to social issues, helping us to make funding more
effective and scarce government resources more efficient (perhaps to
the chagrin of some public servants and lobbyists). As this works in
the world’s biggest cities, it’ll spread to smaller ones, to states, and to
municipalities.

62 | Chapter 5: What to Watch for in Big Data

Making data accessible to citizens is possible, too: Siri and Google Now
show the potential for personalized agents; Narrative Science takes
complex data and turns it into words the masses can consume easily;
Watson and Wolfram Alpha can give smart answers, either through
curated reasoning or making smart guesses.

For the first time, we have a connected citizenry armed (for the most
part) with smartphones. Nielsen estimated that smartphones would
overtake feature phones in 2011, and that concentration is high in
urban cores. The App Store is full of apps for bus schedules, commut‐
ers, local events, and other tools that can quickly become how gov‐
ernments connect with their citizens and manage their bureaucracies.

The consequence of all this, of course, is more data. Once governments
go digital, their interactions with citizens can be easily instrumented
and analyzed for waste or efficiency. That’s sure to provoke resistance
from those who don’t like the scrutiny or accountability, but it’s a side
effect of digitization: every industry that goes digital gets analyzed and
optimized, whether it likes it or not.

Customer Relationship Optimization
The final home of applied big data is marketing. More specifically, it’s
improving the relationship with consumers so companies can, as Ser‐
gio Zyman once said, sell them more stuff, more often, for more money,
more efficiently.

The biggest data systems today are focused on web analytics, ad opti‐
mization, and the like. Many of today’s most popular architectures
were weaned on ads and marketing, and have their ancestry in direct
marketing plans. They’re just more focused than the comparatively
blunt instruments with which direct marketers used to work.

The number of contact points in a company has multiplied signifi‐
cantly. Where once there was a phone number and a mailing address,
today there are web pages, social media accounts, and more. Tracking
users across all these channels — and turning every click, like, share,
friend, or retweet into the start of a long funnel that leads, inexorably,
to revenue is a big challenge. It’s also one that companies like Salesforce
understand, with its investments in chat, social media monitoring, co-
browsing, and more.

Three Kinds of Big Data | 63

This is what’s lately been referred to as the “360-degree customer view”
(though it’s not clear that companies will actually act on customer
data if they have it, or whether doing so will become a compliance
minefield). Big data is already intricately linked to online marketing,
but it will branch out in two ways.

First, it’ll go from online to offline. Near-field-equipped smartphones
with ambient check-in are a marketer’s wet dream, and they’re coming
to pockets everywhere. It’ll be possible to track queue lengths, store
traffic, and more, giving retailers fresh insights into their brick-and-
mortar sales. Ultimately, companies will bring the optimization that
online retail has enjoyed to an offline world as consumers become
trackable.

Second, it’ll go from Wall Street (or maybe that’s Madison Avenue and
Middlefield Road) to Main Street. Tools will get easier to use, and while
small businesses might not have a BI platform, they’ll have a tablet or
a smartphone that they can bring to their places of business. Mobile
payment players like Square are already making them reconsider the
checkout process. Adding portable customer intelligence to the tool
suite of local companies will broaden how we use marketing tools.

Headlong into the Trough
That’s my bet for the next three years, given the molasses of market
confusion, vendor promises, and unrealistic expectations we’re about
to contend with. Will big data change the world? Absolutely. Will it be
able to defy the usual cycle of earnest adoption, crushing disappoint‐
ment, and eventual rebirth all technologies must travel? Certainly not.

Automated Science, Deep Data, and the
Paradox of Information
By Bradley Voytek

A lot of great pieces have been written about the relatively recent surge
in interest in big data and data science, but in this piece I want to
address the importance of deep data analysis: what we can learn from
the statistical outliers by drilling down and asking, “What’s different
here? What’s special about these outliers, and what do they tell us about
our models and assumptions?”

64 | Chapter 5: What to Watch for in Big Data

The reason that big data proponents are so excited about the bur‐
geoning data revolution isn’t just because of the math. Don’t get me
wrong, the math is fun, but we’re excited because we can begin to distill
patterns that were previously invisible to us due to a lack of information.

That’s big data.

Of course, data are just a collection of facts; bits of information that
are only given context — assigned meaning and importance — by
human minds. It’s not until we do something with the data that any of
it matters. You can have the best machine learning algorithms, the
tightest statistics, and the smartest people working on them, but none
of that means anything until someone makes a story out of the results.

And therein lies the rub.

Do all these data tell us a story about ourselves and the universe in
which we live, or are we simply hallucinating patterns that we want to
see?

(Semi)Automated Science
In 2010, Cornell researchers Michael Schmidt and Hod Lipson pub‐
lished a groundbreaking paper in Science titled “Distilling Free-Form
Natural Laws from Experimental Data.” The premise was simple, and
it essentially boiled down to the question, “can we algorithmically ex‐
tract models to fit our data?”

So they hooked up a double pendulum — a seemingly chaotic system
whose movements are governed by classical mechanics — and trained
a machine learning algorithm on the motion data.

Their results were astounding. (See them here.)

In a matter of minutes the algorithm converged on Newton’s second
law of motion: f = ma. What took humanity tens of thousands of years
to accomplish was completed on 32-cores in essentially no time at all.

In 2011, some neuroscience colleagues of mine, lead by Tal Yarkoni,
published a paper in Nature Methods titled “Large-scale automated
synthesis of human functional neuroimaging data.” In this paper, the
authors sought to extract patterns from the overwhelming flood of
brain imaging research.

To do this, they algorithmically extracted the 3D coordinates of sig‐
nificant brain activations from thousands of neuroimaging studies,

Automated Science, Deep Data, and the Paradox of Information | 65

along with words that frequently appeared in each study. Using these
two pieces of data along with some simple (but clever) mathematical
tools, they were able to create probabilistic maps of brain activation
for any given term.

In other words, you type in a word such as “learning” on their website
search and visualization tool, NeuroSynth, and they give you back a
pattern of brain activity that you should expect to see during a learning
task.

But that’s not all. Given a pattern of brain activation, the system can
perform a reverse inference, asking, “given the data that I’m observing,
what is the most probable behavioral state that this brain is in?”

Similarly, in late 2010, my wife (Jessica Voytek) and I undertook a
project to algorithmically discover associations between concepts in
the peer-reviewed neuroscience literature. As a neuroscientist, the goal
of my research is to understand relationships between the human
brain, behavior, physiology, and disease. Unfortunately, the facts that
tie all that information together are locked away in more than 21 mil‐
lion static peer-reviewed scientific publications.

How many undergrads would I need to hire to read through that many
papers? Any volunteers?

Even more mind-boggling, each year more than 30,000 neuroscientists
attend the annual Society for Neuroscience conference. If we assume
that only two-thirds of those people actually do research, and if we
assume that they only work a meager (for the sciences) 40 hours a
week, that’s around 40 million person-hours dedicated to but one
branch of the sciences.

Annually.

This means that in the 10 years I’ve been attending that conference,
more than 400 million person-hours have gone toward the pursuit of
understanding the brain. Humanity built the pyramids in 30 years. The
Apollo Project got us to the moon in about eight.

So my wife and I said to ourselves, “there has to be a better way.”

Which lead us to create brainSCANr, a simple (simplistic?) tool (cur‐
rently itself under peer review) that makes the assumption that the
more often two concepts appear together in the titles or abstracts of
published papers, the more likely they are to be associated with one
another.

66 | Chapter 5: What to Watch for in Big Data

For example, if 10,000 papers mention “Alzheimer’s disease” that al‐
so mention “dementia,” then Alzheimer’s disease is probably related to
dementia. In fact, there are 17,087 papers that mention Alzheimer’s
and dementia, whereas there are only 14 papers that mention Alz‐
heimer’s and, for example, creativity.

From this, we built what we’re calling the “cognome,” a mapping be‐
tween brain structure, function, and disease.

Big data, data mining, and machine learning are becoming critical
tools in the modern scientific arsenal. Examples abound: text mining
recipes to find cultural food taste preferences, analyzing cultural trends
via word use in books (“culturomics”), identifying seasonality of mood
from tweets, and so on.

But so what?

Deep Data
What those three studies show us is that it’s possible to automate, or
at least semi-automate, critical aspects of the scientific method itself.
Schmidt and Lipson show that it is possible to extract equations that
perfectly model even seemingly chaotic systems. Yarkoni and collea‐
gues show that it is possible to infer a complex behavioral state given
input brian data.

My wife and I wanted to show that brainSCANr could be put to work
for something more useful than just quantifying relationships between
terms. So we created a simple algorithm to perform what we’re calling
“semi-automated hypothesis generation,” which is predicated on a ba‐
sic “the friend of a friend should be a friend” concept.

In the example below, the neurotransmitter “serotonin” has thousands
of shared publications with “migraine,” as well as with the brain region
“striatum.” However, migraine and striatum only share 16 publica‐
tions.

Automated Science, Deep Data, and the Paradox of Information | 67

That’s very odd. Because in medicine there is a serotonin hypothesis
for the root cause of migraines. And we (neuroscientists) know that
serotonin is released in the striatum to modulate brain activity in that
region. Given that those two things are true, why is there so little re‐
search regarding the role of the striatum in migraines?

Perhaps there’s a missing connection?

Such missing links and other outliers in our models are the essence of
deep data analytics. Sure, any data scientist worth their salt can take a
mountain of data and reduce it down to a few simple plots. And such
plots are important because they tell a story. But those aren’t the only
stories that our data can tell us.

For example, in my geoanalytics work as the data evangelist for Uber,
I put some of my (definitely rudimentary) neuroscience network an‐
alytic skills to work to figure out how people move from neighborhood
to neighborhood in San Francisco.

At one point, I checked to see if men and women moved around the
city differently. A very simple regression model showed that the num‐
ber of men who go to any given neighborhood significantly predicts
the number of women who go to that same neighborhood.

No big deal.

68 | Chapter 5: What to Watch for in Big Data

But what’s cool was seeing where the outliers were. When I looked at
the models’ residuals, that’s where I found the far more interesting
story. While it’s good to have a model that fits your data, knowing
where the model breaks down is not only important for internal met‐
rics, but it also makes for a more interesting story: What’s happening
in the Marina district that so many more women want to go there?
And why are there so many more men in SoMa?

The Paradox of Information
The interpretation of big data analytics can be a messy game. Maybe
there are more men in SoMa because that’s where AT&T Park is. But
maybe there are just five guys who live in SoMa who happen to take
Uber 100 times more often than average.

While data-driven posts make for fun reading (and writing), in the
sciences we need to be more careful that we don’t fall prey to ad hoc,
just-so stories that sound perfectly reasonable and plausible, but which
we cannot conclusively prove.

In 2008, psychologists David McCabe and Alan Castel published a
paper in the journal Cognition, titled “Seeing is believing: The effect
of brain images on judgments of scientific reasoning.” In that paper,
they showed that summaries of cognitive neuroscience findings that
are accompanied by an image of a brain scan were rated as more credi‐
ble by the readers.

This should cause any data scientist serious concern. In fact, I’ve for‐
mulated three laws of statistical analyses:

1. The more advanced the statistical methods used, the fewer critics
are available to be properly skeptical.

2. The more advanced the statistical methods used, the more likely
the data analyst will be to use math as a shield.

3. Any sufficiently advanced statistics can trick people into believing
the results reflect truth.

The first law is closely related to the “bike shed effect” (also known as
Parkinson’s Law of Triviality) which states that, “the time spent on any
item of the agenda will be in inverse proportion to the sum involved.”

Automated Science, Deep Data, and the Paradox of Information | 69

In other words, if you try to build a simple thing such as a public bike
shed, there will be endless town hall discussions wherein people argue
over trivial details such as the color of the door. But if you want to build
a nuclear power plant — a project so vast and complicated that most
people can’t understand it — people will defer to expert opinion.

Such is the case with statistics.

If you make the mistake of going into the comments section of any
news piece discussing a scientific finding, invariably someone will
leave the comment, “correlation does not equal causation.”

We’ll go ahead and call that truism Voytek’s fourth law.

But people rarely have the capacity to argue against the methods and
models used by, say, neuroscientists or cosmologists.

But sometimes we get perfect models without any understanding of
the underlying processes. What do we learn from that?

The always fantastic Radiolab did a follow-up story on the Schmidt
and Lipson “automated science” research in an episode titled “Limits
of Science.” It turns out, a biologist contacted Schmidt and Lipson and
gave them data to run their algorithm on. They wanted to figure out
the principles governing the dynamics of a single-celled bacterium.
Their result?

Well sometimes the stories we tell with data…they just don’t make
sense to us.

They found “two equations that describe the data.”

But they didn’t know what the equations meant. They had no context.
Their variables had no meaning. Or, as Radiolab co-host Jad Abum‐
rad put it, “the more we turn to computers with these big questions,
the more they’ll give us answers that we just don’t understand.”

So while big data projects are creating ridiculously exciting new vistas
for scientific exploration and collaboration, we have to take care to
avoid the Paradox of Information wherein we can know too many
things without knowing what those “things” are.

Because at some point, we’ll have so much data that we’ll stop being
able to discern the map from the territory. Our goal as (data) scientists
should be to distill the essence of the data into something that tells as

70 | Chapter 5: What to Watch for in Big Data

true a story as possible while being as simple as possible to understand.
Or, to operationalize that sentence better, we should aim to find bal‐
ance between minimizing the residuals of our models and maximizing
our ability to make sense of those models.

Recently, Stephen Wolfram released the results of a 20-year long ex‐
periment in personal data collection, including every keystroke he’s
typed and every email he’s sent. In response, Robert Krulwich, the
other co-host of Radiolab, concludes by saying “I’m looking at your
data [Dr. Wolfram], and you know what’s amazing to me? How much
of you is missing.”

Personally, I disagree; I believe that there’s a humanity in those num‐
bers and that Mr. Krulwich is falling prey to the idea that science
somehow ruins the magic of the universe. Quoth Dr. Sagan:

It is sometimes said that scientists are unromantic, that their passion
to figure out robs the world of beauty and mystery. But is it not stirring
to understand how the world actually works — that white light is made
of colors, that color is the way we perceive the wavelengths of light,
that transparent air reflects light, that in so doing it discriminates
among the waves, and that the sky is blue for the same reason that the
sunset is red? It does no harm to the romance of the sunset to know
a little bit about it.

So go forth and create beautiful stories, my statistical friends. See you
after peer-review.

The Chicken and Egg of Big Data Solutions
By Jim Stogdill

Before I came to O’Reilly I was building the “big data and disruptive
analytics practice” at a major systems integrator. It was a blast to spend
every week talking to customers in different industries who were wak‐
ing up to the possibilities that technologies like Hadoop offered their
businesses. Many of these businesses are going to fundamentally
change as they embrace this stuff (or be replaced by those that do). But
there’s a catch.

Twenty years or so ago large integrators made big business building
applications on the then-new relational paradigm. They put in Oracle

The Chicken and Egg of Big Data Solutions | 71

databases with custom code, wrote PowerBuilder apps on Sybase, and
of course lots of businesses rolled their own with VB and SQL Server.
It was an era of custom coding where Oracle, Sybase, SQL Server, In‐
formix and etc. were thought of as platforms to build stuff on.

Then the market matured and shifted to package solution implemen‐
tation. ERP, CRM,…, etc. The big guys focused on integrating again
and told their clients there was no ROI in building custom stuff. ROI
would come from integrating best-of-breed solutions. Databases be‐
came commodity back ends to the applications that were always the
real focus.

Now along comes big data, NoSQL, data science, and all that stuff and
it seems like we’re starting the cycle over again. But this time clients,
having been well trained over the last decade or so, aren’t having any
of that “build it from scratch” mentality. They know that Hadoop and
other new technologies can be transformative to their business, but
they want it packaged up and solution’ified like they are used to. I heard
a lot of “let us know when you have a solution already built or available
to buy that does X” in the last year.

Also, lots of the shops that do this stuff at scale are built and staffed
around the package implementation model and have shed many of the
skills they used to have for custom work. Everything from staffing
models to methodologies are oriented toward package installation.

So, here we are with all of this disruptive technology, but we seem to
have lost the institutional wherewithal to do anything with it in a lot
of large companies. Of course that fact was hard on my numbers. I had
a great pipeline of companies with pain to solve, and great technologies
to solve it, but too much of the time it was hard to close it without
readymade solutions.

Every week I talked to the companies building these new platforms to
share leads and talk about their direction. After a while I started cutting
them off when they wanted to talk about the features of their next
release. I just got to the point where I didn’t really care, it just wasn’t
all that relevant to my customers. I mean, it’s important that they are
making the platforms more manageable and building bridges to tra‐
ditional BI, ETL, RDBMS, and the like. But the focus was too much on
platforms and tools.

I wanted to know “What are you doing to encourage solution devel‐
opment? Are you staffing a support system for ISVs? What startups

72 | Chapter 5: What to Watch for in Big Data

and/or established players are you aware of that are building solutions
on this platform?” So when I saw this tweet I let out a little yelp. Awe‐
some! The lack of ready-to-install solutions was getting attention, and
from Mike Olsen.

You can watch the rest of what Mike Olson said here and you’ll find
he tells a similar story about the RDBMS historical parallel.

I talked to Mike a few weeks ago to find out what was behind his
comment and explore what else they are doing to support solution
development. It boils down to what he said — he will help connect you
with money — plus a newly launched partner program designed to
provide better support to ISVs among others. Also, the continued at‐
tention to APIs and tools like Pig and Hive should make it easier for
the solution ecosystem to develop. It can only be good for his business
to have lots of other companies directly solving business problems,
and simply pulling in his platform.

Hortonworks also started a partner program in the fall, and I think
we’ll see a lot more emphasis on this across the space this year. How‐
ever, at the moment wherever I look (Hortonworks Partners, Cloudera
Partners, Accel big data portfolio) the focus today remains firmly on
platform and tools or partnering with integrators. Tresata, a startup
focused on financial risk management, pops up in a lot of lists as the
obvious odd one out — an actual domain-specific solution.

What about other people that could be building solutions? Is it the
maturity level of the technology, the lack of penetration of Hadoop,
etc., into your customer’s data centers, or some combination of other
factors that is slowing things down?

Of course, during the RDBMS adoption it took a lot of years before
the custom era was over and thoroughly replaced by the era of package
implementation. The question I’m pondering is whether customer ex‐
pectations and the pace of technology will make it happen faster this
time? Or is the disruptive value of big data going to continue to accrue
only to risk-taking early adopters for the foreseeable future?

Walking the Tightrope of Visualization
Criticism
By Andy Kirk

Walking the Tightrope of Visualization Criticism | 73

In a talk at the SEE conference in Germany, data illustrator Stefanie
Posavec opened her talk with a sobering observation about how she
had found the data visualization field really intimidating.

Her experience was that many visualization bloggers and active par‐
ticipants seem to believe in one right way and lots of wrong ways to
create a visualization. To those entering the field, these types of views
will create a fair amount of confusion, inconsistency, and contradic‐
tion. It demonstrates our current glass-is-never-full tendency toward
critical evaluation.

This should act as an important wake-up call to all of us who care about
maintaining an accessible and supportive community around data
visualization and infographic design, particularly as these disciplines
continue to penetrate the mainstream consciousness.

The fear is that Posavec’s expressing of this view is just the tip of an
iceberg. Who knows how many designers outside of the spotlight hold
a similar perception and are reluctant to share their work and engage
with the field?

In this article, I seek to take a detached view of the visualization field
and weave in my experiences from delivering introductory data visu‐
alization training courses over the past year. I want to take a look at
the constituency of this discipline and the role of critique to see how
Posavec’s experiences could have materialised and contrast them with
the people I meet in my classes.

The Visualization Ecosystem
One of the most rewarding personal experiences from my training
courses has been getting the chance to mix with a variety of delegates
from different countries, cultures, occupations, and industries. Spend‐
ing time with essentially everyday people, learning as much from them
as they do from me, has been hugely refreshing. The term “everyday
people” could be perceived as condescending, but far from it. Allow
me to elaborate.

When you are an active participant in a field like data visualization
you spend most of your time consuming and digesting information
from your peers. This can create a bubble of exposure to just those
hardcore connoisseurs — the academics, the authors, the designers,
and the bloggers — who have spent years refining their knowledge and
perfecting their craft.

74 | Chapter 5: What to Watch for in Big Data

This is the sharp-end of the field where the intensity of debate, knowl‐
edge exchange, and opinion expression is high. The observations
emerging at this level represent the most perceptive, creative, and
comprehensive insights into the design techniques on show today. The
attention to detail, the care for quality, and the commitment to eval‐
uation and feedback is significant. However, it can inadvertently create
a certain suffocating or perhaps inhibiting barrier for many looking
to learn and develop their capabilities.

A key observation from my training courses has been the sense that
we in the field could be accused, at times, of a certain amount of design
snobbery. We criticise and lambast many of the popular but “trashy”
infographics, and believe them to be an inferior practice. However,
during training sessions, I invite delegates to assess a variety of differ‐
ent types of visualization design, including such infographic pieces. I
often hear comments that express and reason a preference or even a
“like” for pieces that I would not. This has proven to be a highly illu‐
minating experience.

A consequence of associating with or belonging to this top-tier “bub‐
ble” is that you can become somewhat detached and even oblivious to
the opinions of those who might be considered to exist in the real
world. These are the casual enthusiasts, the everyday people I men‐
tioned early. They are likely to be beginning their journey into the field
or have been nibbling around the edges for a while, but probably never
too seriously until now. In contrast to the hardcore connoisseurs, this
lower-expertise but more highly populated tier of the field’s pyramid
of participants makes up a totally different demographic and psycho‐
graphic.

These people provide a great tapestry of different opinions, back‐
grounds, and capabilities and, generally, they offer a more sympathetic,
fresh, and open-minded view on visualization design. Without the
burden of knowledge, theories, and principles that the rest of us carry
around with us all the time, and by not living and breathing the subject
across every waking hour, their appreciation of visualization is more
rooted in taste and instinct and fueled by a fresh enthusiasm to con‐
sume information in visual form.

Beyond and beneath this middle-tier sits, well, everybody else. These
are the purely occasional consumers and nothing more. Their daily
roles may not have anything to do with data, they possibly don’t even
know or probably care what visualization is. Yet, they belong to the

Walking the Tightrope of Visualization Criticism | 75

almost silent but abundant cohort of people who are occasionally cu‐
rious enough to look at an attractive visualization or light-weight in‐
fographic. They don’t want or need to learn about the field, they just
find enough interest in having a look at some of its output.

This is the true make-up of the visualization and infographic field, and
we need to appreciate its relevance.

The Irrationality of Needs: Fast Food to Fine Dining
There is a prominent, long-established film critic in the U.K. who is
generally considered a fair and sound judge of movies. He has deep
subject expertise and is capable of fully reasoning all his reviews with
thorough analysis. Despite this, he does occasionally resort to the ri‐
poste “other opinions are available, but they’re the wrong ones” when
challenged by readers or viewers.

As with any subject’s “expert” tier, we in data visualization can find
ourselves being a little too closed off, perhaps believing the merit of
our views hold greater weight than other, contrary opinions from out‐
side. But this is largely because we don’t always entirely appreciate the
variety of intentions and needs behind visualization designs. Further‐
more, there are so many different contexts, target audiences, and for‐
mats through which visual communication of data can exist.

Sometimes we’re looking to impart a data-driven communication
where the absolute accuracy of interpretation is vital. On other occa‐
sions it might be about creating a visual representation of data to im‐
pact more on an emotional level, trying to change behaviour and con‐
nect with people through non-standard methods. Sometimes we are
working on subjects that are important, complex, and deep, and re‐
quire a more engaging and prolonged interactive exploratory experi‐
ence. By contrast, we might just be presenting some rather lightweight
facts or stats that relate to a harmless, maybe even “fun,” subject matter.

This is where a comparison with other creative territories like music,
TV, movies, and food is appropriate to help illustrate how fundamen‐
tally impulsive, inconsistent, and irrational our tastes can be. Of
course, the intention is very different with these channels of expres‐
sion, but still we can relate to experiences when we sometimes prefer
a fast food meal or to feast on junk food snacks as opposed to sitting
down to a wholesome, home-cooked meal. We know it’s probably bad
for us, we’ll probably spend more money on it, and we know we’re likely
to be hungry again in an hour, but we still do it.

76 | Chapter 5: What to Watch for in Big Data

You will typically never be too far away from running across intelligent,
well-written movies or TV programs, but sometimes a trashy, loud,
special-effects-laden blockbuster just does the trick. The critics might
have told us how much we should hate them and how we should have
spent our time with a more critically acclaimed work, but we don’t care;
we just want some mindless escapism. You can extend this to writing.
Maybe we should all be sitting down in our spare time reading Shake‐
speare or Keats, enriching our minds. But most of us aren’t. I know I’m
not.

You can extend this to music, art, or really to any other creative chan‐
nel. Of course, there are many other factors at play (access, time, re‐
sources, peer influences, etc.), but we still instinctively seek to mix
things up on occasion and go against the grain. Being told what we
should and shouldn’t do can create as many followers as it does op‐
ponents.

It’s the same with visualization. For many people, sometimes a harm‐
less infographic showing some throw-away facts or stats about social
media, or demonstrating how to avoid getting bitten by a shark is just
what people fancy viewing at that point in time. This explains the vast
success of works presented on gallery sites like visual.ly, the growth of
design agencies like Column Five, and the general phenomenon of
modern-day tower infographics.

Whilst more important subjects and works from leading organisations
like the New York Times are arguably where we should be paying our
attention to learn and respond to critical issues, occasionally we just
need a release. We just want a blend of different visuals. This is the
visualization ecosystem, and we need to appreciate its value. Nathan
Yau recently wrote an insightful comment piece about this pattern.

Extend this discussion further and consider the appeal of fun and of
aesthetic attraction to help stimulate the brain into engaging and
learning with representations of information. This has been proposed
as an important attribute of design for a long time but still exists as
such a divisive issue within the data visualization field.

Whilst I recently remarked that there might be a sense that the tradi‐
tional factions in the field were starting to better appreciate each other,
I feel there is still more visible polarity than harmony. Indeed, arguably
more polarity than even co-existence. This is an indication that the

Walking the Tightrope of Visualization Criticism | 77

field is still evolving but needs to mature, and it is through our critique
where these fault-lines and opinion clashes manifest themselves. Most
of it is valuable and healthy debate, but equally, we need to make sure
it remains reasoned and accessible.

Grown-up Criticism
A key part of the training sessions I deliver is focused on trying to
equip delegates with a more informed sense of how to evaluate a vis‐
ualization piece. It urges them to attempt to understand the process,
the purpose, and the parameters that have surrounded a project. Rath‐
er than drawing conclusions from a superficial “taste” reaction, they
are asked to take a forensic approach to assessing the quality and ef‐
fectiveness of a visualization, peeling through the layers of a visuali‐
zation’s anatomy and putting themselves into the mind of the designer.

This is something we should all try to do before publishing our knee-
jerk conclusions to the world. To empathise with the constraints that
might have existed within the project, the limitations of the data, try
to imagine the brief and the influencing factors the designer had to
contend with. When we view and evaluate a piece, we are looking at
something that has not benefited from infinite time, endless resources,
and limitless capability. Could we have done better ourselves given the
same context?

On a perverse level, I feel this part of the training risks eroding the raw
innocence (without being disrespectful) that enables more casual ob‐
servers to take visualizations and infographics on face value. They are
not cursed by the depth of analysis and variety of lenses through which
they should evaluate a piece.

However, I shouldn’t worry because what always comes across from
the delegates when we do this exercise is the very grounded, realistic,
and practical appreciation of what works and doesn’t work in different
contexts. There is a mature and pragmatic acceptance and appreciation
of the type of limitations, pressures, constraints, and interferences that
might have shaped the resulting design.

Such experiences in my training course have made me think that those
of us in the connoisseur’s cohort are occasionally guilty of assessing
visualization pieces too harshly, too readily, and too rapidly. This was
the essence of Stefanie Posavec’s observation. It’s not so much looking
at the glass being half empty; it’s more akin to seeing the slightest
shortcoming and amplifying the importance of this perceived flaw.

78 | Chapter 5: What to Watch for in Big Data

A recent observation on Twitter from Santiago Ortiz highlights this
idea, characterising the type of critique that often exists about different
visualization methods and approaches.

And here’s the vehicle Ortiz was referring to:

Via the Visual Dictionary.

This observation resonates with a question I have been asked on sev‐
eral occasions by training course delegates. Many express a frustration
in their struggle to understand and identify what makes a perfect vis‐
ualization. By extension, they admit to a difficulty in establishing
clarity in their own convictions about judging what is a right way and
a wrong way to approach a visualization design.

Entering the field, you begin with fundamentally no informed rea‐
soning for appreciation of quality; it is a gut instinct based on the effect
it has on you. Yet, through the influence of reading key articles and
exposure to social media, when you see others expressing a conviction,
you feel obliged to jump off the fence and hurriedly wave a flag, any
flag, of your judgment. It’s not so much a case of following the crowd,
rather more about feeling a need to express an opinion as quickly and
as clearly as everybody else seems to.

Here’s the truth: Developing clarity of your design conviction is diffi‐
cult. If it were purely about taste, it would be easier. That’s why you can
be much more affirmative about your tastes in things like music, art,
or movies. “Did it connect with you?” is a very open but fitting question
that easily allows you to arrive at a Boolean type of response and the
clarity of your judgment.

Walking the Tightrope of Visualization Criticism | 79

I recently wrote an article to discuss the visualizations I like. In this
piece, I talked much less about style, approach, subjects, technique, or
principles, but instead focused on those visualizations that give back
more in return than you put in. That is my conviction, but it has taken
a long while to arrive at that level of clarity. As many others will, I’ve
been through a full discovery cycle of liking things that I now don’t
like and disliking things that I now do.

This conviction is informed by knowledge, by exposure to other dis‐
ciplines and methods, and also through greater appreciation of what
it takes to craft an effective visualization solution that works for the
problem context it is responding to. Fundamentally, this is a hard dis‐
cipline to do well.

Final Thoughts
The balance, fairness, and realism of our criticism needs to improve.

The desire of those active “experts” in the field to influence widespread
effective practice needs to be matched by a greater maturity and sen‐
sitivity in the way we also evaluate the output of this creativity. More‐
over, commentators and critics, myself included, need to develop a
smarter appreciation of the different contexts in which these works are
created.

A creative field, by its very nature, will have many different interpre‐
tations and perspectives, and the resolution and richness of this opin‐
ion is important to safeguard. Of course, promoting a more open-
minded approach to evaluation doesn’t mean to say there should be
no critical analysis. We also need to ensure there isn’t too much dem‐
onstration of the emperor’s new clothes attitude, especially when a
work looks cool or demonstrates impressive technical competence.

There is great importance in having the conviction and confidence to
ask the question “so what?,” to engage in constructive and mature cri‐
tique (for example), and to exhibit a desire to understand and probe
the intention behind all visualisation work. From this, we will all learn
so much more and help create an environment that facilitates encour‐
agement rather than discouragement.

This article likely contains some sweeping generalisations that manage
to over-simplify things, but hopefully they help illustrate the impor‐

80 | Chapter 5: What to Watch for in Big Data

tance of lifting our heads above the noise and seeing what’s actually
going on, who is active in this field, what roles they are taking on, and
the value they are bringing to the whole visualization ecosystem, not
just to the top table.

Fundamentally, what we need to avoid is inadvertently creating bar‐
riers to people trying to enter and develop in this field by creating the
impression that a 1% missed opportunity is more important than the
99% of a design’s features that were a nailed-on success.

I know I will be making a concerted effort to achieve this balance and
fairness in my own analyses.

Walking the Tightrope of Visualization Criticism | 81

CHAPTER 6

Big Data and Health Care

Solving the Wanamaker Problem
for Health Care
By Tim O’Reilly, Julie Steele, Mike Loukides and Colin Hill

The best minds of my generation are thinking about how to make
people click ads.

— Jeff Hammerbacher
early Facebook employee

Work on stuff that matters.
— Tim O’Reilly

In the early days of the 20th century, department store magnate John
Wanamaker famously said, “I know that half of my advertising doesn’t
work. The problem is that I don’t know which half.”

83

The consumer Internet revolution was fueled by a search for the an‐
swer to Wanamaker’s question. Google AdWords and the pay-per-
click model began the transformation of a business in which adver‐
tisers paid for ad impressions into one in which they pay for results.
“Cost per thousand impressions” (CPM) was outperformed by “cost
per click” (CPC), and a new industry was born. It’s important to un‐
derstand why CPC outperformed CPM, though. Superficially, it’s be‐
cause Google was able to track when a user clicked on a link, and was
therefore able to bill based on success. But billing based on success
doesn’t fundamentally change anything unless you can also change the
success rate, and that’s what Google was able to do. By using data to
understand each user’s behavior, Google was able to place advertise‐
ments that an individual was likely to click. They knew “which half ”
of their advertising was more likely to be effective, and didn’t bother
with the rest.

Since then, data and predictive analytics have driven ever deeper in‐
sight into user behavior such that companies like Google, Facebook,
Twitter, and LinkedIn are fundamentally data companies. And data
isn’t just transforming the consumer Internet. It is transforming fi‐
nance, design, and manufacturing — and perhaps most importantly,
health care.

How is data science transforming health care? There are many ways
in which health care is changing, and needs to change. We’re focusing
on one particular issue: the problem Wanamaker described when
talking about his advertising. How do you make sure you’re spending
money effectively? Is it possible to know what will work in advance?

Too often, when doctors order a treatment, whether it’s surgery or an
over-the-counter medication, they are applying a “standard of care”
treatment or some variation that is based on their own intuition, ef‐
fectively hoping for the best. The sad truth of medicine is that we don’t
always understand the relationship between treatments and outcomes.
We have studies to show that various treatments will work more often
than placebos; but, like Wanamaker, we know that much of our med‐
icine doesn’t work for half or our patients, we just don’t know which
half. At least, not in advance. One of data science’s many promises is
that, if we can collect enough data about medical treatments and use
that data effectively, we’ll be able to predict more accurately which
treatments will be effective for which patient, and which treatments
won’t.

84 | Chapter 6: Big Data and Health Care

A better understanding of the relationship between treatments, out‐
comes, and patients will have a huge impact on the practice of medicine
in the United States. Health care is expensive. The U.S. spends over
$2.6 trillion on health care every year, an amount that constitutes a
serious fiscal burden for government, businesses, and our society as a
whole. These costs include over $600 billion of unexplained variations
in treatments: treatments that cause no differences in outcomes, or
even make the patient’s condition worse. We have reached a point at
which our need to understand treatment effectiveness has become vi‐
tal — to the health care system and to the health and sustainability of
the economy overall.

Why do we believe that data science has the potential to revolutionize
health care? After all, the medical industry has had data for genera‐
tions: clinical studies, insurance data, hospital records. But the health
care industry is now awash in data in a way that it has never been
before: from biological data such as gene expression, next-generation
DNA sequence data, proteomics, and metabolomics, to clinical data
and health outcomes data contained in ever more prevalent electronic
health records (EHRs) and longitudinal drug and medical claims. We
have entered a new era in which we can work on massive datasets
effectively, combining data from clinical trials and direct observation
by practicing physicians (the records generated by our $2.6 trillion of
medical expense). When we combine data with the resources needed
to work on the data, we can start asking the important questions, the
Wanamaker questions, about what treatments work and for whom.

The opportunities are huge: for entrepreneurs and data scientists look‐
ing to put their skills to work disrupting a large market, for researchers
trying to make sense out of the flood of data they are now generating,
and for existing companies (including health insurance companies,
biotech, pharmaceutical, and medical device companies, hospitals and
other care providers) that are looking to remake their businesses for
the coming world of outcome-based payment models.

Making Health Care More Effective
What, specifically, does data allow us to do that we couldn’t do before?
For the past 60 or so years of medical history, we’ve treated patients as
some sort of an average. A doctor would diagnose a condition and
recommend a treatment based on what worked for most people, as
reflected in large clinical studies. Over the years, we’ve become more
sophisticated about what that average patient means, but that same

Solving the Wanamaker Problem for Health Care | 85

statistical approach didn’t allow for differences between patients. A
treatment was deemed effective or ineffective, safe or unsafe, based on
double-blind studies that rarely took into account the differences be‐
tween patients. With the data that’s now available, we can go much
further. The exceptions to this are relatively recent and have been do‐
minated by cancer treatments, the first being Herceptin for breast
cancer in women who over-express the Her2 receptor. With the data
that’s now available, we can go much further for a broad range of dis‐
eases and interventions that are not just drugs but include surgery,
disease management programs, medical devices, patient adherence,
and care delivery.

For a long time, we thought that Tamoxifen was roughly 80% effective
for breast cancer patients. But now we know much more: we know that
it’s 100% effective in 70 to 80% of the patients, and ineffective in the
rest. That’s not word games, because we can now use genetic markers
to tell whether it’s likely to be effective or ineffective for any given
patient, and we can tell in advance whether to treat with Tamoxifen or
to try something else.

Two factors lie behind this new approach to medicine: a different way
of using data, and the availability of new kinds of data. It’s not just
stating that the drug is effective on most patients, based on trials (in‐
deed, 80% is an enviable success rate); it’s using artificial intelligence
techniques to divide the patients into groups and then determine the
difference between those groups. We’re not asking whether the drug
is effective; we’re asking a fundamentally different question: “for which
patients is this drug effective?” We’re asking about the patients, not
just the treatments. A drug that’s only effective on 1% of patients might
be very valuable if we can tell who that 1% is, though it would certainly
be rejected by any traditional clinical trial.

More than that, asking questions about patients is only possible be‐
cause we’re using data that wasn’t available until recently: DNA se‐
quencing was only invented in the mid-1970s, and is only now coming
into its own as a medical tool. What we’ve seen with Tamoxifen is as
clear a solution to the Wanamaker problem as you could ask for: we
now know when that treatment will be effective. If you can do the same
thing with millions of cancer patients, you will both improve outcomes
and save money.

Dr. Lukas Wartman, a cancer researcher who was himself diagnosed
with terminal leukemia, was successfully treated with sunitinib, a drug

86 | Chapter 6: Big Data and Health Care

that was only approved for kidney cancer. Sequencing the genes of
both the patient’s healthy cells and cancerous cells led to the discovery
of a protein that was out of control and encouraging the spread of the
cancer. The gene responsible for manufacturing this protein could po‐
tentially be inhibited by the kidney drug, although it had never been
tested for this application. This unorthodox treatment was surpris‐
ingly effective: Wartman is now in remission.

While this treatment was exotic and expensive, what’s important isn’t
the expense but the potential for new kinds of diagnosis. The price of
gene sequencing has been plummeting; it will be a common doctor’s
office procedure in a few years. And through Amazon and Google, you
can now “rent” a cloud-based supercomputing cluster that can solve
huge analytic problems for a few hundred dollars per hour. What is
now exotic inevitably becomes routine.

But even more important: we’re looking at a completely different ap‐
proach to treatment. Rather than a treatment that works 80% of the
time, or even 100% of the time for 80% of the patients, a treatment
might be effective for a small group. It might be entirely specific to the
individual; the next cancer patient may have a different protein that’s
out of control, an entirely different genetic cause for the disease. Treat‐
ments that are specific to one patient don’t exist in medicine as it’s
currently practiced; how could you ever do an FDA trial for a medi‐
cation that’s only going to be used once to treat a certain kind of cancer?

Foundation Medicine is at the forefront of this new era in cancer treat‐
ment. They use next-generation DNA sequencing to discover DNA
sequence mutations and deletions that are currently used in standard
of care treatments, as well as many other actionable mutations that are
tied to drugs for other types of cancer. They are creating a patient-
outcomes repository that will be the fuel for discovering the relation
between mutations and drugs. Foundation has identified DNA muta‐
tions in 50% of cancer cases for which drugs exist (information via a
private communication), but are not currently used in the standard of
care for the patient’s particular cancer.

The ability to do large-scale computing on genetic data gives us the
ability to understand the origins of disease. If we can understand why
an anti-cancer drug is effective (what specific proteins it affects), and
if we can understand what genetic factors are causing the cancer to
spread, then we’re able to use the tools at our disposal much more
effectively. Rather than using imprecise treatments organized around

Solving the Wanamaker Problem for Health Care | 87

symptoms, we’ll be able to target the actual causes of disease, and de‐
sign treatments tuned to the biology of the specific patient. Eventually,
we’ll be able to treat 100% of the patients 100% of the time, precisely
because we realize that each patient presents a unique problem.

Personalized treatment is just one area in which we can solve the Wa‐
namaker problem with data. Hospital admissions are extremely ex‐
pensive. Data can make hospital systems more efficient, and to avoid
preventable complications such as blood clots and hospital re-
admissions. It can also help address the challenge of health care hot-
spotting (a term coined by Atul Gawande): finding people who use an
inordinate amount of health care resources. By looking at data from
hospital visits, Dr. Jeffrey Brenner of Camden, NJ, was able to deter‐
mine that “just one per cent of the hundred thousand people who made
use of Camden’s medical facilities accounted for thirty per cent of its
costs.” Furthermore, many of these people came from only two apart‐
ment buildings. Designing more effective medical care for these pa‐
tients was difficult; it doesn’t fit our health insurance system, the pa‐
tients are often dealing with many serious medical issues (addiction
and obesity are frequent complications), and have trouble trusting
doctors and social workers. It’s counter-intuitive, but spending more
on some patients now results in spending less on them when they
become really sick. While it’s a work in progress, it looks like building
appropriate systems to target these high-risk patients and treat them
before they’re hospitalized will bring significant savings.

Many poor health outcomes are attributable to patients who don’t take
their medications. Eliza, a Boston-based company started by Alexan‐
dra Drane, has pioneered approaches to improve compliance through
interactive communication with patients. Eliza improves patient drug
compliance by tracking which types of reminders work on which types
of people; it’s similar to the way companies like Google target adver‐
tisements to individual consumers. By using data to analyze each pa‐
tient’s behavior, Eliza can generate reminders that are more likely to
be effective. The results aren’t surprising: if patients take their medicine
as prescribed, they are more likely to get better. And if they get better,
they are less likely to require further, more expensive treatment. Again,
we’re using data to solve Wanamaker’s problem in medicine: we’re
spending our resources on what’s effective, on appropriate reminders
that are mostly to get patients to take their medications.

88 | Chapter 6: Big Data and Health Care

More Data, More Sources
The examples we’ve looked at so far have been limited to traditional
sources of medical data: hospitals, research centers, doctor’s offices,
insurers. The Internet has enabled the formation of patient networks
aimed at sharing data. Health social networks now are some of the
largest patient communities. As of November 2011, PatientsLikeMe
has over 120,000 patients in 500 different condition groups; ACOR has
over 100,000 patients in 127 cancer support groups; 23andMe has over
100,000 members in their genomic database; and diabetes health social
network SugarStats has over 10,000 members. These are just the larger
communities, thousands of small communities are created around rare
diseases, or even uncommon experiences with common diseases. All
of these communities are generating data that they voluntarily share
with each other and the world.

Increasingly, what they share is not just anecdotal, but includes an
array of clinical data. For this reason, these groups are being recruited
for large-scale crowdsourced clinical outcomes research.

Thanks to ubiquitous data networking through the mobile network,
we can take several steps further. In the past two or three years, there’s
been a flood of personal fitness devices (such as the Fitbit) for moni‐
toring your personal activity. There are mobile apps for taking your
pulse, and an iPhone attachment for measuring your glucose. There
has been talk of mobile applications that would constantly listen to a
patient’s speech and detect changes that might be the precursor for a
stroke, or would use the accelerometer to report falls. Tanzeem Choud‐
hury has developed an app called Be Well that is intended primarily
for victims of depression, though it can be used by anyone. Be Well
monitors the user’s sleep cycles, the amount of time they spend talking,
and the amount of time they spend walking. The data is scored, and
the app makes appropriate recommendations, based both on the in‐
dividual patient and data collected across all the app’s users.

Continuous monitoring of critical patients in hospitals has been nor‐
mal for years; but we now have the tools to monitor patients constantly,
in their home, at work, wherever they happen to be. And if this sounds
like big brother, at this point most of the patients are willing. We don’t
want to transform our lives into hospital experiences; far from it! But
we can collect and use the data we constantly emit, our “data exhaust,”

Solving the Wanamaker Problem for Health Care | 89

to maintain our health, to become conscious of our behavior, and to
detect oncoming conditions before they become serious. The most
effective medical care is the medical care you avoid because you don’t
need it.

Paying for Results
Once we’re on the road toward more effective health care, we can look
at other ways in which Wanamaker’s problem shows up in the medical
industry. It’s clear that we don’t want to pay for treatments that are
ineffective. Wanamaker wanted to know which part of his advertising
was effective, not just to make better ads, but also so that he wouldn’t
have to buy the advertisements that wouldn’t work. He wanted to pay
for results, not for ad placements. Now that we’re starting to under‐
stand how to make treatment effective, now that we understand that
it’s more than rolling the dice and hoping that a treatment that works
for a typical patient will be effective for you, we can take the next step:
Can we change the underlying incentives in the medical system? Can
we make the system better by paying for results, rather than paying for
procedures?

It’s shocking just how badly the incentives in our current medical sys‐
tem are aligned with outcomes. If you see an orthopedist, you’re likely
to get an MRI, most likely at a facility owned by the orthopedist’s
practice. On one hand, it’s good medicine to know what you’re doing
before you operate. But how often does that MRI result in a different
treatment? How often is the MRI required just because it’s part of the
protocol, when it’s perfectly obvious what the doctor needs to do?
Many men have had PSA tests for prostate cancer; but in most cases,
aggressive treatment of prostate cancer is a bigger risk than the disease
itself. Yet the test itself is a significant profit center. Think again about
Tamoxifen, and about the pharmaceutical company that makes it. In
our current system, what does “100% effective in 80% of the patients”
mean, except for a 20% loss in sales? That’s because the drug company
is paid for the treatment, not for the result; it has no financial interest
in whether any individual patient gets better. (Whether a statistically
significant number of patients has side-effects is a different issue.) And
at the same time, bringing a new drug to market is very expensive, and
might not be worthwhile if it will only be used on the remaining 20%
of the patients. And that’s assuming that one drug, not two, or 20, or
200 will be required to treat the unlucky 20% effectively.

It doesn’t have to be this way.

90 | Chapter 6: Big Data and Health Care

In the U.K., Johnson & Johnson, faced with the possibility of losing
reimbursements for their multiple myeloma drug Velcade, agreed to
refund the money for patients who did not respond to the drug. Several
other pay-for-performance drug deals have followed since, paving the
way for the ultimate transition in pharmaceutical company business
models in which their product is health outcomes instead of pills. Such
a transition would rely more heavily on real-world outcome data (are
patients actually getting better?), rather than controlled clinical trials,
and would use molecular diagnostics to create personalized “treatment
algorithms.” Pharmaceutical companies would also focus more on
drug compliance to ensure health outcomes were being achieved. This
would ultimately align the interests of drug makers with patients, their
providers, and payors.

Similarly, rather than paying for treatments and procedures, can we
pay hospitals and doctors for results? That’s what Accountable Care
Organizations (ACOs) are about. ACOs are a leap forward in business
model design, where the provider shoulders any financial risk. ACOs
represent a new framing of the much maligned HMO approaches from
the ’90s, which did not work. HMOs tried to use statistics to predict
and prevent unneeded care. The ACO model, rather than controlling
doctors with what the data says they “should” do, uses data to measure
how each doctor performs. Doctors are paid for successes, not for the
procedures they administer. The main advantage that the ACO model
has over the HMO model is how good the data is, and how that data
is leveraged. The ACO model aligns incentives with outcomes: a prac‐
tice that owns an MRI facility isn’t incentivized to order MRIs when
they’re not necessary. It is incentivized to use all the data at its disposal
to determine the most effective treatment for the patient, and to follow
through on that treatment with a minimum of unnecessary testing.

When we know which procedures are likely to be successful, we’ll be
in a position where we can pay only for the health care that works.
When we can do that, we’ve solved Wanamaker’s problem for health
care.

Enabling Data
Data science is not optional in health care reform; it is the linchpin of
the whole process. All of the examples we’ve seen, ranging from cancer

Solving the Wanamaker Problem for Health Care | 91

treatment to detecting hot spots where additional intervention will
make hospital admission unnecessary, depend on using data effec‐
tively: taking advantage of new data sources and new analytics tech‐
niques, in addition to the data the medical profession has had all along.

But it’s too simple just to say “we need data.” We’ve had data all along:
handwritten records in manila folders on acres and acres of shelving.
Insurance company records. But it’s all been locked up in silos: insur‐
ance silos, hospital silos, and many, many doctor’s office silos. Data
doesn’t help if it can’t be moved, if data sources can’t be combined.

There are two big issues here. First, a surprising number of medical
records are still either hand-written, or in digital formats that are
scarcely better than hand-written (for example, scanned images of
hand-written records). Getting medical records into a format that’s
computable is a prerequisite for almost any kind of progress. Second,
we need to break down those silos.

Anyone who has worked with data knows that, in any problem, 90%
of the work is getting the data in a form in which it can be used; the
analysis itself is often simple. We need electronic health records: pa‐
tient data in a more-or-less standard form that can be shared effi‐
ciently, data that can be moved from one location to another at the
speed of the Internet. Not all data formats are created equal, and some
are certainly better than others: but at this point, any machine-readable
format, even simple text files, is better than nothing. While there are
currently hundreds of different formats for electronic health records,
the fact that they’re electronic means that they can be converted from
one form into another. Standardizing on a single format would make
things much easier, but just getting the data into some electronic form,
any, is the first step.

Once we have electronic health records, we can link doctor’s offices,
labs, hospitals, and insurers into a data network, so that all patient data
is immediately stored in a data center: every prescription, every pro‐
cedure, and whether that treatment was effective or not. This isn’t some
futuristic dream; it’s technology we have now. Building this network
would be substantially simpler and cheaper than building the networks
and data centers now operated by Google, Facebook, Amazon, Apple,
and many other large technology companies. It’s not even close to
pushing the limits.

Electronic health records enable us to go far beyond the current mech‐
anism of clinical trials. In the past, once a drug has been approved in

92 | Chapter 6: Big Data and Health Care

trials, that’s effectively the end of the story: running more tests to de‐
termine whether it’s effective in practice would be a huge expense. A
physician might get a sense for whether any treatment worked, but
that evidence is essentially anecdotal: it’s easy to believe that something
is effective because that’s what you want to see. And if it’s shared with
other doctors, it’s shared while chatting at a medical convention. But
with electronic health records, it’s possible (and not even terribly ex‐
pensive) to collect documentation from thousands of physicians treat‐
ing millions of patients. We can find out when and where a drug was
prescribed, why, and whether there was a good outcome. We can ask
questions that are never part of clinical trials: is the medication used
in combination with anything else? What other conditions is the pa‐
tient being treated for? We can use machine learning techniques to
discover unexpected combinations of drugs that work well together,
or to predict adverse reactions. We’re no longer limited by clinical tri‐
als; every patient can be part of an ongoing evaluation of whether his
treatment is effective, and under what conditions. Technically, this isn’t
hard. The only difficult part is getting the data to move, getting data
in a form where it’s easily transferred from the doctor’s office to ana‐
lytics centers.

To solve problems of hot-spotting (individual patients or groups of
patients consuming inordinate medical resources) requires a different
combination of information. You can’t locate hot spots if you don’t
have physical addresses. Physical addresses can be geocoded (con‐
verted from addresses to longitude and latitude, which is more useful
for mapping problems) easily enough, once you have them, but you
need access to patient records from all the hospitals operating in the
area under study. And you need access to insurance records to deter‐
mine how much health care patients are requiring, and to evaluate
whether special interventions for these patients are effective. Not only
does this require electronic records, it requires cooperation across dif‐
ferent organizations (breaking down silos), and assurance that the data
won’t be misused (patient privacy). Again, the enabling factor is our
ability to combine data from different sources; once you have the data,
the solutions come easily.

Breaking down silos has a lot to do with aligning incentives. Currently,
hospitals are trying to optimize their income from medical treatments,
while insurance companies are trying to optimize their income by
minimizing payments, and doctors are just trying to keep their heads
above water. There’s little incentive to cooperate. But as financial pres‐

Solving the Wanamaker Problem for Health Care | 93

sures rise, it will become critically important for everyone in the health
care system, from the patient to the insurance executive, to assume
that they are getting the most for their money. While there’s intense
cultural resistance to be overcome (through our experience in data
science, we’ve learned that it’s often difficult to break down silos within
an organization, let alone between organizations), the pressure of de‐
livering more effective health care for less money will eventually break
the silos down. The old zero-sum game of winners and losers must
end if we’re going to have a medical system that’s effective over the
coming decades.

Data becomes infinitely more powerful when you can mix data from
different sources: many doctor’s offices, hospital admission records,
address databases, and even the rapidly increasing stream of data
coming from personal fitness devices. The challenge isn’t employing
our statistics more carefully, precisely, or guardedly. It’s about letting
go of an old paradigm that starts by assuming only certain variables
are key and ends by correlating only these variables. This paradigm
worked well when data was scarce, but if you think about, these as‐
sumptions arise precisely because data is scarce. We didn’t study the
relationship between leukemia and kidney cancers because that would
require asking a huge set of questions that would require collecting a
lot of data; and a connection between leukemia and kidney cancer is
no more likely than a connection between leukemia and flu. But the
existence of data is no longer a problem: we’re collecting the data all
the time. Electronic health records let us move the data around so that
we can assemble a collection of cases that goes far beyond a particular
practice, a particular hospital, a particular study. So now, we can use
machine learning techniques to identify and test all possible hypoth‐
eses, rather than just the small set that intuition might suggest. And
finally, with enough data, we can get beyond correlation to causation:
rather than saying “A and B are correlated,” we’ll be able to say “A causes
B,” and know what to do about it.

Building the Health Care System We Want
The U.S. ranks 37th out of developed economies in life expectancy and
other measures of health, while by far outspending other countries on
per-capita health care costs. We spend 18% of GDP on health care,
while other countries on average spend on the order of 10% of GDP.
We spend a lot of money on treatments that don’t work, because we
have a poor understanding at best of what will and won’t work.

94 | Chapter 6: Big Data and Health Care

Part of the problem is cultural. In a country where even pets can have
hip replacement surgery, it’s hard to imagine not spending every penny
you have to prolong Grandma’s life — or your own. The U.S. is a weal‐
thy nation, and health care is something we choose to spend our money
on. But wealthy or not, nobody wants ineffective treatments. Nobody
wants to roll the dice and hope that their biology is similar enough to
a hypothetical “average” patient. No one wants a “winner take all”
payment system in which the patient is always the loser, paying for
procedures whether or not they are helpful or necessary. Like Wana‐
maker with his advertisements, we want to know what works, and we
want to pay for what works. We want a smarter system where treat‐
ments are designed to be effective on our individual biologies; where
treatments are administered effectively; where our hospitals our used
effectively; and where we pay for outcomes, not for procedures.

We’re on the verge of that new system now. We don’t have it yet, but
we can see it around the corner. Ultra-cheap DNA sequencing in the
doctor’s office, massive inexpensive computing power, the availability
of EHRs to study whether treatments are effective even after the FDA
trials are over, and improved techniques for analyzing data are the tools
that will bring this new system about. The tools are here now; it’s up
to us to put them into use.

Recommended Reading
We recommend the following articles and books regarding technology,
data, and health care reform:

• Ahier, Brian. “Big data is the next big thing in health IT,” O’Reilly
Radar. February 27, 2012.

• Bigelow, Bruce. “Big Data, Big Biology, and the ‘Tipping Point’ in
Quantified Health,” Xconomy. April 26, 2012.

• Brawley, Otis Webb. How We Do Harm: A Doctor Breaks Ranks
About Being Sick in America. St. Marten’s Press, 2012.

• Christensen, Clayton M. et al. The Innovator’s Prescription: A Dis‐
ruptive Solution for Health Care. McGraw Hill, 2008.

• Howard, Alex. “Data for the Public Good,” O’Reilly Radar. Feb‐
ruary 22, 2012.

• Manyika, James et al. “Big data: The next frontier for innovation,
competition, and productivity,” McKinsey Global Institute. May,
2011.

Solving the Wanamaker Problem for Health Care | 95

• Oram, Andy. “Five tough lessons I had to learn about health
care,” O’Reilly Radar. March 26, 2012.

• Shah, Nigam H and Jessica D Tenenbaum. “The coming age of
data-driven medicine: translational bioinformatics’ next frontier,”
Journal of the American Medical Informatics Association (JAMIA).
March 26, 2012.

• Trotter, Fred and David Uhlman. Meaningful Use and Beyond.
O’Reilly Media, 2011.

• Wilbanks, John. “Valuing Health Care: Improving Productivity
and Quality” [PDF], Ewing Marion Kauffman Foundation. April,
2012.

Dr. Farzad Mostashari on Building the Health
Information Infrastructure for the Modern
ePatient
By Alex Howard

To learn more about what levers the government is pulling to catalyze
innovation in the healthcare system, I turned to Dr. Farzad Mostashari
(@Farzad_ONC). As the National Coordinator for Health IT, Mosta‐
shari is one of the most important public officials entrusted with im‐
proving the nation’s healthcare system through smarter use of tech‐
nology.

Mostashari, a public-health informatics specialist, was named ONC
chief in April 2011, replacing Dr. David Blumenthal. Mostashari’s full
biography, available at HHS.gov, notes that he “was one of the lead
investigators in the outbreaks of West Nile Virus and anthrax in New
York City, and was among the first developers of real-time electronic
disease surveillance systems nationwide.”

I talked to Mostashari on the same day that he published a look back
over 2011, which he hailed as a year of momentous progress in health
information technology. Our interview follows.

What excites you about your work? What trends matter here?

96 | Chapter 6: Big Data and Health Care

Farzad Mostashari: Well, it’s a really fun job. It feels like this is the ideal
time for this health IT revolution to tie into other massive megatrends
that are happening around consumer and patient empowerment, pay‐
ment and delivery reform, as I talked about in my TED Med Talk with
Aneesh Chopra.

These three streams [how patients are cared for, how care is paid for,
and how people take care of their own health] coming together feels
great. And it really feels like we’re making amazing progress.

How does what’s happening today grow out of the passage of the
Health Information Technology for Economic and Clinical Health
Act (HITECH) Act in 2009?

Farzad Mostashari: HITECH was a key part of ARRA, the American
Recovery and Reinvestment Act. This is the reinvestment part. People
think of roadways and runways and railways. This is the information
infrastructure for healthcare.

In the past two years, we made as much progress on adoption as we
had made in the past 20 years before that. We doubled the adoption of
electronic health records in physician offices between the time the
stimulus passed and now. What that says is that a large number of
barriers have been addressed, including the financial barriers that are
addressed by the health IT incentive payments.

It also, I think, points to the innovation that’s happening in the health
IT marketplace, with more products that people want to buy and want
to use, and an explosion in the number of options people have.

The programs we put in place, like the Regional Health IT Extension
Centers modeled after the Agriculture Extension program, give a
helping hand. There are local nonprofits throughout the country that
are working with one-third of all primary care providers in this coun‐
try to help them adopt electronic health records, particularly smaller
practices and maybe health centers, critical access hospitals, and so
forth.

This is obviously a big lift and a big change for medicine. It moves at
what Jay Walker called “med speed,” not tech speed. The pace of trans‐
formation in medicine that’s happening right now may be unparal‐
leled. It’s a good thing.

Dr. Farzad Mostashari on Building the Health Information Infrastructure for the Modern ePatient
| 97

Healthcare providers have a number of options as they adopt elec‐
tronic health records. How do you think about the choice between
open source versus proprietary options?

Farzad Mostashari: We’re pretty agnostic in terms of the technology
and the business model. What matters are the outcomes. We’ve really
left the decisions about what technology to use to the people who have
to live with it, like the doctors and hospitals who make the purchases.

There are definitely some very successful models, not only on the EHR
side, but also on the health information exchange side.

(Note: For more on this subject, read Brian Ahier’s Radar post on the
Health Internet.)

What role do open standards play in the future of healthcare?

Farzad Mostashari: We are passionate believers in open standards. We
think that everybody should be using them. We’ve gotten really great
participation by vendors of open source and proprietary software, in
terms of participating in an open standards development process.

I think what we’ve enabled, through things like modular certification,
is a lot more innovation. Different pieces of the entire ecosystem could
be done through reducing the barrier to entry, enabling a variety of
different innovative startups to come to the field. What we’re seeing is,
a lot of the time, this is migrating from installed software to web serv‐
ices.

If we’re setting up a reference implementation of the standards, like
the Connect software or popHealth, we do it through a process where
the result is open source. I think the government as a platform ap‐
proach at the Veterans Affairs department, DoD, and so forth is tre‐
mendously important.

How is the mobile revolution changing healthcare?

We had Jay Walker talking about big change [at a recent ONC Grantee
Meeting]. I just have this indelible image of him waving in his left hand
a clay cone with cuneiform on it that is from 2,000 B.C. — 4,000 years
ago — and in his right hand he held his iPhone.

He was saying both of them represented the cutting edge of technology
that evolved to meet consumer need. His strong assertion was that this
is absolutely going to revolutionize what happens in medicine at tech
speed. Again, not “med speed.”

98 | Chapter 6: Big Data and Health Care

I had the experience of being at my clinic, where I get care, and the
pharmacist sitting in the starched, white coat behind the counter tell‐
ing me that I should take this medicine at night.

And I said, “Well, it’s easier for me to take it in the morning.” And he
said, “Well, it works better at night.”

And I asked, acting as an empowered patient, “Well, what’s the half
life?” And he answered, “Okay. Let me look it up.”

He started clacking away at his pharmacy information system; clickity
clack, clickity clack. I can’t see what he’s doing. And then he says, “Ah
hell,” and he pulls out his smartphone and Googles it.

There’s now a democratization of information and information tools,
where we’re pushing the analytics to the cloud. Being able to put that
in the hand of not just every doctor or every healthcare provider but
every patient is absolutely going to be that third strand of the DNA,
putting us on the right path for getting healthcare that results in health.

We’re making sure that people know they have a right to get their own
data, making sure that the policies are aligned with that. We’re making
sure that we make it easy for doctors to give patients their own infor‐
mation through things like the Direct Project, the Blue Button, mean‐
ingful use requirements, or the Consumer E-Health Pledge.

We have more than 250 organizations that collectively hold data for
100 million Americans that pledge to make it easy for people to get
electronic copies of their own data.

Do you think people will take ownership of their personal health
data and engage in what Susannah Fox has described as “peer-to-
peer healthcare”?

Farzad Mostashari: I think that it will be not just possible, not even
just okay, but actually encouraged for patients to be engaged in their
care as partners. Let the epatient help. I think we’re going to see that
emerging as there’s more access and more tools for people to do stuff
with their data once they get it through things like the health data
initiative. We’re also beginning to work with stakeholder groups, like
Consumer’s Union, the American Nurses Association, and some of the
disease groups, to change attitudes around it being okay to ask for your
own records.

This interview was edited and condensed.

Dr. Farzad Mostashari on Building the Health Information Infrastructure for the Modern ePatient
| 99

John Wilbanks Discusses the Risks and
Rewards of a Health Data Commons
By Alex Howard

As I wrote earlier this year in an ebook on data for the public good,
while the idea of data as a currency is still in its infancy, it’s important
to think about where the future is taking us and our personal data.

If the Obama administration’s smart disclosure initiatives gather
steam, more citizens will be able to do more than think about personal
data: they’ll be able to access their financial, health, education, or en‐
ergy data. In the U.S. federal government, the Blue Button initiative,
which initially enabled veterans to download personal health data, is
now spreading to all federal employees, and it also earned adoption at
private institutions like Aetna and Kaiser Permanente. Putting health
data to work stands to benefit hundreds of millions of people. The
Locker Project, which provides people with the ability to move and
store personal data, is another approach to watch.

The promise of more access to personal data, however, is balanced by
accompanying risks. Smartphones, tablets, and flash drives, after all,
are lost or stolen every day. Given the potential of mhealth, and big
data and health care information technology, researchers and policy
makers alike are moving forward with their applications. As they do
so, conversations and rulemaking about health care privacy will need
to take into account not just data collection or retention but context
and use.

Put simply, businesses must confront the ethical issues tied to massive
aggregation and data analysis. Given that context, Fred Trotter’s post
on who owns health data is a crucial read. As Fred highlights, the real
issue is not ownership, per se, but “What rights do patients have re‐
garding health care data that refers to them?”

Would, for instance, those rights include the ability to donate personal
data to a data commons, much in the same way organs are donated
now for research? That question isn’t exactly hypothetical, as the fol‐
lowing interview with John Wilbanks highlights.

Wilbanks, a senior fellow at the Kauffman Foundation and director of
the Consent to Research Project, has been an advocate for open data

100 | Chapter 6: Big Data and Health Care

and open access for years, including a stint at Creative Commons; a
fellowship at the World Wide Web Consortium; and experience in the
academic, business, and legislative worlds. Wilbanks will be speaking
at the Strata Rx Conference in October.

Our interview, lightly edited for content and clarity, follows.

Where did you start your career? Where has it taken you?

John Wilbanks: I got into all of this, in many ways, because I studied
philosophy 20 years ago. What I studied inside of philosophy was se‐
mantics. In the ’90s, that was actually sort of pointless because there
wasn’t much semantic stuff happening computationally.

In the late ’90s, I started playing around with biotech data, mainly
because I was dating a biologist. I was sort of shocked at how the data
was being represented. It wasn’t being represented in a way that was
very semantic, in my opinion. I started a software company and we
ran that for a while, [and then] sold it during the crash.

I went to the World Wide Web Consortium, where I spent a year help‐
ing start their Semantic Web for Life Sciences project. While I was
there, Creative Commons (CC) asked me to come and start their sci‐
ence project because I had known a lot of those guys. When I started
my company, I was at the Berkman Center at Harvard Law School,
and that’s where Creative Commons emerged from, so I knew the
people. I knew the policy and I had gone off and had this bioinfor‐
matics software adventure.

I spent most of the last eight years at CC working on trying to build
different commons in science. We looked at open access to scientific
literature, which is probably where we had the most success because
that’s copyright-centric. We looked at patents. We looked at physical
laboratory materials, like stem cells in mice. We looked at different
legal regimes to share those things. And we looked at data. We looked
at both the technology aspects and legal aspects of sharing data and
making it useful.

A couple of times over those years, we almost pivoted from science to
health because science is so institutional that it’s really hard for any of
the individual players to create sharing systems. It’s not like software,
where anyone with a PC and an Internet connection can contribute to
free software, or Flickr, where anybody with a digital camera can li‐
cense something under CC. Most scientists are actually restricted by
their institutions. They can’t share, even if they want to.

John Wilbanks Discusses the Risks and Rewards of a Health Data Commons | 101

Health kept being interesting because it was the individual patients
who had a motivation to actually create something different than the
system did. At the same time, we were watching and seeing the capacity
of individuals to capture data about themselves exploding. So, at the
same time that the capacity of the system to capture data about you
exploded, your own capacity to capture data exploded.

That, to me, started taking on some of the interesting contours that
make Creative Commons successful, which was that you didn’t need
a large number of people. You didn’t need a very large percentage of
Wikipedia users to create Wikipedia. You didn’t need a large percentage
of free software users to create free software. If this capacity to generate
data about your health was exploding, you didn’t need a very large
percentage of those people to create an awesome data resource: you
needed to create the legal and technical systems for the people who
did choose to share to make that sharing useful.

Since Creative Commons is really a copyright-centric organization, I
left because the power on which you’re going to build a commons of
health data is going to be privacy power, not copyright power. What I
do now is work on informed consent, which is the legal system you
need to work with instead of copyright licenses, as well as the tech‐
nologies that then store, clean, and forward user-generated data to
computational health and computational disease research.

What are the major barriers to people being able to donate their data
in the same way they might donate their organs?

John Wilbanks: Right now, it looks an awful lot like getting onto the
Internet before there was the Web. The big ISPs kind of dominated the
early adopters of computer technologies. You had AOL. You had Com‐
puServe. You had Prodigy. And they didn’t communicate with each
other. You couldn’t send email from AOL to CompuServe.

What you have now depends on the kind of data. If the data that in‐
terests you is your genotype, you’re probably a 23andMe customer and
you’ve got a bunch of your data at 23andMe. If you are the kind of
person who has a chronic illness and likes to share information about
that illness, you’re probably a customer at PatientsLikeMe. But those
two systems don’t interoperate. You can’t send data from one to the
other very effectively or really at all.

102 | Chapter 6: Big Data and Health Care

On top of that, the system has data about you. Your insurance company
has your billing records. Your physician has your medical records. Your
pharmacy has your pharmacy records. And if you do quantified self,
you’ve got your own set of data streams. You’ve got your Fitbit, the data
coming off of your smartphone, and your meal data.

Almost all of these are basically populating different silos. In some
cases, you have the right to download certain pieces of the data. For
the most part, you don’t. It’s really hard for you, as an individual, to
build your own, multidimensional picture of your data, whereas it’s
actually fairly easy for all of those companies to sell your data to one
another. There’s not a lot of technology that lets you share.

What are some of the early signals we’re seeing about data usage
moving into actual regulatory language?

John Wilbanks: The regulatory language actually makes it fairly hard
to do contextual privacy waiving, in a Creative Commons sense. It’s
hard to do granular permissions around privacy in the way you can
do granular conditional copyright grants because you don’t have in‐
tellectual property. The only legal tool you have is a contract, and the
contracts don’t have a lot of teeth.

It’s pretty hard to do anything beyond a gift. It’s more like organ don‐
ation, where you don’t get to decide where the organs go. What I’m
working on is basically a donation, not a conditional gift. The regula‐
tory environment makes it quite hard to do anything besides that.

There was a public comment period that just finished. It’s an an‐
nouncement of proposed rulemaking on what’s called the Common
Rule, which is the Department of Health and Human Services privacy
language. It was looking to re-examine the rules around letting de-
identified data or anonymized data out for widespread use. They got
a bunch of comments.

There’s controversy as to how de-identified data can actually be and
still be useful. There is going to be, probably, a three-to-five year pro‐
cess where they rewrite the Common Rule and it’ll be more modern.
No one knows how modern, but it will be at least more modern when
that finishes.

Then there’s another piece in the U.S. — HIPAA — which creates a
totally separate regime. In some ways, it is the same as the Common

John Wilbanks Discusses the Risks and Rewards of a Health Data Commons | 103

Rule, but not always. I don’t think that’s going to get opened up. The
way HIPAA works is that they have 17 direct identifiers that are labeled
as identifying information. If you strip those out, it’s considered de-
identified.

There’s an 18th bucket, which is anything else that can reasonably
identify people. It’s really hard to hit. Right now, your genome is not
considered to fall under that. I would be willing to bet within a year
or two, it will be.

From a regulatory perspective, you’ve got these overlapping regimes
that don’t quite fit and both of them are moving targets. That creates
a lot of uncertainty from an investment perspective or from an ana‐
lytics perspective.

How are you thinking about a “health data commons,” in terms of
weighing potential risks against potential social good?

John Wilbanks: I think that that’s a personal judgment as to the risk-
benefit decision. Part of the difficulty is that the regulations are very
syntactic — “This is what re-identification is” — whereas the concept
of harm, benefit, or risk is actually something that’s deeply personal.
If you are sick, if you have cancer or a rare disease, you have a very
different idea of what risk is compared to somebody who thinks of him
or herself as healthy.

What we see — and this is born out in the Framingham Heart Study
and all sorts of other longitudinal surveys — is that people’s attitudes
toward risk and benefit change depending on their circumstances.
Their own context really affects what they think is risky and what they
think isn’t risky.

I believe that the early data donors are likely to be people for whom
there isn’t a lot of risk perceived because the health system already
knows that they’re sick. The health system is already denying them
coverage, denying their requests for PET scans, denying their requests
for access to care. That’s based on actuarial tables, not on their personal
data. It’s based on their medical history.

If you’re in that group of people, then the perceived risk is actually
pretty low compared to the idea that your data might actually get used
or to the idea that you’re no longer passive. Even if it’s just a donation,
you’re doing something outside of the system that’s accelerating the
odds of getting something discovered. I think that’s the natural group.

104 | Chapter 6: Big Data and Health Care

If you think back to the numbers of users who are required to create
free software or Wikipedia, to create a cultural commons, a very low
percentage is needed to create a useful resource.

Depending on who you talk to, somewhere between 5-10% of all
Americans either have a rare disease, have it in their first order family,
or have a friend with a rare disease. Each individual disease might not
have very many people suffering from it, but if you net them all up, it’s
a lot of people. Getting several hundred thousand to a few million
people enrolled is not an outrageous idea.

When you look at the existing examples of where such commons
have come together, what have been the most important concrete
positive outcomes for society?

John Wilbanks: I don’t think we have really even started to see them
because most people don’t have computable data about themselves.
Most people, if they have any data about themselves, have scans of
their medical records.

What we really know is that there’s an opportunity cost to not trying,
which is that the existing system is really inefficient, very bad at dis‐
covering drugs, and very bad at getting those drugs to market in a
timely basis.

That’s one of the reasons we’re doing this is as an experiment. We would
like to see exactly how effective big computational approaches are on
health data. The problem is that there are two ways to get there.

One is through a set of monopoly companies coming together and
working together. That’s how semiconductors work. The other is
through an open network approach. There’s not a lot of evidence that
things besides these two approaches work. Government intervention
is probably not going to work.

Obviously, I come down on the open network side. But there’s an im‐
plicit belief, I think, both in the people who are pushing the cooper‐
ating monopolies approach and the people who are pushing the open
networks approach, that there’s enormous power in the big-data-
driven approach. We’re just leaving that on the table right now by not
having enough data aggregated.

The benefits to health that will come out will be the ability to increas‐
ingly, by looking at a multidimensional picture of a person, predict

John Wilbanks Discusses the Risks and Rewards of a Health Data Commons | 105

with some confidence whether or not a drug will work, or whether
they’re going to get sick, or how sick they’re going to get, or what life‐
style changes they can make to mitigate an illness. Right now, basically,
we really don’t know very much.

Esther Dyson on Health Data, “Preemptive
Healthcare,” and the Next Big Thing
By Alex Howard

If we look ahead to the next decade, it’s worth wondering whether the
way we think about health and health care will have shifted. Will health
care technology be a panacea? Will it drive even higher costs, creating
a broader divide between digital haves and have-nots? Will opening
health data empower patients or empower companies?

As ever, there will be good outcomes and bad outcomes, and not just
in the medical sense. There’s a great deal of thought around the po‐
tential for mobile applications right now, from the FDA’s potential de‐
cision to regulate them to a reported high abandonment rate. There
are also significant questions about privacy, patient empowerment,
and meaningful use of electronic health care records.

When I’ve talked to US CTO Todd Park or Dr. Farzad Mostashari
they’ve been excited about the prospect for health data to fuel better
dashboards and algorithms to give frontline caregivers access to crit‐
ical information about people they’re looking after, providing critical
insight at the point of contact.

Kathleen Sebelius, the U.S. Secretary for Health and Human Services,
said at this year’s Health Datapalooza that venture capital investment
in the health care IT area is up 60% since 2009.

Given that context, I was more than a little curious to hear what Esther
Dyson (@edyson) is thinking about when she looks at the intersection
of health care, data, and information technology.

Dyson, who started her career as a journalist, is now an angel investor
and philanthropist. Dyson is a strong supporter of “preemptive health
care” — and she’s putting her money where her interest lies, with her
investments.

Our interview, which was lightly edited for content and clarity, follows.

How do you see health care changing?

106 | Chapter 6: Big Data and Health Care

Dyson: There are multiple perspectives. The one I have does not in‐
validate others, nor it is intended to trump the others, but it’s the one
that I focus on — and that’s “health” as opposed to “health care.”

If you maintain good health, you can avoid health care. That’s one of
those great and unrealizable goals, but it’s realizable in part. Any health
care you can avoid because you’re healthy is valuable.

What I’m mostly focused on is trying to change people’s behavior.
You’ll get agreement from almost everybody that eating right, not
smoking, getting exercise, avoiding too much stress, and sleeping a lot
are good for your health.

The challenge is what makes people do those things, and that’s where
there’s a real lack of data. So a lot of what I’m doing is investing in that
space. There’s evidence-based medicine. There’s also evidence-based
prevention, and that’s even harder to validate.

Right now, a lot of people are doing a lot of different things. Many of
them are collecting data, which over time, with luck, will prove that
some of these things I’m going to talk about are valuable.

What does the landscape for health care products and services look
like to you today?

Dyson: I see three markets.

There’s the traditional health care market, which is what people usu‐
ally talk about. It’s drugs, clinics, hospitals, doctors, therapies, devices,
insurance companies, data processors, or electronic health records.

Then there’s the market for bad health, which people don’t talk about
a lot, at least not in those terms, but it’s huge. It’s the products and all
of the advertising around everything from sugared soft drinks to cig‐
arettes to recreational drugs to things that keep you from going to bed,
going to sleep, keep you on the couch, and keep you immobile. Look
at cigarettes and alcohol: That’s a huge market. People are being en‐
couraged to engage in unhealthy behaviors, whether it’s stuff that
might be healthy in moderation or stuff that just isn’t healthy at all.

The new [third] market for health existed already as health clubs.
What’s exciting is that there’s now an explicit market for things that
are designed to change your behavior. Usually, they’re information-
and social-based. These are the quantified self — analytical tools, tools
for sharing, tools for fostering collaboration or competition with peo‐
ple that behave in a healthy way. Most of those have very little data to

Esther Dyson on Health Data, “Preemptive Healthcare,” and the Next Big Thing | 107

back them up. The business models are still not too clear, because if
I’m healthy, who’s going to pay for that? The chances are that if I’ll pay
for it, I’m already kind of a health nut and don’t need it as much as
someone who isn’t.

Pharma companies will pay for some such things, especially if they
think they can sell people drugs in conjunction with them. I’ll sell you
a cholesterol-lowering drug through a service that encourages you to
exercise, for example. That’s a nice market. You go to the pre-diabetics
and you sell them your statin. Various vendors of sports clubs and so
forth will fund this. But over time, I expect you’re going to see em‐
ployers realize the value of this, then finally, long-term insurance com‐
panies and perhaps government. But it’s a market that operates mostly
on faith at this point.

Speaking of faith, Rock Health shared data that around 80% of mo‐
bile health apps are being abandoned by consumers after two weeks.
Thoughts?

Dyson: To me, that’s infant mortality. The challenge is to take the 20%
and then make those persist. But you’re right, people try a lot of stuff
and it turns out to be confusing and not well-designed, et cetera.

If you look ahead a decade, what are the big barriers for health data
and mobile technology playing a beneficial role, as opposed to a more
dystopian one?

Dyson: Well, the benign version is we’ve done a lot of experimentation.
We’ve discovered that most apps have an 80% abandon rate, but the
20% that are persisting get better and better and better. So the 80% that
are abandoned vanish and the marketplace and the vendors focus on
the 20%. And we get broad adoption. You get onto the subway in New
York and everybody’s thin and healthy.

Yeah, that’s not going to happen. But there’s some impact. Employers
understand the value of this. There’s a lot more to do than just these
[mobile] apps. The employers start serving only healthy food in the
cafeteria. Actually, one big sign is going to be what they serve for
breakfast at Strata RX. I was at the Kauffman Life Sciences Entrepre‐
neur Conference and they had muffins, bagels, and cream cheese.

Carbohydrates and fat, in other words.

108 | Chapter 6: Big Data and Health Care

Dyson: And sugar-filled yogurts. That was the first day. They respon‐
ded to somebody’s tweet [the second day] and it was better. But it’s not
just the advertising. It’s the selection of stuff that you get when you go
to these events or when you go to a hotel or you go to school or you
go to your cafeteria at your office.

Defaults are tremendously important. That’s why I’m a big fan of what
[Michael] Bloomberg is trying to do in New York. If you really want
to buy two servings of soda, that’s fine, but the default serving should
be one. All of this stuff really does have an impact.

Ten years from now, evidence has shown what works. What works is
working because people are doing it. A lot of this is that social norms
have changed. The early adopters have adopted, the late adopters are
being carried along in the wake — just like there are still people who
smoke, but it’s no longer the norm.

Do you have concerns or hopes for the risks and rewards of open
health data releases?

Dyson: If we have a sensible health care system, the data will be helpful.
Hospitals will say, “Oh my God, this guy’s at-risk, let’s prevent him
from getting sick.” Hospitals and the payers will know, “If we let this
guy get sick, it’s going to cost us a lot more in the long run. And we
actually have a business model that operates long-term rather than
simply tries to minimize cost in the short-term.”

And insurance companies will say, “I’m paying for this guy. I better
keep him healthy.” So the most important thing is for us to have a
system that works long-term like that.

What role will personal data ownership play in the health care system
of the future?

Dyson: Well, first we have to define what it is. From my point-of-view,
you own your own data. On the other hand, if you want care, you’ve
got to share it.

I think people are way too paranoid about their data. There will, in‐
evitably, be data spills. We should try to avoid them, but we should also
not encourage paranoia. If you have a rational economic system, pri‐
vacy will be an issue, but financial security will not. Those two have
gotten mingled in people’s minds.

Yes, I may just want to keep it quiet that I have a sexually transmitted
disease, but it’s not going to affect my ability to get treatment or to get

Esther Dyson on Health Data, “Preemptive Healthcare,” and the Next Big Thing | 109

1. Dyson is an investor in 23andMe.

insurance if I’ve got it. On the other hand, if I have to pay a little more
for my diet soda or my hamburger because it’s being taxed, I don’t think
that’s such a bad idea. Not that I want somebody recording how many
hamburgers I eat, just tax them — but you don’t need to tax me per‐
sonally: tax the hamburger.

What about the potential for the quantified self-movement to some‐
day reveal that hamburger consumption to insurers?

Dyson: People are paranoid about insurers, but they’re too busy.
They’re not tracking the hamburgers you eat. They’re insuring popu‐
lations. I went to get insurance and I told Aetna, “You can have my
genetic profile.” And they said, “We wouldn’t know what to do with it.”
I’m not saying that [tracking is] entirely impossible, but I really think
people obsess too much about this kind of stuff.

How should — or could — startups in health care be differentiating
themselves? What are the big problems they could be working on
solving?

Dyson: There’s the whole social aspect. How do you design a game, a
social interaction, that encourages people to react the way you want
them to react? It’s like the difference between Facebook and Friendster.
They both had the same potential user base. One was successful; one
wasn’t. It’s the quality of the analytics you show individuals about their
behavior. It’s the narratives, the tools and the affordances that you give
them for interacting with their friends.

For what it’s worth, of the hundreds of companies that Rock Health or
anybody else will tell you about, probably a third of them will disap‐
pear. One tenth will be highly successful and will acquire the remaining
57%.

What are the health care startup models that interest you? Why?

Dyson: I don’t think there’s a single one. There’s bunches of them oc‐
cupying different places.

One area I really like is user-generated research and experiments. Ob‐
viously, there’s 23andMe.1 Deep analysis of your own data and the
option to share it with other people and with researchers. User-
generated data science research is really fascinating.

110 | Chapter 6: Big Data and Health Care

And then social affordance, like HealthRally, where people interact
with each other. Omada Health — which I’m an investor in — is a Rock
Health company that says we can’t do it all ourselves — there’s a des‐
ignated counselor for a group. Right now it’s focused on pre-diabetics.

I love that, partly because I think it’s going to be effective, and partly
because I really like it as an employment model. I think our country
is too focused on manufacturing and there’s a way to turn more people
into health counselors. I’d take all of the laid off auto workers and turn
them into gym teachers, and all the laid off engineers and turn them
into data scientists or people developing health apps. Or something
like that.

What’s the biggest myth in the health data world? What’s the thing
that drives you up the wall, so to speak?

Dyson: The biggest myth is that any single thing is the solution. The
biggest need is for long-term thinking, which is everything from an
individual thinking long-term about the impact of behavior to a fi‐
nancial institution thinking long-term and having the incentive to
think long-term.

Individuals need to be influenced by psychology. Institutions, and the
individuals in them, are employees that can be motivated or not. As
an institution, they need financial incentives that are aligned with the
long-term rather than the short-term.

That, again, goes back to having a vested interest in the health of people
rather than in the cost of care.

Employers, to some extent, have that already. Your employer wants
you to be healthy. They want you to show up for work, be cheerful,
motivated and well rested. They get a benefit from you being healthy,
far beyond simply avoiding the cost of your care.

Whereas the insurance companies, at this point, simply pass it
through. If the insurance company is too effective, they actually have
to lower their premiums, which is crazy. It’s really not insurance: it’s a
cost-sharing and administration role that the insurance companies
play. That’s something a lot of people don’t get. That needs to be fixed,
one way or another.

Esther Dyson on Health Data, “Preemptive Healthcare,” and the Next Big Thing | 111

A Marriage of Data and Caregivers Gives Dr.
Atul Gawande Hope for Health Care
By Alex Howard

Dr. Atul Gawande (@Atul_Gawande) has been a bard in the health
care world, straddling medicine, academia and the humanities as a
practicing surgeon, medical school professor, best-selling author, and
staff writer at the New Yorker magazine. His long-form narratives and
books have helped illuminate complex systems and wicked problems
to a broad audience.

One recent feature that continues to resonate for those who wish to
apply data to the public good is Gawande’s New Yorker piece “The Hot
Spotters,” where Gawande considered whether health data could help
lower medical costs by giving the neediest patients better care. That
story brings home the challenges of providing health care in a city,
from cultural change to gathering data to applying it.

This summer, after meeting Gawande at the 2012 Health DataPaloo‐
za, I interviewed him about hot spotting, predictive analytics, net‐
worked transparency, health data, feedback loops, and the problems
that technology won’t solve. Our interview, lightly edited for content
and clarity, follows.

Given what you’ve learned in Camden, N.J. — the backdrop for your
piece on hot spotting — do you feel hot spotting is an effective way
for cities and people involved in public health to proceed?

Gawande: The short answer, I think, is “yes.”

Here we have this major problem of both cost and quality — and we
have signs that some of the best places that seem to do the best jobs
can be among the least expensive. How you become one of those places
is a kind of mystery.

It really parallels what happened in the police world. Here is something
that we thought was an impossible problem: crime. Who could pos‐
sibly lower crime? One of the ways we got a handle on it was by di‐
recting policing to the places where there was the most crime. It sounds
kind of obvious, but it was not apparent that crime is concentrated and
that medical costs are concentrated.

112 | Chapter 6: Big Data and Health Care

The second thing I knew but hadn’t put two and two together about is
that the sickest people get the worst care in the system. People with
complex illness just don’t fit into 20-minute office visits.

The work in Camden was emblematic of work happening in pockets
all around the country where you prioritize. As soon as you look at the
system, you see hundreds, thousands of things that don’t work properly
in medicine. But when you prioritize by saying, “For the sickest people
— the 5% who account for half of the spending — let’s look at what
their $100,000 moments are,” you then understand it’s strengthening
primary care and it’s the ability to manage chronic illness.

It’s looking at a few acute high-cost, high-failure areas of care, such as
how heart attacks and congestive heart failure are managed in the sys‐
tem; looking at how renal disease patients are cared for; or looking at
a few things in the commercial population, like back pain, being a huge
source of expense. And then also end-of-life care.

With a few projects, it became more apparent to me that you genuinely
could transform the system. You could begin to move people from
depending on the most expensive places where they get the least care
to places where you actually are helping people achieve goals of care
in the most humane and least wasteful ways possible.

The data analytics office in New York City is doing fascinating pre‐
dictive analytics. That approach could have transformative applica‐
tions in health care, but it’s notable how careful city officials have
been about publishing certain aspects of the data. How do you think
about the relative risks and rewards here, including balancing social
good with the need to protect people’s personal health data?

Gawande: Privacy concerns can sometimes be a barrier, but I haven’t
seen it be the major barrier here. There are privacy concerns in the
data about households as well in the police data.

The reason it works well for the police is not just because you have a
bunch of data geeks who are poking at the data and finding interesting
things. It’s because they’re paired with people who are responsible for
responding to crime, and above all, reducing crime. The commanders
who have the responsibility have a relationship with the people who
have the data. They’re looking at their population saying, “What are
we doing to make the system better?”

That’s what’s been missing in health care. We have not married the
people who have the data with people who feel responsible for ach‐

A Marriage of Data and Caregivers Gives Dr. Atul Gawande Hope for Health Care | 113

ieving better results at lower costs. When you put those people to‐
gether, they’re usually within a system, and within a system, there is
no privacy barrier to being able to look and say, “Here’s what we can
be doing in this health system,” because it’s often that particular.

The beautiful aspect of the work in New York is that it’s not at a terribly
abstract level. Yes, they’re abstracting the data, but they’re also helping
the police understand: “It’s this block that’s the problem. It’s shifted in
the last month into this new sector. The pattern of the crime is that it
looks more like we have a problem with domestic violence. Here are a
few more patterns that might give you a clue about what you can go
in and do.” There’s this give and take about what can be produced and
achieved.

That, to me, is the gold in the health care world — the ability to peer
in and say: “Here are your most expensive patients and your sickest
patients. You didn’t know it, but here, there’s an alcohol and drug ad‐
diction issue. These folks are having car accidents and major trauma
and turning up in the emergency rooms and then being admitted with
$12,000 injuries.”

That’s a system that could be improved and, lo and behold, there’s an
intervention here that’s worked before to slot these folks into treatment
programs, which by and large, we don’t do at all.

That sense of using the data to help you solve problems requires two
things. It requires data geeks and it requires the people in a system who
feel responsible, the way that Bill Bratton made commanders feel re‐
sponsible in the New York police system for the rate of crime. We
haven’t had physicians who felt that they were responsible for 10,000
ICU patients and how well they do on everything from the cost to how
long they spend in the ICU.

Health data is creating opportunities for more transparency into
outcomes, treatments, and performance. As a practicing physician,
do you welcome the additional scrutiny that such collective intelli‐
gence provides, or does it concern you?

Gawande: I think that transparency of our data is crucial. I’m not sure
that I’m with the majority of my colleagues on this. The concerns are
that the data can be inaccurate, that you can overestimate or under‐
estimate the sickness of the people coming in to see you, and that my
patients aren’t like your patients.

114 | Chapter 6: Big Data and Health Care

That said, I have no idea who gets better results at the kinds of oper‐
ations I do and who doesn’t. I do know who has high reputations and
who has low reputations, but it doesn’t necessarily correspond to the
kinds of results they get. As long as we are not willing to open up data
to let people see what the results are, we will never actually learn.

The experience of what happens in fields where the data is open is that
it’s the practitioners themselves that use it. I’ll give a couple of exam‐
ples. Mortality for childbirth in hospitals has been available for a cen‐
tury. It’s been public information, and the practitioners in that field
have used that data to drive the death rates for infants and mothers
down from the biggest killer in people’s lives for women of childbearing
age and for newborns into a rarity.

Another field that has been able to do this is cystic fibrosis. They had
data for 40 years on the performance of the centers around the country
that take care of kids with cystic fibrosis. They shared the data privately.
They did not tell centers how the other centers were doing. They just
told you where you stood relative to everybody else and they didn’t
make that information public. About four or five years ago, they began
making that information public. It’s now available on the Internet. You
can see the rating of every center in the country for cystic fibrosis.

Several of the centers had said, “We’re going to pull out because this
isn’t fair.” Nobody ended up pulling out. They did not lose patients in
hoards and go bankrupt unfairly. They were able to see from one an‐
other who was doing well and then go visit and learn from one and
other.

I can’t tell you how fundamental this is. There needs to be transparency
about our costs and transparency about the kinds of results. It’s murky
data. It’s full of lots of caveats. And yes, there will be the occasional
journalist who will use it incorrectly. People will misinterpret the data.
But the broad result, the net result of having it out there, is so much
better for everybody involved that it far outweighs the value of closing
it up.

U.S. officials are trying to apply health data to improve outcomes,
reduce costs and stimulate economic activity. As you look at the suc‐
cesses and failures of these sorts of health data initiatives, what do
you think is working and why?

A Marriage of Data and Caregivers Gives Dr. Atul Gawande Hope for Health Care | 115

Gawande: I get to watch from the sidelines, and I was lucky to partic‐
ipate in Datapalooza this year. I mostly see that it seems to be following
a mode that’s worked in many other fields, which is that there’s a fun‐
damental role for government to be able to make data available.

When you work in complex systems that involve multiple people who
have to, in health care, deal with patients at different points in time,
no one sees the net result. So, no one has any idea of what the actual
experience is for patients. The open data initiative, I think, has inno‐
vative people grabbing the data and showing what you can do with it.

Connecting the data to the physical world is where the cool stuff starts
to happen. What are the kinds of costs to run the system? How do I
get people to the right place at the right time? I think we’re still in
primitive days, but we’re only two or three years into starting to make
something more than just data on bills available in the system. Even
that wasn’t widely available — and it usually was old data and not very
relevant to this moment in time.

My concern all along is that data needs to be meaningful to both the
patient and the clinician. It needs to be able to connect the abstract
world of data to the physical world of what really happens, which
means it has to be timely data. A six-month turnaround on data is not
great. Part of what has made Wal-Mart powerful, for example, is they
took retail operations from checking their inventory once a month to
checking it once a week and then once a day and then in real-time,
knowing exactly what’s on the shelves and what’s not.

That equivalent is what we’ll have to arrive at if we’re to make our
systems work. Timeliness, I think, is one of the under-recognized but
fundamentally powerful aspects because we sometimes over prioritize
the comprehensiveness of data and then it’s a year old, which doesn’t
make it all that useful. Having data that tells you something that hap‐
pened this week, that’s transformative.

Are you using an iPad at work?

Gawande: I do use the iPad here and there, but it’s not readily part of
the way I can manage the clinic. I would have to put in a lot of effort
for me to make it actually useful in my clinic.

For example, I need to be able to switch between radiology scans and
past records. I predominantly see cancer patients, so they’ll have 40
pages of records that I need to have in front of me, from scans to lab
tests to previous notes by other folks.

116 | Chapter 6: Big Data and Health Care

I haven’t found a better way than paper, honestly. I can flip between
screens on my iPad, but it’s too slow and distracting, and it doesn’t let
me talk to the patient. It’s fun if I can pull up a screen image of this or
that and show it to the patient, but it just isn’t that integrated into
practice.

What problems are immune to technological innovation? What will
need to be changed by behavior?

Gawande: At some level, we’re trying to define what great care is. Great
care means being able to provide optimally knowledgeable care in the
right time and the right way for people and not wasting resources.

Some of it’s crucially aided by information technology that connects
information to where it needs to be so that good decision-making
happens, both by patients and by the clinicians who work with them.

If you’re going to be able to make health care work better, you’ve got
to be able to make that system work better for people, more efficiently
and less wastefully, less harmfully and with much better teamwork. I
think that information technology is a tool in that, but fundamentally
you’re talking about making teams that can go from being disconnec‐
ted cowboys in care to pit crews that actually work together toward
solving a problem.

In a football team or a pit crew, technology is really helpful, but it’s only
a tiny part of what makes that team great. What makes the team great
is that they know what they’re aiming to do, they’re very clear about
their goals, and they are able to make sure they execute every basic
thing that’s crucial for that success.

What do you worry about in this surge of interest in more data-
driven approaches to medicine?

Gawande: I worry the most about a disconnect between the people
who have to use the information and technology and tools, and the
people who make them. We see this in the consumer world. Funda‐
mentally, there is not a single [health] application that is remotely like
my iPod, which is instantly usable. There are a gazillion number of
ways in which information would make a huge amount of difference.

That sense of being able to understand the world of the user, the task
that’s accomplished and the complexity of what they have to do, and

A Marriage of Data and Caregivers Gives Dr. Atul Gawande Hope for Health Care | 117

connecting that to the people making the technology — there just aren’t
that many lines of marriage. In many of the companies that have some
of the dominant systems out there, I don’t see signs that that’s neces‐
sarily going to get any better.

If people gain access to better information about the consequences
of various choices, will that lead to improved outcomes and quality
of life?

Gawande: That’s where the art comes in. There are problems because
you lack information, but when you have information like “you
shouldn’t drink three cans of Coke a day — you’re going to put on
weight,” then having that information is not sufficient for most people.

Understanding what is sufficient to be able to either change the care
or change the behaviors that we’re concerned about is the crux of what
we’re trying to figure out and discover.

When the information is presented in a really interesting way, people
have gradually discovered — for example, having a little ball on your
dashboard that tells you when you’re accelerating too fast and burning
off extra fuel — how that begins to change the actual behavior of the
person in the car.

No amount of presenting the information that you ought to be driving
in a more environmentally friendly way ends up changing anything.
It turns out that change requires the psychological nuance of present‐
ing the information in a way that provokes the desire to actually do it.

We’re at the very beginning of understanding these things. There’s also
the same sorts of issues with clinician behavior — not just information,
but how you are able to foster clinicians to actually talk to one another
and coordinate when five different people are involved in the care of
a patient and they need to get on the same page.

That’s why I’m fascinated by the police work, because you have the
data people, but they’re married to commanders who have responsi‐
bility and feel responsibility for looking out on their populations and
saying, “What do we do to reduce the crime here? Here’s the kind of
information that would really help me.” And the data people come
back to them and say, “Why don’t you try this? I’ll bet this will help
you.”

It’s that give and take that ends up being very powerful.

118 | Chapter 6: Big Data and Health Care

Five Elements of Reform that Health Providers
Would Rather Not Hear About
By Andy Oram

The quantum leap we need in patient care requires a complete overhaul
of record-keeping and health IT. Leaders of the health care field know
this and have been urging the changes on health care providers for
years, but the providers are having trouble accepting the changes for
several reasons.

What’s holding them back? Change certainly costs money, but the in‐
dustry is already groaning its way through enormous paradigm shifts
to meet current financial and regulatory climates, so the money might
as well be directed toward things that work. Training staff to handle
patients differently is also difficult, but the staff on the floor of these
institutions are experiencing burn-out and can be inspired by a new
direction. The fundamental resistance seems to be expectations by
health providers and their vendors about the control they need to
conduct their business profitably.

A few months ago I wrote an article titled “Five Tough Lessons I Had
to Learn About Health Care.” Here I’ll delineate some elements of a
new health care system that are promoted by thought leaders, that echo
the evolution of other industries, that will seem utterly natural in a
couple decades — but that providers are loathe to consider. I feel that
leaders in the field are not confronting that resistance with an equiv‐
alent sense of conviction that these changes are crucial.

1. Reform Will Not Succeed Unless Electronic Records Standardize
on a Common, Robust Format

Records are not static. They must be combined, parsed, and analyzed
to be useful. In the health care field, records must travel with the pa‐
tient. Furthermore, we need an explosion of data analysis applications
in order to drive diagnosis, public health planning, and research into
new treatments.

Interoperability is a common mantra these days in talking about elec‐
tronic health records, but I don’t think the power and urgency of record
formats can be conveyed in eight-syllable words. It can be conveyed
better by a site that uses data about hospital procedures, costs, and
patient satisfaction to help consumers choose a desirable hospital. Or
an app that might prevent a million heart attacks and strokes.

Five Elements of Reform that Health Providers Would Rather Not Hear About | 119

Data-wise (or data-ignorant), doctors are stuck in the 1980s, buying
proprietary record systems that don’t work together even between dif‐
ferent departments in a hospital, or between outpatient clinics and
their affiliated hospitals. Now the vendors are responding to pressures
from both government and the market by promising interoperability.
The federal government has taken this promise as good coin, hoping
that vendors will provide windows onto their data. It never really hap‐
pens. Every baby step toward opening up one field or another requires
additional payments to vendors or consultants.

That’s why exchanging patient data (health information exchange —
HIE) requires a multi-million-dollar investment, year after year, and
why most HIEs go under. And that’s why the HL7 committee, puta‐
tively responsible for defining standards for electronic health records
(EHR), keeps on putting out new, complicated variations on a long
history of formats that were not well-enough defined to ensure com‐
patibility among vendors.

The Direct Project and perhaps the nascent RHEx RESTful exchange
standard will let hospitals exchange the limited types of information
that the government forces them to exchange. But it won’t create a
platform (as suggested in this PDF slideshow) for the hundreds of ap‐
plications we need to extract useful data from records. Nor will it open
the records to the masses of data we need to start collecting. It remains
to be seen whether Accountable Care Organizations (ACO), which are
the latest reform in U.S. health care and are described in this video,
will be able to use current standards to exchange the data that each
member institution needs to coordinate care. Shahid Shaw has laid out
in glorious detail the elements of open data exchange in health care.

2. Reform Will Not Succeed Unless Massive Amounts of Patient Data
Are Collected

We aren’t giving patients the most effective treatments because we just
don’t know enough about what works. This extends throughout the
health care system:

• We can’t prescribe a drug tailored to the patient because we don’t
collect enough data about patients and their reactions to the drug.

• We can’t be sure drugs are safe and effective because we don’t col‐
lect data about how patients fare on those drugs.

• We don’t see a heart attack or other crisis coming because we don’t
track the vital signs of at-risk populations on a daily basis.

120 | Chapter 6: Big Data and Health Care

• We don’t make sure patients follow through on treatment plans
because we don’t track whether they take their medications and
perform their exercises.

• We don’t target people who need treatment because we don’t keep
track of their risk factors.

Some institutions have adopted a holistic approach to health, but as a
society there’s a huge amount more that we could do in this area.

Leaders in the field know what health care providers could accomplish
with data. A recent article even advises policy makers to focus on the
data instead of the electronic records. The question is whether pro‐
viders are technically and organizationally prepped to accept it in such
quantities and variety. When doctors and hospitals think they own the
patients’ records, they resist putting in anything but their own notes
and observations, along with lab results they order. We’ve got to change
the concept of ownership, which strikes deep into their culture.

3. Reform Will Not Succeed Unless Patients Are in Charge of Their
Records

Doctors are currently acting in isolation, occasionally consulting with
the other providers seen by their patients but rarely sharing detailed
information. It falls on the patient, or a family advocate, to remember
that one drug or treatment interferes with another or to remind treat‐
ment centers of follow-up plans. And any data collected by the patient
remains confined to scribbled notes or (in the modern Quantified Self
equivalent) a website that’s disconnected from the official records.

Doctors don’t trust patients. They have some good reasons for this:
medical records are complicated documents in which a slight reword‐
ing or typographical error can change the meaning enough to risk a
life. But walling off patients from records doesn’t insulate them against
errors: on the contrary, patients catch errors entered by staff all the
time. So ultimately it’s better to bring the patient onto the team and
educate her. If a problem with records altered by patients — deliber‐
ately or through accidental misuse — turns up down the line, digital
certificates can be deployed to sign doctor records and output from
devices.

The amounts of data we’re talking about get really big fast. Genomic
information and radiological images, in particular, can occupy dozens
of gigabytes of space. But hospitals are moving to the cloud anyway.

Five Elements of Reform that Health Providers Would Rather Not Hear About | 121

Practice Fusion just announced that they serve 150,000 medical prac‐
titioners and that “One in four doctors selecting an EHR today chooses
Practice Fusion.” So we can just hand over the keys to the patients and
storage will grow along with need.

The movement for patient empowerment will take off, as experts in
health reform told U.S. government representatives, when patients are
in charge of their records. To treat people, doctors will have to ask for
the records, and the patients can offer the full range of treatment his‐
tories, vital signs, and observations of daily living they’ve collected.
Applications will arise that can search the data for patterns and rele‐
vant facts.

Once again, the U.S. government is trying to stimulate patient em‐
powerment by requiring doctors to open their records to patients. But
most institutions meet the formal requirements by providing portals
that patients can log into, the way we can view flight reservations on
airlines. We need the patients to become the pilots. We also need to
give them the information they need to navigate.

4. Reform Will Not Succeed Unless Providers Conform to Practice
Guidelines

Now that the government is forcing doctors to release information
about outcomes, patients can start to choose doctors and hospitals that
offer the best chances of success. The providers will have to apply more
rigor to their activities, using checklists and more, to bring up the
scores of the less successful providers. Medicine is both a science and
an art, but many lag on the science — that is, doing what has been
statistically proven to produce the best likely outcome — even at pres‐
tigious institutions.

Patient choice is restricted by arbitrary insurance rules, unfortunately.
These also contribute to the utterly crazy difficulty determining what
a medical procedure will cost as reported by e-Patient Dave and WBUR
radio. Straightening out this problem goes way beyond the doctors and
hospitals, and settling on a fair, predictable cost structure will benefit
them almost as much as patients and taxpayers. Even some insurers
have started to see that the system is reaching a dead-end and they are
erecting new payment mechanisms.

5. Reform Will Not Succeed Unless Providers and Patients Can Form
Partnerships

122 | Chapter 6: Big Data and Health Care

I’m always talking about technologies and data in my articles, but none
of that constitutes health. Just as student testing is a poor model for
education, data collection is a poor model for medical care. What pa‐
tients want is time to talk intensively with their providers about their
needs, and providers voice the same desires.

Data and good record keeping can help us use our resources more
efficiently and deal with the physician shortage, partly by spreading
out jobs among other clinical staff. Computer systems can’t deal with
complex and overlapping syndromes, or persuade patients to adopt
practices that are good for them. Relationships will always have to be
in the forefront. Health IT expert Fred Trotter says, “Time is the gas
that makes the relationship go, but the technology should be focused
on fuel efficiency.”

Arien Malec, former contractor for the Office of the National Coor‐
dinator, used to give a speech about the evolution of medical care.
Before the revolution in antibiotics, doctors had few tools to actually
cure patients, but they live with the patients in the same community
and know their needs through and through. As we’ve improved the
science of medicine, we’ve lost that personal connection. Malec argued
that better records could help doctors really know their patients again.
But conversations are necessary too.

Five Elements of Reform that Health Providers Would Rather Not Hear About | 123

  • Copyright
  • Table of Contents
  • Chapter 1. Introduction
  • Chapter 2. Getting Up to Speed with Big Data
    • What Is Big Data?
      • What Does Big Data Look Like?
      • In Practice
    • What Is Apache Hadoop?
      • The Core of Hadoop: MapReduce
      • Hadoop’s Lower Levels: HDFS and MapReduce
      • Improving Programmability: Pig and Hive
      • Improving Data Access: HBase, Sqoop, and Flume
      • Coordination and Workflow: Zookeeper and Oozie
      • Management and Deployment: Ambari and Whirr
      • Machine Learning: Mahout
      • Using Hadoop
    • Why Big Data Is Big: The Digital Nervous System
      • From Exoskeleton to Nervous System
      • Charting the Transition
      • Coming, Ready or Not
  • Chapter 3. Big Data Tools, Techniques, and Strategies
    • Designing Great Data Products
      • Objective-based Data Products
      • The Model Assembly Line: A Case Study of Optimal Decisions Group
      • Drivetrain Approach to Recommender Systems
      • Optimizing Lifetime Customer Value
      • Best Practices from Physical Data Products
      • The Future for Data Products
    • What It Takes to Build Great Machine Learning Products
      • Progress in Machine Learning
      • Interesting Problems Are Never Off the Shelf
      • Defining the Problem
  • Chapter 4. The Application of Big Data
    • Stories over Spreadsheets
      • A Thought on Dashboards
      • Full Interview
    • Mining the Astronomical Literature
      • Interview with Robert Simpson: Behind the Project and What Lies Ahead
      • Science between the Cracks
    • The Dark Side of Data
      • The Digital Publishing Landscape
      • Privacy by Design
  • Chapter 5. What to Watch for in Big Data
    • Big Data Is Our Generation’s Civil Rights Issue, and We Don’t Know It
    • Three Kinds of Big Data
      • Enterprise BI 2.0
      • Civil Engineering
      • Customer Relationship Optimization
      • Headlong into the Trough
    • Automated Science, Deep Data, and the Paradox of Information
      • (Semi)Automated Science
      • Deep Data
      • The Paradox of Information
    • The Chicken and Egg of Big Data Solutions
    • Walking the Tightrope of Visualization Criticism
      • The Visualization Ecosystem
      • The Irrationality of Needs: Fast Food to Fine Dining
      • Grown-up Criticism
      • Final Thoughts
  • Chapter 6. Big Data and Health Care
    • Solving the Wanamaker Problem for Health Care
      • Making Health Care More Effective
      • More Data, More Sources
      • Paying for Results
      • Enabling Data
      • Building the Health Care System We Want
      • Recommended Reading
    • Dr. Farzad Mostashari on Building the Health Information Infrastructure for the Modern ePatient
    • John Wilbanks Discusses the Risks and Rewards of a Health Data Commons
    • Esther Dyson on Health Data, “Preemptive Healthcare,” and the Next Big Thing
    • A Marriage of Data and Caregivers Gives Dr. Atul Gawande Hope for Health Care
    • Five Elements of Reform that Health Providers Would Rather Not Hear About
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