The use of the term "Data Science" is becoming increasingly common along with "Big Data." What does Data Science mean? What skills should a "data scientist" possess?
Working paper CeDER-12-01, May 2012
Data Science and Prediction
Professor, Stern School of Business
Director, Center for Digital Economy Research
March 29, 2012
The use of the term "Data Science" is becoming increasingly
common along with "Big Data." What does Data Science mean? Is there something
unique about it? What skills should a "data scientist" possess to be productive
in the emerging digital age characterized by a deluge of data? What are the
implications for business and for scientific inquiry? In this brief monograph I
address these questions from a predictive modeling perspective.
The use of the term "Data Science" is becoming increasingly
common along with "Big Data." What does Data Science mean? Is there something
unique about it? What skills should a "data scientist" possess to be productive
in the emerging digital age characterized by a deluge of data? What are the
implications for scientific inquiry?
The term "Science" implies knowledge gained by systematic study.
According to one definition, it is a systematic enterprise that builds and organizes knowledge in
the form of testable explanations and predictions about the
Data Science might therefore imply a focus around data and by extension, Statistics,
which is a systematic study about the organization, properties, and analysis of
data and their role in inference, including our confidence in such inference.
Why then do we need a new term, when Statistics has been around for centuries?
The fact that we now have huge amounts of data should not in and of itself
justify the need for a new term.
The short answer is that it is different in several ways. First, the raw material, the "data" part of Data Science, is increasingly
heterogeneous and unstructured - text, images, and video, often emanating from
networks with complex relationships among its entities. Figure 1 shows the relative
expected volumes of unstructured and structured data between 2008 and 2015,
projecting a difference of almost 200 pedabytes in 2015 compared to a
difference of 50 pedabytes in 2012. Analysis, including the combination of the
two types of data with requires integration, interpretation, and sense making,
increasingly based on tools from linguistics, sociology, and other disciplines.
Secondly, the proliferation of markup languages, tags, etc. are designed to let
computers interpret data automatically, making them active agents in the
process of sense making. In contrast to early markup languages such as HTML
that were about displaying information for human consumption, the majority of
the data now being generated by computers is for consumption by other
computers. In other words, computers are increasingly doing the background work
for each other. This allows decision making to scale: it is becoming
increasingly common for the computer to be the decision maker, unaided by
humans. The shift from humans towards computers as decision makers raises a
multitude of issues ranging from the costs of incorrect decisions to ethical
and privacy issues. These fall into the domains of business, law, ethics, among
Figure 1: Projected Growth in Unstructured and Structured Data
From an epistemological perspective, the data
explosion makes it productive to visit the age old philosophical debate on the
limits of induction as a scientific method for knowledge discovery.
Specifically, it positions the computer as a credible generator and tester of
hypotheses by ameliorating some of the known errors associated with statistical
induction. Machine learning, which is characterized by statistical induction aimed
at generating robust predictive models, becomes central to Data Science.
From an engineering standpoint, it turns out
that scale matters in that it has rendered the traditional database models
somewhat inadequate for knowledge discovery. Traditional database methods are
not suited for knowledge discovery because they are optimized for fast access
and summarization of data given what the user wants to ask
(i.e. a query), not discovery of patterns in massive swaths of data when
the user does not have a well formulated query. Unlike database querying which
asks "what data satisfy this pattern (query)," discovery is about asking "what patterns
satisfy this data?" Specifically, our concern is in finding interesting and
robust patterns that satisfy the data, where interesting is usually
something unexpected and actionable, and robust means a pattern that is
expected to occur in the future.
What makes an insight actionable? Other than
domain-specific reasons, it is prediction. Specifically, what makes an insight
actionable is its predictive power in that the return distribution associated
with an action can be reliably estimated from past data and therefore acted
upon with a high degree of confidence.
The emphasis on prediction is particularly strong in the
machine learning and "KDD" communities. Unless a learned model is predictive,
it is generally regarded with skepticism. This position on prediction mirrors
the view expressed strongly by the philosopher Karl Popper as being a primary
criterion for evaluating a theory and for scientific progress in general.
that theories that sought only to explain a phenomenon were weak whereas those
that make "bold predictions" that stand the test of time and are not
falsifiable should be taken more seriously. In his well-known treatise on this
subject, Conjectures and Refutations, Popper presented Einstein's theory
of relativity as a "good" one since it made bold predictions that could be
easily falsified. All attempts at falsification on this theory have indeed
failed on this date. In contrast, Popper argued that a theory like Freud's,
which could be "bent" to accommodate almost any scenario is weak in that it
was virtually unfalsifiable.
The emphasis on predictive accuracy implicitly favors
"simple" theories over more complex ones, a point we return to shortly. Data
Science is increasingly about prediction on observations that will occur in the
future. This requires a unique mindset, one that has heretofore seen little
representation in typically academic curricula, in social science literature,
and in commerce.
In the remainder of this short monograph I will discuss the
implications of Data science from a business and research standpoint. I first
talk about the implications for skills - what will people in industry need to
know about and why? How should educators thinking about designing programs that
deliver the skills most efficiently and enjoyably? Finally, what kinds of
decision-making skills will be required in the era of big data and how will
these be different from the past?
The second part of my answer is aimed at research. How can scientists
use the abundance of data and massive computational power to their advantage in
scientific inquiry? How does this new line of thinking complement our
traditional methods of scientific inquiry? How can it augment the way we think
about discovery and innovation?
2. Implications for Business
According to recent report by McKinsey and Company
the volume of
data is growing at a rate of roughly 50% per year. This translates into a
roughly 40-fold increase in ten years. Hundreds of billions of messages are
transmitted on social media daily and millions of videos are uploaded daily
across the Internet. As storage becomes virtually costless, most of this information
is stored because businesses generally associate a positive option value with
data. In other words, since it may turn to be useful in ways not yet
foreseen, why not just keep it? (An indicator of how cheap storage has become
is the fact that it is possible to store the world's current stock of music on
a $500 device!)
The viability of using large amounts of data for decision
making became practical in the 80s. The field of "Data Mining" started to
burgeon in the early 90s as relational database technology matured and business
processes became increasingly automated. Early books on Data Mining
from the 90s described
how various methods from Machine Learning could be applied to a variety of
business problems. There was a corresponding explosion in the available
software tools geared towards leveraging transactional and behavioral data for
purposes of explanation and prediction.
An important lesson learned during the 90s was that machine
learning "works" in the sense that these methods detect subtle structure in
data relatively easily without having to make strong assumptions about
linearity, monotonicity, or parameters of distributions. The downside is that
these methods is that they also pick up the noise in data
and often have
no way of distinguishing between what is signal and what is noise, a point we
return to shortly.
Despite the drawbacks, there is a lot to be said for methods
that don't force us to make assumptions about the nature of the relationship
between variables before we begin our inquiry. This is not a trivial issue.
Most of us are trained to believe that theory must originate in the human mind
based on prior theory and data is then gathered to demonstrate the validity of
the theory. Machine Learning turns this process around. Given a large trove of
data, the computer taunts us by saying "If only you knew what question to ask
me, I would give you some very interesting answers based on the data!"
The reality is that we often don't know what question to
ask. For example, consider a healthcare database of individuals who have been using
the healthcare system for many years, where a group has been diagnosed with
Type 2 diabetes and some subset of this group has developed complications. We
would like to know whether there is any pattern to complications and whether
the probability of complication can be predicted and therefore acted upon.
What could the data from the healthcare system look like?
Essentially, it would consist of "transactions," that is, points of contact over
time of a patient with the healthcare system. The system records service
rendered by a healthcare provider or medication dispensed on a particular date.
Notes and observations could be part of such a record. Figure 2 shows what the
raw data would look like for 5 individuals, where the data are separated into a
"clean period" which captures history prior to diagnosis, the red bar which
represents the "diagnosis" and the "outcome period" which consists of costs and
other outcomes such as the occurrence of complications. Each colored bar in the
clean period represents a medication, showing that the first individual was on
three different medications prior to diagnosis, the second individual was on
two, and the last three were on a single medication. The last two individuals
were the costliest to treat and had complications represented by the downward
pointing red arrows whereas the first three individuals had no complications.
Figure 2: Healthcare Use Database Snippet
It is non-trivial to extract the interesting patterns from a
large temporal database of the type above. For starters, the raw data across
individuals typically needs to be aggregated into some sort of canonical form
before useful patterns can be discovered. For example, suppose we count the
number of prescriptions an individual is on at every point in time without
regard to the specifics of each prescription as one approximation of the
"health" of an individual prior to diagnosis. Another identifier might be the
specific medications involved, where green and blue might be "severe"
From the above data, a "complications database" might be
synthesized from the raw data. This might include demographic information such
as a patient's age and their medical history including a list of current
medications aggregated into a count in which case we get a summary table of the
type below. A learning algorithm, designated by the right facing blue arrow in
Figure 3, could then be applied to discover the pattern shown on the right of
the table. The pattern represents an abstraction of the data. Essentially, this
is the type of question we should ask the database, if only we knew what
Figure 3: Extracting Interesting Patterns of Health Outcomes From Healthcare System Use
Why is the pattern on the right side interesting? To
appreciate this, suppose the overall complication rate in the population is 5%.
In other words, a random sample of the database would on average, contain 5%
complications. Under this scenario, the snippet on the right hand side could be
very interesting since its complication rate is many times greater than the
average. The critical question here is whether this is a pattern that is robust
and hence predictive, that is, likely to hold up on unseen cases in the
future. The issue of determining robustness has been addressed extensively in
the machine learning literature.
If the above table is representative of the larger database,
the box on the right tells us the interesting question to ask our database, namely,
"What is the incidence of complications in Type 2 diabetes for people over 36 who are on more than five medications?"
In terms of
actionability, such a pattern would suggest being extra vigilant about people
with this profile who do not currently have a complication because of their high
susceptibility to complications.
The general point is that when the data are large and
multidimensional, it is practically impossible for us to know a priori that a
query such as the one above is a good one, that is, one that provides a
potentially interesting insight. Suitably designed machine learning help find
such patterns for us. Equally importantly, these patterns must be predictive.
Typically, the emphasis on predictability favors Occam's razor since simpler
models generally have a higher chance of holding up on future observations than
more complex models, all else being equal.
For example, consider the diabetes complication pattern above:
Age > 50 and #Medication > 6 => Complication_rate=100%
A simpler competing model might ignore age altogether,
stating that people over 50 develop complications. The goodness of such a model
would become more apparent when applied to future data. Does simplicity lead to
higher future predictive accuracy in terms of lower false positives and false
negatives? If so, it is favored. The practice of "out of sample" and "out of
time" testing is used to assess the robustness of patterns from a predictive
When predictive accuracy becomes a primary objective, the
computer tends to play a significant role in model building and decision
making. It builds predictive models through an intelligent "generate and test"
process, the end result of which is an assembled model that is the decision
maker. In other words, it takes automates Popper's criterion of predictive
accuracy for evaluating models at a scale that has not been feasible before. It
is notable that the powerhouse organizations of the Internet era which include
Google, and Amazon, and most of the emerging Web 2.0 companies have business
models that hinge on predictive models based on machine learning. Indeed the
first machine that could arguably be considered to pass the Turing test,
namely, IBM's Watson, could not have done so without extensive use of machine
learning in how it interpreted questions. In a game like jeopardy where
understanding the question itself is often a nontrivial task, it is not
practical to tackle this problem through an extensive enumeration of
possibilities. Rather, the solution is to "train" a computer to interpret
questions correctly based on large numbers of examples.
Machine learning skills are fast becoming a necessary skill
set in the marketplace as companies reel under the data deluge and try to build
automated decision systems that hinge on future predictive accuracy. A basic
course in machine learning is an absolute necessity in today's marketplace. In
addition, knowledge of text processing or "text mining" is becoming essential
in light of the explosion of text and other unstructured data in healthcare
systems, social networks, and other forums. Knowledge about markup languages
such as XML and its derivatives is also essential as more and more content becomes
tagged and hence capable of being interpreted automatically by computers.
Knowledge about machine learning must build on more basic skills
which fall into three broad classes. The first is Statistics. This requires a
working knowledge of probability, distributions, hypothesis testing and
multivariate analysis. This knowledge can be acquired in a two or three course
sequence. The last of these topics, multivariate analysis, often overlaps with the
subject of econometrics which is concerned with fitting robust statistical
models to economic data. Unlike machine learning methods which make no or few
assumptions about the functional form of relationships among variables,
multivariate analysis and econometrics by and large focus on estimating
parameters of linear models where the relationship between the dependent and
independent variables is expressed as a linear equality.
The second set of skills for a data scientist comes
from Computer Science and pertains to how data are internally represented and
manipulated by computers. This is a sequence of courses on data structures,
algorithms, and database systems. The well-known textbook "Data Structures +
Algorithms = Programs" expresses the fact that a program is a procedure that
operates on data. Database systems are specialized programs optimized to
access, store, and manipulate data. Together with scripting languages such as
Python and Perl, database systems provide fundamental skills required for
dealing with reasonably sized datasets. For handling very large datasets,
however, standard database systems built on the relational data model has
severe limitations. The recent move towards Hadoop for dealing with enormous
datasets signals a new set of required skills for data scientists.
The final skill set is the most non-standard
and elusive, but probably what differentiates effective data scientists. This
is the ability to formulate problems in a way that results in effective
solutions. Herbert Simon, the famous economist and "father of Artificial
Intelligence" argued that many seemingly different problems are often
"isomorphic" in that they have the identical underlying structure. Simon
demonstrated that many recursive problems, for example, could be expressed as
the standard Towers of Hanoi problem, that is, with identical initial and goal
states and operators. Simon observed these differently stated problems took
very different amounts of time to solve, representing different levels of
difficulty even though they had the identical underlying structure. Simon's larger
point was that is easy to solve seemingly difficult problems if represented creatively.
In a broader sense, formulation expertise involves
the ability to see commonalities across very different problems. For example,
many problems of interest have "unbalanced target classes" usually denoting
that the dependent variable is interesting only a small minority of the time. As
an example, very few people commit fraud in population, very few people develop
diabetes, and very few people respond to marketing offers or promotions. Yet,
these are the cases of interest that we would like to predict. Such problems
pose challenges for models which have to go out on a limb to make such
predictions which are very likely to be wrong unless the model is very good at
discriminating among the classes. Experienced data miners are very familiar
with such problems and at knowing how to formulate problems in a way that give
a system a chance of making correct predictions under conditions where the
priors are stacked heavily against it.
The above represent "core skills" for data
scientists over the next decade. The term "computational thinking" coined
by Seymour Papert
and elaborated by Wing
is similar to the core skills we describe, but also encompasses abstract
thinking about the kinds of problems computers are better at than humans and
vice versa, and its implications. There is a scramble at universities to train
students in the core skills, and electives that are more suited to specific
disciplines. The McKinsey study mentioned earlier projects are roughly 200
thousand additional "deep analytical" positions and 1.5 to 2 million "data
manages" over the next five years.
The projection of almost two million managers is not just
about managing data scientists, but about a fundamental shift in how managerial
decisions are being driven by data. The famous Ed Demming's quote has come to
characterize the new orientation from intuition-based decision making to
fact-based decision making: "in God we trust, everyone else please bring data."
This isn't going to be an easy transition, requiring organizations to focus on
change management. Imagine informing an expert clinician that the success rate
based on his prescribed regimen is 40% or that the costs associated with the
treatment are double the national average with an inferior outcome. Or a chief
economist that the data support a hypotheses at odds with her theory? Or a
veteran marketer that his proven methods no longer work? Or the sports manager
of a professional basketball team that his strategy is flawed against teams
that have a majority of left-handed sluggers. The list goes on. Fundamentally
managers will have to adapt their information gathering and decision making
strategy in this new world.
More generally, we are moving into an era of big data where
for many types of problems, computers are inherently better decision makers
than humans, where "better" could be defined in terms of cost and accuracy.
This shift has already happened in the world of data-intensive finance where
computers now make the majority of investment decisions often in fractions of
seconds as new information becomes available. The same is true in areas of
online advertising where millions of auctions are conducted in milliseconds
every day, air traffic control, routing, and many types of planning tasks that
require scale, speed, and accuracy simultaneously. This trend is likely to accelerate
in the near future.
3. Knowledge Discovery
In his provocative article titled
The End of Theory: The Data Deluge Makes the Scientific Method Obsolete
Chris Anderson drew on the famous
quote by George Box that "All models are wrong, but some are useful," arguing
that with the huge amounts of data now available, we don't need to settle for
wrong models or any models for that matter. Anderson pointed out that
prediction is of paramount importance to businesses, and that data can be used
to let such models emerge using machine learning algorithms, largely unaided by
humans. Anderson points to companies such as Google as symbolizing the triumph
of machine learning over top-down theory development. Google's language
translator doesn't "understand" language, nor do its algorithms know the
contents on webpages. Nor does IBM's Watson "understand" the question it is
asked. There are dozens of lesser known companies that likewise are able to
predict the odds of someone responding to a display ad, etc., without any solid
theory, but rather, based on gobs of data about behaviors of individuals and
the similarities and differences among these behaviors.
Anderson's article set off
a vigorous debate in academic circles. How can one have science and predictive
models without first articulating a theory?
The observation that
"patterns emerge before reasons for them become apparent"
tends to resonate universally among people, particularly in financial markets,
marketing, and even healthcare. If this is true, Box's observation becomes very
relevant: if a problem is non-stationary and a model will be approximate, why
not build the best predictive model based on data available until that time and
just update it periodically? Why bother developing a detailed causal model if
it is poor at prediction and more importantly, likely to get worse over time
due to "concept drift?"
Anderson's point has
particular relevance in the health, social, and earth science in this era of
big data since these areas have generally been characterized by the lack of
solid theory but where we are now seeing huge amounts of data that can serve as
grist for theory building. For illustration, contrast the areas of physics and social
sciences which lie at opposite ends of the spectrum in terms of the predictive
power of their theories. In physics, a theory is expected to be "complete" in
the sense that a relationship among certain variables is intended to explain
the phenomenon completely, with no exceptions. Such a model is expected
to make perfect predictions (subject to measurement error, but not error due to
omitted variables or unintended consequences). In such domains, the explanatory
and predictive models are synonymous. The behavior of a space shuttle, for
example, is completely explained by the causal model that describes the forces
acting on it. This model can also be used to predict what will happen if any of
the inputs change. It is not sufficient to have a model which is 95% sure of
outcomes, and leave the rest to chance. Engineering follows science.
In contrast, the social
sciences are generally characterized by incomplete models that are intended to
be partial approximations of reality, often based on assumptions of human
behavior known to be simplistic. A model that is correct 95% of the time in
this world would be considered very good. Ironically, however, the emphasis in
social science theory development is on proposing theories that embody
causality without serious consideration of their predictive power. When such a theory
claims that "A causes B" data are gathered to confirm whether the relationship
is causal. But its predictive accuracy could be poor because the theory is
incomplete. Indeed, it is not uncommon for two experts in the social sciences
to propose opposite relationships among the variables and to offer
diametrically opposite predictions based on the same sets of facts. Economists,
for example, routinely disagree on both theory and prediction, and are often
wrong in their forecasts.
How could big data put these domains on firmer ground?
Hastie et al enumerate
that errors in prediction come from three sources
The first type is from misspecification of a model. For example, a linear model
that attempts to fit a nonlinear phenomenon will generate an error simply
because the linear model imposes an inappropriate bias on the problem. The
second source of error is from the use of samples for estimating parameters.
The third is due to randomness, even when the model is perfectly specified.
Figure 4: Sources of Error in Predictive Models and Their Mitigation
As illustrated in Figure
4, big data allows us to significantly reduce the first two types of errors.
Large amounts of data allow us to consider richer models than linear or
logistic regressions simply because there is a lot more data to test such
models and compute reliable error bounds. Big data also eliminate the second
type of error as sample estimates become reasonable proxies for the population.
The theoretical limitation
of observational data, regardless of how big it is, is that it is generally
"passive," representing what actually happened in contrast to the multitude of
things that could have happened had the circumstances been different. In
the healthcare example, it is like having observed the use of the healthcare
system passively, and now having the chance of understand it in retrospect and
extract predictive patterns from it. The data do not tell us what could have
happened if some other treatment had been administered to a specific patient or
to an identical patient. In other words, it does not represent a clean
controlled randomized experiment where the researcher is able to establish
controls and measure the differential impact of treatments on matched pairs.
Interestingly however, we
are now in an era where there is increasing possibilities of conducting large
scale randomized experiments on behavior on the Internet and uncover
interesting interactions that are not possible to observe in the laboratory or
through observational data alone. In a recent controlled experiment on
"influence versus homophily" conducted on Facebook via an "app," Aral and
Walker uncovered the determinants of influence on online video games.
Their results include patterns such as "Older men are more influential than
younger men," "people of the same age group have more influence on each other
than from other age groups," etc. These results, which are undoubtedly peculiar
to video games, make us wonder whether influences are different for different
types of products, and more generally that influence is more complicated than
we thought previously, not amenable to simple generalizations like Gladwell's
concept of "super influencers." The last of these assumptions has also been
questioned by Goel et. al
who observe in large scale studies
that influence in networks is perhaps overrated.
More generally, however,
social science theory building is likely to get a big boost from big data and
machine learning. Never before have we been able to observe human behavior at a
degree of granularity we are seeing now with increasing amounts of human
interaction and economic activity being mediated by the Internet. While there
are clearly limitations to the inductive method, the sheer volume of data being
generated not only makes it feasible, but practically speaking, little us with
little as an alternative. We do not mean to imply that the traditional
scientific method is "dead" as claimed by Anderson. To the contrary, it
continues to serve us well. However, we now have a new and powerful method at
our disposal for theory development that was not previously practical due to
the paucity of data. That era is largely over.
4. Concluding Remarks
There is no free lunch. While large amounts of observational
data provide us with unprecedented opportunity to develop predictive models,
they are limited when it comes to explanation
Since it is impossible to run controlled experiments, except by design, we
cannot know the consequences of things that did not transpire. A
limitation of this is that we are limited in our ability to impact the future
through intervention that is possible when the causal mechanisms are well
The second limitation of predictive modeling with causation
is that multiple models that appear different on the surface might represent
the same underlying causal structure, but there is no way to know this. For
example, in the diabetes example, there could be multiple uncorrelated robust
patterns that predict complications. The good thing, however, is that If they
are predictive, they are still useful in that they could suggest multiple
observable conditions that lead to complications that should therefore be
Despite the limitations of observational data, however, the
sheer size of the data allows us to slice and dice the data in many ways
without losing sample size, a limitation that has traditionally hindered our
ability to examine conditional relationships in data even if they were real.
The ability to interpret unstructured data and integrate it with numbers
further increases our ability to extract useful knowledge in real-time and act on
it. Incredibly, HAL isn't just a fantasy now but an imminent reality.
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Dhar's piece is an excellent exposition of the power of prediction and its importance not only for real applications, but also for theory development. While "data scientists" operate in companies and organizations that use predictions directly, academic researchers in the social sciences are much less incentivized to adopt prediction into their research methodology. The main difference between data scientists and social science academic researchers is that the former are focused on specific measurable outcomes (such as "complications"), whereas researchers are focused on "constructs", which are underlying abstractions (such as "well-being"). Mapping such abstractions into measurable data is challenging and creates a serious divide between explanatory and predictive modeling.
Please note that my last sentence should have ended with the phrase "statements out of context".
This is a very good and informative article that presents an excellent review of much of the consensus in the field. Yet, I also believe that it also might be presenting a point of view that is overly pessimistic toward the building of explanatory models based upon observational data.