Organizational Behavior and Leadership In the 21st Century sept 20 2024

Abstract

Companies have realized they need to hire data scientists, academic institutions are scrambling to put together data- science programs, and publications are touting data science as a hot—even ‘‘sexy’’—career choice. However, there is confusion about what exactly data science is, and this confusion could lead to disillusionment as the concept diffuses into meaningless buzz. In this article, we argue that there are good reasons why it has been hard to pin down exactly what is data science. One reason is that data science is intricately intertwined with other important concepts also of growing importance, such as big data and data-driven decision making. Another reason is the natural tendency to associate what a practitioner does with the definition of the practitioner’s field; this can result in overlooking the fundamentals of the field. We believe that trying to define the boundaries of data science precisely is not of the utmost importance. We can debate the boundaries of the field in an academic setting, but in order for data science to serve business effectively, it is important (i) to understand its relationships to other important related concepts, and (ii) to begin to identify the fundamental principles underlying data science. Once we embrace (ii), we can much better understand and explain exactly what data science has to offer. Furthermore, only once we embrace (ii) should we be comfortable calling it data science. In this article, we present a perspective that addresses all these concepts. We close by offering, as examples, a partial list of fundamental principles underlying data science.

Introduction

With vast amounts of data now available, companies in

almost every industry are focused on exploiting data for

competitive advantage. The volume and variety of data have

far outstripped the capacity of manual analysis, and in some

cases have exceeded the capacity of conventional databases.

At the same time, computers have become far more powerful,

networking is ubiquitous, and algorithms have been devel-

oped that can connect datasets to enable broader and deeper

analyses than previously possible. The convergence of these

phenomena has given rise to the increasingly widespread

business application of data science.

Companies across industries have realized that they need to

hire more data scientists. Academic institutions are scram-

bling to put together programs to train data scientists. Pub-

lications are touting data science as a hot career choice and

even ‘‘sexy.’’1 However, there is confusion about what exactly

is data science, and this confusion could well lead to

1Leonard N. Stern School of Business, New York University, New York, New York. 2Data Scientists, LLC, New York, New York and Mountain View, California.

ª Foster Provost and Tom Fawcett 2013; Published by Mary Ann Liebert, Inc. This article is available under the Creative Commons License CC-BY-NC (http://creativecommons.org/

licenses/by-nc/4.0). This license permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. Permission only needs to be

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DATA SCIENCE AND ITS

RELATIONSHIP TO BIG DATA AND

DATA-DRIVEN DECISION MAKING

Foster Provost1 and Tom Fawcett2

ORIGINAL ARTICLE

DOI: 10.1089/big.2013.1508 � MARY ANN LIEBERT, INC. � VOL. 1 NO. 1 � MARCH 2013 BIG DATA BD51

disillusionment as the concept diffuses into meaningless buzz.

In this article, we argue that there are good reasons why it has

been hard to pin down what exactly is data science. One

reason is that data science is intricately intertwined with other

important concepts, like big data and data-driven decision

making, which are also growing in importance and attention.

Another reason is the natural tendency, in the absence of

academic programs to teach one otherwise, to associate what

a practitioner actually does with the definition of the prac-

titioner’s field; this can result in overlooking the fundamen-

tals of the field.

At the moment, trying to define the boundaries of data sci-

ence precisely is not of foremost importance. Data-science

academic programs are being developed, and in an academic

setting we can debate its boundaries. However, in order for

data science to serve business effectively, it is important (i) to

understand its relationships to these other important and

closely related concepts, and (ii) to

begin to understand what are the

fundamental principles underlying

data science. Once we embrace

(ii), we can much better under-

stand and explain exactly what

data science has to offer. Further-

more, only once we embrace (ii)

should we be comfortable calling it data science.

In this article, we present a perspective that addresses all these

concepts. We first work to disentangle this set of closely in-

terrelated concepts. In the process, we highlight data science

as the connective tissue between data-processing technologies

(including those for ‘‘big data’’) and data-driven decision

making. We discuss the complicated issue of data science as a

field versus data science as a profession. Finally, we offer as

examples a list of some fundamental principles underlying

data science.

Data Science

At a high level, data science is a set of fundamental principles

that support and guide the principled extraction of infor-

mation and knowledge from data. Possibly the most closely

related concept to data science is data mining—the actual

extraction of knowledge from data via technologies that in-

corporate these principles. There are hundreds of different

data-mining algorithms, and a great deal of detail to the

methods of the field. We argue that underlying all these many

details is a much smaller and more concise set of fundamental

principles.

These principles and techniques are applied broadly across

functional areas in business. Probably the broadest business

applications are in marketing for tasks such as targeted

marketing, online advertising, and recommendations for

cross-selling. Data science also is applied for general customer

relationship management to analyze customer behavior in

order to manage attrition and maximize expected customer

value. The finance industry uses data science for credit scoring

and trading and in operations via fraud detection and work-

force management. Major retailers from Wal-Mart to Amazon

apply data science throughout their businesses, from mar-

keting to supply-chain management. Many firms have differ-

entiated themselves strategically with data science, sometimes

to the point of evolving into data-mining companies.

But data science involves much more than just data-mining

algorithms. Successful data scientists must be able to view

business problems from a data perspective. There is a fun-

damental structure to data-analytic thinking, and basic

principles that should be understood. Data science draws

from many ‘‘traditional’’ fields of study. Fundamental prin-

ciples of causal analysis must be understood. A large portion

of what has traditionally been studied within the field of

statistics is fundamental to data

science. Methods and methology

for visualizing data are vital. There

are also particular areas where

intuition, creativity, common

sense, and knowledge of a partic-

ular application must be brought

to bear. A data-science perspective

provides practitioners with structure and principles, which

give the data scientist a framework to systematically treat

problems of extracting useful knowledge from data.

Data Science in Action

For concreteness, let’s look at two brief case studies of ana-

lyzing data to extract predictive patterns. These studies il-

lustrate different sorts of applications of data science. The

first was reported in the New York Times:

Hurricane Frances was on its way, barreling across

the Caribbean, threatening a direct hit on Florida’s

Atlantic coast. Residents made for higher ground,

but far away, in Bentonville, Ark., executives at Wal-

Mart Stores decided that the situation offered a great

opportunity for one of their newest data-driven

weapons.predictive technology.

A week ahead of the storm’s landfall, Linda M.

Dillman, Wal-Mart’s chief information officer,

pressed her staff to come up with forecasts based on

what had happened when Hurricane Charley struck

several weeks earlier. Backed by the trillions of bytes’

worth of shopper history that is stored in Wal-

Mart’s data warehouse, she felt that the company

could ‘‘start predicting what’s going to happen, in-

stead of waiting for it to happen,’’ as she put it.2

Consider why data-driven prediction might be useful in this

scenario. It might be useful to predict that people in the path

‘‘PUBLICATIONS ARE TOUTING DATA SCIENCE AS A HOT CAREER

CHOICE AND EVEN ‘SEXY.’’’

DATA SCIENCE AND BIG DATA Provost and Fawcett

52BD BIG DATA MARCH 2013

of the hurricane would buy more bottled water. Maybe, but it

seems a bit obvious, and why do we need data science to

discover this? It might be useful to project the amount of

increase in sales due to the hurricane, to ensure that local

Wal-Marts are properly stocked. Perhaps mining the data

could reveal that a particular DVD sold out in the hurricane’s

path—but maybe it sold out that week at Wal-Marts across

the country, not just where the hurricane landing was im-

minent. The prediction could be somewhat useful, but

probably more general than Ms. Dillman was intending.

It would be more valuable to discover patterns due to the

hurricane that were not obvious. To do this, analysts might

examine the huge volume of Wal-Mart data from prior,

similar situations (such as Hurricane Charley earlier in the

same season) to identify unusual local demand for products.

From such patterns, the company might be able to anticipate

unusual demand for products and

rush stock to the stores ahead of

the hurricane’s landfall.

Indeed, that is what happened.

The New York Times reported

that: ‘‘. the experts mined the

data and found that the stores

would indeed need certain prod-

ucts—and not just the usual

flashlights. ‘We didn’t know in the

past that strawberry Pop-Tarts

increase in sales, like seven times their normal sales rate,

ahead of a hurricane,’ Ms. Dillman said in a recent interview.’

And the pre-hurricane top-selling item was beer.*’’’2

Consider a second, more typical business scenario and how it

might be treated from a data perspective. Assume you just

landed a great analytical job with MegaTelCo, one of the

largest telecommunication firms in the United States. They

are having a major problem with customer retention in their

wireless business. In the mid-Atlantic region, 20% of cell-

phone customers leave when their contracts expire, and it is

getting increasingly difficult to acquire new customers. Since

the cell-phone market is now saturated, the huge growth in

the wireless market has tapered off. Communications com-

panies are now engaged in battles to attract each other’s

customers while retaining their own. Customers switching

from one company to another is called churn, and it is ex-

pensive all around: one company must spend on incentives to

attract a customer while another company loses revenue

when the customer departs.

You have been called in to help understand the problem and

to devise a solution. Attracting new customers is much more

expensive than retaining existing ones, so a good deal of

marketing budget is allocated to prevent churn. Marketing

has already designed a special retention offer. Your task is to

devise a precise, step-by-step plan for how the data science

team should use MegaTelCo’s vast data resources to decide

which customers should be offered the special retention deal

prior to the expiration of their contracts. Specifically, how

should MegaTelCo decide on the set of customers to target to

best reduce churn for a particular incentive budget? An-

swering this question is much more complicated than it

seems initially.

Data Science and Data-Driven Decision Making

Data science involves principles, processes, and techniques

for understanding phenomena via the (automated) analysis

of data. For the perspective of this article, the ultimate goal of

data science is improving deci-

sion making, as this generally is

of paramount interest to busi-

ness. Figure 1 places data science

in the context of other closely

related and data-related pro-

cesses in the organization. Let’s

start at the top.

Data-driven decision making

(DDD)3 refers to the practice of

basing decisions on the analysis

of data rather than purely on intuition. For example, a

marketer could select advertisements based purely on her

long experience in the field and her eye for what will work.

Or, she could base her selection on the analysis of data re-

garding how consumers react to different ads. She could also

use a combination of these approaches. DDD is not an all-or-

nothing practice, and different firms engage in DDD to

greater or lesser degrees.

The benefits of data-driven decision making have been dem-

onstrated conclusively. Economist Erik Brynjolfsson and his

colleagues from MIT and Penn’s Wharton School recently

conducted a study of how DDD affects firm performance.3

They developed a measure of DDD that rates firms as to how

strongly they use data to make decisions across the company.

They show statistically that the more data-driven a firm is, the

more productive it is—even controlling for a wide range of

possible confounding factors. And the differences are not small:

one standard deviation higher on the DDD scale is associated

with a 4–6% increase in productivity. DDD also is correlated

with higher return on assets, return on equity, asset utilization,

and market value, and the relationship seems to be causal.

Our two example case studies illustrate two different sorts of

decisions: (1) decisions for which ‘‘discoveries’’ need to be

‘‘FROM SUCH PATTERNS, THE COMPANY MIGHT BE ABLE TO

ANTICIPATE UNUSUAL DEMAND FOR PRODUCTS AND RUSH STOCK

TO THE STORES AHEAD OF THE HURRICANE’S LANDFALL.’’

*Of course! What goes better with strawberry Pop-Tarts than a nice cold beer?

Provost and Fawcett

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made within data, and (2) decisions that repeat, especially at

massive scale, and so decision making can benefit from even

small increases in accuracy based on data analysis. The Wal-

Mart example above illustrates a type-1 problem. Linda

Dillman would like to discover knowledge that will help Wal-

Mart prepare for Hurricane Frances’s imminent arrival. Our

churn example illustrates a type-

2 DDD problem. A large tele-

communications company may

have hundreds of millions of

customers, each a candidate for

defection. Tens of millions of

customers have contracts expir-

ing each month, so each one of

them has an increased likelihood of defection in the near

future. If we can improve our ability to estimate, for a given

customer, how profitable it would be for us to focus on her,

we can potentially reap large benefits by applying this ability

to the millions of customers in the population. This same

logic applies to many of the areas where we have seen the

most intense application of data science and data mining:

direct marketing, online advertising, credit scoring, financial

trading, help-desk management, fraud detection, search

ranking, product recommendation, and so on.

The diagram in Figure 1 shows data science supporting data-

driven decision making, but also overlapping with it. This

highlights the fact that, increasingly, business decisions are

being made automatically by computer systems. Different

industries have adopted automatic decision making at dif-

ferent rates. The finance and telecommunications industries

were early adopters. In the 1990s, automated decision making

changed the banking and consumer-credit industries dra-

matically. In the 1990s, banks and telecommunications

companies also implemented massive-scale systems for

managing data-driven fraud con-

trol decisions. As retail systems

were increasingly computerized,

merchandising decisions were

automated. Famous examples in-

clude Harrah’s casinos’ reward

programs and the automated

recommendations of Amazon and

Netflix. Currently we are seeing a revolution in advertising,

due in large part to a huge increase in the amount of time

consumers are spending online and the ability online to make

(literally) split-second advertising decisions.

Data Processing and ‘‘Big Data’’

Despite the impression one might get from the media, there is

a lot to data processing that is not data science. Data engi-

neering and processing are critical to support data-science

activities, as shown in Figure 1, but they are more general and

are useful for much more. Data-processing technologies are

important for many business tasks that do not involve ex-

tracting knowledge or data-driven decision making, such as

efficient transaction processing, modern web system proces-

sing, online advertising campaign management, and others.

‘‘Big data’’ technologies, such as Hadoop, Hbase, CouchDB,

and others have received considerable media attention re-

cently. For this article, we will simply take big data to mean

datasets that are too large for traditional data-processing

systems and that therefore require new technologies. As with

the traditional technologies, big data technologies are used

for many tasks, including data engineering. Occasionally, big

data technologies are actually used for implementing data-

mining techniques, but more often the well-known big data

technologies are used for data processing in support of the

data-mining techniques and other data-science activities, as

represented in Figure 1.

Economist Prasanna Tambe of New York University’s Stern

School has examined the extent to which the utilization of big

data technologies seems to help firms.4 He finds that, after

controlling for various possible confounding factors, the use

of big data technologies correlates with significant additional

productivity growth. Specifically, one standard deviation higher

utilization of big data technologies is associated with 1–3%

higher productivity than the average firm; one standard devi-

ation lower in terms of big data utilization is associated with

1–3% lower productivity. This leads to potentially very large

productivity differences between the firms at the extremes.

FIG. 1. Data science in the context of closely related processes in the organization.

‘‘THE BENEFITS OF DATA-DRIVEN DECISION MAKING HAVE BEEN

DEMONSTRATED CONCLUSIVELY.’’

DATA SCIENCE AND BIG DATA Provost and Fawcett

54BD BIG DATA MARCH 2013

From Big Data 1.0 to Big Data 2.0

One way to think about the state of big data technologies is to

draw an analogy with the business adoption of internet

technologies. In Web 1.0, businesses busied themselves with

getting the basic internet technologies in place so that they

could establish a web presence, build electronic commerce

capability, and improve operating efficiency. We can think of

ourselves as being in the era of Big Data 1.0, with firms

engaged in building capabilities to process large data. These

primarily support their current operations—for example, to

make themselves more efficient.

With Web 1.0, once firms had

incorporated basic technologies

thoroughly (and in the process had

driven down prices) they started to

look further. They began to ask

what the web could do for them,

and how it could improve upon

what they’d always done. This

ushered in the era of Web 2.0, in

which new systems and companies

started to exploit the interactive nature of the web. The

changes brought on by this shift in thinking are extensive and

pervasive; the most obvious are the incorporation of social-

networking components and the rise of the ‘‘voice’’ of the

individual consumer (and citizen).

Similarly, we should expect a Big Data 2.0 phase to follow Big

Data 1.0. Once firms have become capable of processing

massive data in a flexible fashion, they should begin asking:

What can I now do that I couldn’t do before, or do better than I

could do before? This is likely to usher in the golden era of data

science. The principles and techniques of data science will be

applied far more broadly and far more deeply than they are

today.

It is important to note that in the Web-1.0 era, some pre-

cocious companies began applying Web-2.0 ideas far ahead

of the mainstream. Amazon is a prime example, incorpo-

rating the consumer’s ‘‘voice’’ early on in the rating of

products and product reviews (and deeper, in the rating of

reviewers). Similarly, we see some companies already ap-

plying Big Data 2.0. Amazon again is a company at the

forefront, providing data-driven recommendations from

massive data. There are other examples as well. Online ad-

vertisers must process extremely large volumes of data (bil-

lions of ad impressions per day is not unusual) and maintain

a very high throughput (real-time bidding systems make

decisions in tens of milliseconds). We should look to these

and similar industries for signs of advances in big data and

data science that subsequently will be adopted by other

industries.

Data-Analytic Thinking

One of the most critical aspects of data science is the support

of data-analytic thinking. Skill at thinking data-analytically is

important not just for the data scientist but throughout the

organization. For example, managers and line employees in

other functional areas will only get the best from the com-

pany’s data-science resources if they have some basic un-

derstanding of the fundamental principles. Managers in

enterprises without substantial data-science resources should

still understand basic principles in order to engage consul-

tants on an informed basis. Investors in data-science ventures

need to understand the funda-

mental principles in order to as-

sess investment opportunities

accurately. More generally, busi-

nesses increasingly are driven by

data analytics, and there is great

professional advantage in being

able to interact competently

with and within such businesses.

Understanding the fundamental

concepts, and having frameworks

for organizing data-analytic thinking, not only will allow one

to interact competently, but will help to envision opportu-

nities for improving data-driven decision making or to see

data-oriented competitive threats.

Firms in many traditional industries are exploiting new and

existing data resources for competitive advantage. They em-

ploy data-science teams to bring advanced technologies to

bear to increase revenue and to decrease costs. In addition,

many new companies are being developed with data mining

as a key strategic component. Facebook and Twitter, along

with many other ‘‘Digital 100’’ companies,5 have high valu-

ations due primarily to data assets they are committed to

capturing or creating.{ Increasingly, managers need to man-

age data-analytics teams and data-analysis projects, marketers

have to organize and understand data-driven campaigns,

venture capitalists must be able to invest wisely in businesses

with substantial data assets, and business strategists must be

able to devise plans that exploit data.

As a few examples, if a consultant presents a proposal to

exploit a data asset to improve your business, you should be

able to assess whether the proposal makes sense. If a com-

petitor announces a new data partnership, you should rec-

ognize when it may put you at a strategic disadvantage. Or,

let’s say you take a position with a venture firm and your first

project is to assess the potential for investing in an advertising

company. The founders present a convincing argument that

they will realize significant value from a unique body of data

they will collect, and on that basis, are arguing for a sub-

stantially higher valuation. Is this reasonable? With an

‘‘SIMILARLY, WE SHOULD EXPECT A BIG DATA 2.0 PHASE

TO FOLLOW BIG DATA 1.0 . THIS IS LIKELY TO USHER IN THE

GOLDEN ERA OF DATA SCIENCE.’’

{Of course, this is not a new phenomenon. Amazon and Google are well-established companies that obtain tremendous value from their data assets.

Provost and Fawcett

ORIGINAL ARTICLE

MARY ANN LIEBERT, INC. � VOL. 1 NO. 1 � MARCH 2013 BIG DATA BD55

understanding of the fundamentals of data science, you

should be able to devise a few probing questions to determine

whether their valuation arguments are plausible.

On a scale less grand, but probably more common, data-

analytics projects reach into all business units. Employees

throughout these units must interact with the data-science

team. If these employees do not have a fundamental

grounding in the principles of data-analytic thinking, they

will not really understand what is happening in the business.

This lack of understanding is much more damaging in data-

science projects than in other technical projects, because the

data science supports improved

decision making. Data-science

projects require close interaction

between the scientists and the

business people responsible for the

decision making. Firms in which

the business people do not under-

stand what the data scientists are

doing are at a substantial disad-

vantage, because they waste time

and effort or, worse, because they

ultimately make wrong decisions.

A recent article in Harvard Busi-

ness Review concludes: ‘‘For all the breathless promises about

the return on investment in Big Data, however, companies

face a challenge. Investments in analytics can be useless, even

harmful, unless employees can incorporate that data into

complex decision making.’’6

Some Fundamental Concepts of Data Science

There is a set of well-studied, fundamental concepts under-

lying the principled extraction of knowledge from data, with

both theoretical and empirical backing. These fundamental

concepts of data science are drawn from many fields that

study data analytics. Some reflect the relationship between

data science and the business problems to be solved. Some

reflect the sorts of knowledge discoveries that can be made

and are the basis for technical solutions. Others are cau-

tionary and prescriptive. We briefly discuss a few here. This

list is not intended to be exhaustive; detailed discussions even

of the handful below would fill a book.* The important thing

is that we understand these fundamental concepts.

Fundamental concept: Extracting useful knowledge from data

to solve business problems can be treated systematically by fol-

lowing a process with reasonably well-defined stages. The Cross-

Industry Standard Process for Data Mining7 (CRISP-DM) is

one codification of this process. Keeping such a process in

mind can structure our thinking about data analytics prob-

lems. For example, in actual practice one repeatedly sees an-

alytical ‘‘solutions’’ that are not based on careful analysis of

the problem or are not carefully evaluated. Structured think-

ing about analytics emphasizes these often underappreciated

aspects of supporting decision making with data. Such

structured thinking also contrasts critical points at which

human intuition and creativity is necessary versus points at

which high-powered analytical tools can be brought to bear.

Fundamental concept: Evaluating data-science results requires

careful consideration of the context in which they will be used.

Whether knowledge extracted from data will aid in decision

making depends critically on the

application in question. For our

churn-management example, how

exactly are we going to use the

patterns that are extracted from

historical data? More generally,

does the pattern lead to better de-

cisions than some reasonable alter-

native? How well would one have

done by chance? How well would

one do with a smart ‘‘default’’ al-

ternative? Many data science eva-

luation frameworks are based on

this fundamental concept.

Fundamental concept: The relationship between the business

problem and the analytics solution often can be decomposed into

tractable subproblems via the framework of analyzing expected

value. Various tools for mining data exist, but business

problems rarely come neatly prepared for their application.

Breaking the business problem up into components corre-

sponding to estimating probabilities and computing or esti-

mating values, along with a structure for recombining the

components, is broadly useful. We have many specific tools

for estimating probabilities and values from data. For our

churn example, should the value of the customer be taken

into account in addition to the likelihood of leaving? It is

difficult to realistically assess any customer-targeting solution

without phrasing the problem as one of expected value.

Fundamental concept: Information technology can be used to

find informative data items from within a large body of data.

One of the first data-science concepts encountered in busi-

ness-analytics scenarios is the notion of finding correlations.

‘‘Correlation’’ often is used loosely to mean data items that

provide information about other data items—specifically,

known quantities that reduce our uncertainty about un-

known quantities. In our churn example, a quantity of in-

terest is the likelihood that a particular customer will leave

after her contract expires. Before the contract expires, this

would be an unknown quantity. However, there may be

known data items (usage, service history, how many friends

‘‘FACEBOOK AND TWITTER, ALONG WITH MANY OTHER

‘DIGITAL 100’ COMPANIES, HAVE HIGH VALUATIONS DUE

PRIMARILY TO DATA ASSETS THEY ARE COMMITTED TO

CAPTURING OR CREATING.’’

*And they do; see http://data-science-for-biz.com.

DATA SCIENCE AND BIG DATA Provost and Fawcett

56BD BIG DATA MARCH 2013

have canceled contracts) that correlate with our quantity of

interest. This fundamental concept underlies a vast number

of techniques for statistical analysis, predictive modeling, and

other data mining.

Fundamental concept: Entities that are similar with respect to

known features or attributes often are similar with respect to

unknown features or attributes. Computing similarity is one of

the main tools of data science. There are many ways to

compute similarity and more are invented each year.

Fundamental concept: If you look

too hard at a set of data, you will

find something—but it might not

generalize beyond the data you’re

observing. This is referred to as

‘‘overfitting’’ a dataset. Techniques

for mining data can be very pow-

erful, and the need to detect and

avoid overfitting is one of the most important concepts to

grasp when applying data-mining tools to real problems. The

concept of overfitting and its avoidance permeates data sci-

ence processes, algorithms, and evaluation methods.

Fundamental concept: To draw causal conclusions, one must

pay very close attention to the presence of confounding factors,

possibly unseen ones. Often, it is not enough simply to un-

cover correlations in data; we may want to use our models to

guide decisions on how to influence the behavior producing

the data. For our churn problem, we want to intervene and

cause customer retention. All methods for drawing causal

conclusions—from interpreting the coefficients of regression

models to randomized controlled experiments—incorporate

assumptions regarding the presence or absence of con-

founding factors. In applying such methods, it is important

to understand their assumptions clearly in order to under-

stand the scope of any causal claims.

Chemistry Is Not About Test Tubes: Data Science vs. the Work of the Data Scientist

Two additional, related complications combine to make it

more difficult to reach a common understanding of just

what is data science and how it fits with other related

concepts.

First is the dearth of academic programs focusing on data

science. Without academic programs defining the field for

us, we need to define the field for ourselves. However, each

of us sees the field from a different perspective and thereby

forms a different conception. The dearth of academic pro-

grams is largely due to the inertia associated with academia

and the concomitant effort involved in creating new aca-

demic programs—especially ones that span traditional dis-

ciplines. Universities clearly see the need for such programs,

and it is only a matter of time before this first complication

will be resolved. For example, in New York City alone, two

top universities are creating degree programs in data sci-

ence. Columbia University is in the process of creating a

master’s degree program within its new Institute for Data

Sciences and Engineering (and has founded a center focus-

ing on the foundations of data science), and NYU will

commence a master’s degree program in data science in

fall 2013.

The second complication builds

on confusion caused by the first.

Workers tend to associate with

their field the tasks they spend

considerable time on or those

they find challenging or reward-

ing. This is in contrast to the tasks

that differentiate the field from

other fields. Forsythe described this phenomenon in an

ethnographic study of practitioners in artificial intelligence

(AI):

The AI specialists I describe view their professional

work as science (and in some cases engineer-

ing).The scientists’ work and the approach they

take to it make sense in relation to a particular view

of the world that is taken for granted in the

laboratory.Wondering what it means to ‘‘do AI,’’

I have asked many practitioners to describe their own

work. Their answers invariably focus on one or more

of the following: problem solving, writing code, and

building systems.8

Forsythe goes on to explain that the AI practitioners focus on

these three activities even when it is clear that they spend

much time doing other things (even less related specifically to

AI). Importantly, none of these three tasks differentiates AI

from other scientific and engineering fields. Clearly just being

very good at these three things does not an AI scientist make.

And as Forsythe points out, technically the latter two are not

even necessary, as the lab director, a famous AI Scientist, had

not written code or built systems for years. Nonetheless, these

are the tasks the AI scientists saw as defining their work—

they apparently did not explicitly consider the notion of what

makes doing AI different from doing other tasks that involve

problem solving, writing code, and system building. (This is

possibly due to the fact that in AI, there were academic dis-

tinctions to call on.)

Taken together, these two complications cause particular

confusion in data science, because there are few academic

distinctions to fall back on, and moreover, due to the state of

the art in data processing, data scientists tend to spend a

majority of their problem-solving time on data preparation

and processing. The goal of such preparation is either to

‘‘WITHOUT ACADEMIC PROGRAMS DEFINING THE FIELD

FOR US, WE NEED TO DEFINE THE FIELD FOR OURSELVES.’’

Provost and Fawcett

ORIGINAL ARTICLE

MARY ANN LIEBERT, INC. � VOL. 1 NO. 1 � MARCH 2013 BIG DATA BD57

subsequently apply data-science methods or to understand

the results. However, that does not change the fact that the

day-to-day work of a data scientist—especially an entry-level

one—may be largely data processing. This is directly analo-

gous to an entry-level chemist spending the majority of her

time doing technical lab work. If this were all she were trained

to do, she likely would not be rightly called a chemist but

rather a lab technician. Important for being a chemist is that

this work is in support of the application of the science of

chemistry, and hopefully the eventual advancement to jobs

involving more chemistry and less technical work. Similarly

for data science: a chief scientist in a data-science-oriented

company will do much less data processing and more data-

analytics design and interpretation.

At the time of this writing, discussions of data science inev-

itably mention not just the analytical skills but the popular

tools used in such analysis. For example, it is common to see

job advertisements mentioning data-mining techniques

(random forests, support vector machines), specific applica-

tion areas (recommendation systems, ad placement optimi-

zation), alongside popular software tools for processing big

data (SQL, Hadoop, MongoDB). This is natural. The partic-

ular concerns of data science in business are fairly new, and

businesses are still working to figure out how best to address

them. Continuing our analogy, the state of data science may

be likened to that of chemistry in the mid-19th century, when

theories and general principles were being formulated and the

field was largely experimental. Every good chemist had to be a

competent lab technician. Similarly, it is hard to imagine a

working data scientist who is not proficient with certain sorts

of software tools. A firm may be well served by requiring that

their data scientists have skills to access, prepare, and process

data using tools the firm has adopted.

Nevertheless, we emphasize that there is an important reason

to focus here on the general principles of data science. In ten

years’ time, the predominant technologies will likely have

changed or advanced enough that today’s choices would seem

quaint. On the other hand, the general principles of data

science are not so differerent than they were 20 years ago and

likely will change little over the coming decades.

Conclusion

Underlying the extensive collection of techniques for mining

data is a much smaller set of fundamental concepts com-

prising data science. In order for data science to flourish as a

field, rather than to drown in the flood of popular attention,

we must think beyond the algorithms, techniques, and tools

in common use. We must think about the core principles and

concepts that underlie the techniques, and also the systematic

thinking that fosters success in data-driven decision making.

These data science concepts are general and very broadly

applicable.

Success in today’s data-oriented business environment re-

quires being able to think about how these fundamental

concepts apply to particular business problems—to think

data-analytically. This is aided by conceptual frameworks that

themselves are part of data science. For example, the auto-

mated extraction of patterns from data is a process with well-

defined stages. Understanding this process and its stages helps

structure problem solving, makes it more systematic, and

thus less prone to error.

There is strong evidence that business performance can be

improved substantially via data-driven decision making,3 big

data technologies,4 and data-science techniques based on big

data.9,10 Data science supports data-driven decision mak-

ing—and sometimes allows making decisions automatically

at massive scale—and depends upon technologies for ‘‘big

data’’ storage and engineering. However, the principles of

data science are its own and should be considered and dis-

cussed explicitly in order for data science to realize its

potential.

Author Disclosure Statement

F.P. and T.F. are authors of the forthcoming book, Data

Science for Business.

References

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Address correspondence to:

F. Provost

Department of Information, Operations,

and Management Sciences

Leonard N. Stern School of Business

New York University

44 W. 4th Street, 8th Floor

New York, NY 10012

E-mail: [email protected]

Provost and Fawcett

ORIGINAL ARTICLE

MARY ANN LIEBERT, INC. � VOL. 1 NO. 1 � MARCH 2013 BIG DATA BD59

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