After studying the textbook chapters, the videos, and the article ‘Creating Business Value with Analytics’ respond to the following: Write an executive summary for this article.

Creating Business Value with Analytics

FA L L 2 0 1 1 V O L . 5 3 N O. 1

R E P R I N T N U M B E R 5 3 1 1 2

David Kiron and Rebecca Shockley

SMR403

This document is authorized for use only by Alma Gonzalez ([email protected]). Copying or posting is an infringement of copyright. Please contact customerse[email protected] or 800-988-0886 for additional copies.

FALL 2011 MIT SLOAN MANAGEMENT REVIEW 57

SEVERAL PROMINENT technology companies recently predicted that a zettabyte of data

will soon be racing about the Internet.1

This raises several important questions, including, just what is a zettabyte? The answer: a nearly

unfathomable quantity of data, roughly equivalent to the information contained in 100 million

Libraries of Congress.

The next big data measure after zettabyte is a yottabyte. It is not named after a Star Wars charac-

ter. Describing the size of a yottabyte makes you sound like a 5-year-old: “You know, it’s a thousand

trillion billion bytes…” It would take billions of years to download a yottabyte file at current high-

speed broadband speeds.

If Internet traffic continues to grow at current rates, we will likely approach the yottabyte mile-

stone before the end of this century.2 At that point or, more likely, long before, we will have to invent

some new words for what comes next. The International Organization for Standardization and the

A proprietary information system helped make Carmax the largest specialty retailer of used cars in the U.S. and the fastest retailer in U.S. history to $1 billion in revenues.

Creating Business Value with Analytics Our new survey suggests that companies experienced in analytics use are increasingly gaining competitive advantage — but their approaches vary. BY DAVID KIRON AND REBECCA SHOCKLEY

THE LEADING QUESTION What kinds of organizations are gaining a competitive advantage from analytics, and how?

FINDINGS There is a widening gap between orga- nizations that are gaining advantage.

Management sup- port for analytics, including sponsors and top-down man- dates, is critical.

Data-oriented cul- tures have three key characteristics that can be developed and refined.

T H E N E W I N T E L L I G E N T E N T E R P R I S E

SLOANREVIEW.MIT.EDU This document is authorized for use only by Alma Gonzalez ([email protected]). Copying or posting is an infringement of copyright. Please contact

customerse[email protected] or 800-988-0886 for additional copies.

58 MIT SLOAN MANAGEMENT REVIEW FALL 2011 SLOANREVIEW.MIT.EDU

T H E N E W I N T E L L I G E N T E N T E R P R I S E

International Electrotechnical Commission — the

official name givers for this sort of thing — have no

words for chunks of data that large.

It is no small problem when words fail to capture

the world’s immensity. When old concepts fail to

keep up with change, traditions and past experience

become inadequate guides for what to do next. When

the normal ties between what is known and what is

wise, between knowledge and practical wisdom tease

apart, a gap emerges and the routes to wisdom shift.

For managers, the pressure is on to find new ap-

proaches to their portion of the zettabyte — to

develop new data-oriented management systems that

make sense of the enormous amount of data their or-

ganizations are generating. The increasing trend

toward the use of analytics in business is driven by the

need — and the ability — to use data to create not

just business value but also competitive advantage.

One sign of this trend? This year, 58% of the

more than 4,500 respondents to a survey conducted

by MIT Sloan Management Review, in partnership

with the IBM Institute for Business Value, said their

companies were gaining competitive value from

analytics — up from just 37% who said that last

year. (See “About the Research” and “Analytics as a

Source of Competitive Advantage.”) However, this

gain comes entirely from those companies that al-

ready use data analytics for more than financial

forecasting, budgeting and supply chain manage-

ment — the baseline for analytics use in today’s

organizations. Companies that are still focused only

on baseline uses of analytics are falling behind.

Using categories we developed in the first year of

our survey, we categorized this year’s survey respon-

dents’ organizations into three levels of reported

analytics prowess: Aspirationals, Experienced and

Transformed.3 Aspirational companies are basic an-

alytics users; they typically rely on analytics for

financial and supply chain management and pri-

marily use spreadsheets and structured, siloed data

that support targeted activities. In addition to these

basic uses, Experienced companies rely on analytics

to guide strategy as well as day-to-day activities in

marketing and operations. This group also has expe-

rience with analytic tools, such as data visualization

and advanced modeling techniques and, in some or-

ganizations, data integration efforts are underway.

Transformed companies, meanwhile, are strong and

sophisticated analytics users. They rely on analytics

in most activities to guide both day-to-day opera-

tions and strategy, and their enterprise data creates

an integrated view of the business — and includes a

growing focus on unstructured data. Transformed

companies typically use a comprehensive portfolio

of tools to support advanced analytic modeling.

In the 2011 survey, the percentage of Experienced

and Transformed organizations reporting competi-

tive advantage from analytics grew substantially,

whereas Aspirationals slipped by 5%. (See: “Who is

Gaining a Competitive Advantage from Analytics,” p.

60.) The existence of a widening gap is only part of

this year’s story. We also took a close look at how

the Experienced companies say they are using ana-

lytics to create competitive advantage and, in the

process, discovered two very different approaches

to analytics. Managers need to understand these

differences to identify what kind of analytics user

their organization is and what they can do to im-

prove their analytics efforts.

The Importance of Organizational Factors Are organizations with more, rather than fewer, re-

sources and capabilities devoted to analyzing their

reserves of data better off, other things being equal?

Common sense says, “Of course!” But what is it

ABOUT THE RESEARCH To deepen our understanding of the challenges and opportunities associated with the use of business analytics, MIT Sloan Management Review, in partnership with the IBM Institute for Business Value, has for the second year in a row conducted a survey to which more than 4,500 business executives, managers and analysts responded from organizations located around the world. This year’s survey saw a 50% increase in the number of respondents, broadening our analysis to include individuals in 122 countries and more than 30 industries. Participating organizations also ranged widely in size. Respondents included MIT alumni and MIT Sloan Management Review sub- scribers, IBM clients and other interested parties.

In addition to these survey results, we interviewed academic experts and subject matter experts from a number of industries and disciplines to understand the practical issues facing organizations today in their use of analytics. Our interviewees’ insights contributed to a richer understanding of the data and the development of recommen- dations that respond to strategic and tactical questions senior executives address as they implement analytics within their organizations. We also drew upon a number of case studies to further illustrate how organizations are using business analytics as a competitive asset.

In this article, the term “analytics” refers to the use of data and related business insights developed through applied analytical disciplines (e.g., statistical, contextual, quantitative, predictive, cognitive and other models) to drive fact-based planning, decisions, execution, management, measurement and learning.

This document is authorized for use only by Alma Gonzalez ([email protected]). Copying or posting is an infringement of copyright. Please contact customerse[email protected] or 800-988-0886 for additional copies.

SLOANREVIEW.MIT.EDU FALL 2011 MIT SLOAN MANAGEMENT REVIEW 59

about these analytics-oriented resources and capa-

bilities that produce value? And what can companies

that do not already have these resources and capabili-

ties do to reap the benefits of analytics? Both questions

have surprising answers.

For one thing, it’s not all about tools or having the

right people to analyze the data. In fact, our research

suggests that organizational factors are important

predictors of whether an organization will be able to

create a competitive advantage with analytics. Ac-

cording to our survey, managers who say their

organizations are most successful with analytics dis-

proportionately describe their companies as having

management support for analytics throughout the

organization, including top-down mandates for ana-

lytics, sponsors and champions; being open to change

and new ideas; having a unified focus on the customer

that is driven by analytics; and using analytics to iden-

tify and address strategic threats to the organization.

In effect, the most advanced users of analytics typi-

cally have a strong data-oriented culture that supports

and guides analytics use. Having the right combina-

tion of tools, data and people, while necessary, is usually

not enough, according to our data. Without strong

cultural commitments, the success of an analytics pro-

gram can be easily shortchanged or derailed.

But this kind of culture doesn’t come easily. Chang-

ing the way people think, interact with one another

and perform their jobs is hard, and much harder than

developing the technology expertise behind analytics

sophistication. Respondents were more than twice

as likely to consider organizational challenges to be

difficult to resolve (44%) rather than easy (21%).

Transformed organizations have found ways to work

through these organizational issues. Less than one-

third of respondents from Transformed organizations

(30%) consider organizational issues to be difficult to

resolve, compared with three 3 of 5 respondents from

Aspirational organizations (60%).

Competitive Analytics Organizations that have moved beyond baseline an-

alytics — Transformed and Experienced users — are

disproportionately using analytics to focus on the

future, on the customer and on increasing efficien-

cies at greater depth and scope than Aspirationals.

Transformed companies tend to have a data-

oriented culture as well as competency in two areas:

information management and analytic expertise.

Both of these competencies require capabilities and

resources beyond what is typically invested in base-

line analytics. Together, a data-oriented culture,

information management and analytic expertise

foster what we call competitive analytics — analyt-

ics that delivers advantage in the marketplace. The

majority of Transformed organizations display a

level of mastery in each of these areas.

What does competitive analytics look like in

practice? Consider the case of CarMax. With $9 bil-

lion in 2011 revenues, CarMax is the largest U.S.

specialty retailer of used cars, and at one time, was the

fastest retailer in U.S. history to reach $1 billion in

revenues.4 How? Although several factors play a role,

including a compelling customer offer — no-haggle

prices and quality guarantees backed by a 125-point

inspection that became an industry benchmark —

and a lucrative financing arm, CarMax’s business

model relies upon a proprietary information system

that captures, analyzes, interprets and disseminates

data about the cars CarMax sells and buys.

CarMax’s data analytics help track “every pur-

chase, number of test drives and credit applications

per car and color preferences in every demographic

and region,” states Katharine W. Kenny, CarMax vice

president of investor relations. Behind the scenes,

CarMax’s proprietary store technology provides its

management with real-time information about

every aspect of store operations, such as inventory

management, pricing, vehicle transfers, wholesale

auctions and sales consultant productivity. This ad-

vanced inventory management system provides the

company with the ability to anticipate future inven-

tory needs and manage pricing. CarMax continues

to enhance and refine its information systems, which

it believes to be a core competitive advantage.

ANALYTICS AS A SOURCE OF COMPETITIVE ADVANTAGE Using analytics to achieve a competitive advantage is on the rise.

+57%

58%

37%

2011

2010

Percent of all respondents who cited a competitive advantage with analytics year-over-year

This document is authorized for use only by Alma Gonzalez ([email protected]). Copying or posting is an infringement of copyright. Please contact customerse[email protected] or 800-988-0886 for additional copies.

60 MIT SLOAN MANAGEMENT REVIEW FALL 2011 SLOANREVIEW.MIT.EDU

T H E N E W I N T E L L I G E N T E N T E R P R I S E

Three Elements of a Data-Oriented Culture According to our research, a data-oriented culture

at the enterprise level has three key characteristics:

1. Analytics is used as a strategic asset;

2. Management supports analytics throughout the

organization;

3. Insights are widely available to those who need them.

(See “Key Elements of a Data-Oriented Culture,”

p. 62, for the percentage of Transformed organiza-

tions with these characteristics.)

By culture, we mean a pattern of practices, be-

haviors and norms organized around a set of shared

aims and beliefs. A data-oriented culture is a pattern

of behaviors and practices by a group of people (in

a department, line of business or enterprise) who

share a belief that having, understanding and using

certain kinds of data play a critical role in the suc-

cess of their business. Explicit codes of conduct,

norms, principles of use and incentives are aligned

to support these patterns.

The role of a data-oriented culture may vary. Data

and analytics may be at the core of an organization’s

everyday operations (as with CarMax). Or it can be on

equal footing with other cultures that predominate in

an organization, coexisting comfortably or uncom-

fortably with others. From its beginnings, Huffington

Post, the online newspaper cofounded in 2005 by Ari-

anna Huffington and sold to AOL for $315 million in

2011, has used analytics to track the popularity of its

stories, blogs and other content; moving, tweaking or

removing content in real time depending on what is

resonating most with readers. Before its merger with

AOL, Huffington Post began hiring journalists from

traditional newspapers, which did not employ such

tools. To succeed, the online paper had to manage a

potential clash between its data-oriented culture and

the culture of its new hires.5

The path to a data-oriented culture may vary. In

some cases, this culture may exist from the very be-

ginnings of an organization; more often than not, a

data-oriented culture evolves over time. Here’s how

one executive described the way his organization’s

culture has become more data-oriented:

What I’m seeing, from an organization perspective,

is more of a focus on understanding what the data

are telling us in order to use resources in the most

efficient and effective way possible. People would

have hypotheses or strategies that they would want

to pursue through numbers. They would quantita-

tively analyze them, but for the most part, unless

there was a glaring difference between the hypothe-

sis and the analytics, people would pursue their

strategies as long as they were compliant with our

legal and regulatory requirements. That’s pretty

much going away. Because we’re at a point where

we can’t ignore any data telling us about the effec-

tiveness of our business strategies.

Foundations of Analytic Competence In addition to creating a data-oriented culture, orga-

nizations that excel at using analytics to create a

competitive advantage must also excel at two other

competencies: information management and ana-

lytics expertise. Without a strong proficiency in both,

any data-oriented culture will lack critical supports

and be vulnerable to organizational and economic

change. Culture, information management and ana-

lytic expertise are mutually reinforcing.

WHO IS GAINING A COMPETITIVE ADVANTAGE FROM ANALYTICS Experienced users show the biggest gains among all groups.

-5%

Percentages of respondents who cited a competitive advantage with analytics year-over-year

2011

2010

+23%

2011

2010

+66%

2011

2010

Aspirational

37%

39%

80%

65%

63%

38%

Experienced

Transformed

This document is authorized for use only by Alma Gonzalez ([email protected]). Copying or posting is an infringement of copyright. Please contact customerse[email protected] or 800-988-0886 for additional copies.

SLOANREVIEW.MIT.EDU FALL 2011 MIT SLOAN MANAGEMENT REVIEW 61

Building these competencies takes time, and

each poses distinct challenges. Analytic expertise is

built from talent, tools and technology, whereas a

solid information management agenda is built

from strong data governance, data management

practices and the capability to deliver the right in-

formation to the right people at the right time.

Our analysis of responses from Transformed or-

ganizations, who make up 24% of all respondents,

showed that a majority of Transformed organiza-

tions are strong on both competencies.

A natural question would be: Which compe-

tency should a manager tackle first?

To answer that question we looked at response

patterns from a representative sample of 1,200 Ex-

perienced users, using a set of key questions within

the survey. We found that there is no “typical” se-

quential evolution of competencies and culture.

Interestingly, this deeper examination revealed

patterns that show most Experienced organizations

are taking one of two distinct approaches to analyt-

ics. Just under half are taking an approach focused

on developing their information management

competency, with attention focused on creating an

enterprise-wide information platform to support

broad and consistent use of analytics; we call these

Collaborative organizations. On the other hand,

slightly more than half are focused on building

their analytics expertise. With this specialized line-

of-business or functionally focused approach to

analytics, leaders are deepening analytic skills

within operations, finance and marketing to opti-

mize and predict specific business processes. We

call these Specialized organizations.

Collaborative Organizations Emphasize Informa-

tion Management Collaborative and Specialized

organizations have taken different approaches to cre-

ating an information management competency, which

typically involves a single integrated analytic platform

that shares data across product lines and functional

channels. Many Collaborative organizations have de-

veloped capabilities that enable silo-busting data

creation and sharing. Twice as many Collaborative en-

terprise-focused organizations as Specialized users

report strong data integration practices and skills.

Such integration can have important benefits. For

example, several years ago, when the U.K.’s BT (for-

merly British Telecom) was transitioning from a

telephone company to a 21st-century broadband com-

pany, the company had well-developed data systems,

but poor data integration across functions.6 Their cus-

tomer service was notorious, with the speed to

completion of customer service calls more important

in certain functions than whether a customer had his

or her problem resolved. By linking together its data

silos, creating broadband-related management incen-

tives and cultivating a new culture of collaboration

across functions, the company’s broadband venture

was able to improve its customer service dramatically.

And, in less than two years, BT’s broadband customer

base grew from 1 million to 5 million customers.7

Collaborative organizations are almost three

times more likely to use analytics to guide future

strategies than Specialized organizations and are

shifting to rely on analytics in day-to-day operations

as well, at twice the rate of Specialized organizations.

Collaborative organizations are more than twice as

likely to deliver insights to customer-facing employ-

ees to drive sales and productivity, and twice as likely

to provide insights to anyone in the organization

who needs them.

Specialized Organizations Emphasize Analytics

Expertise On the other hand, many organizations

taking a Specialized approach have deepened their

analytic skills beyond basic spreadsheets and visual-

izations; they are applying advanced modeling

THREE CHARACTERISTICS OF COMPETITIVE ANALYTICS Successfully competing on analytics depends on capabilities in three critical areas.

Baseline Analytics

Competitive Analytics

Data-oriented culture

Information management

Analytics expertise

This document is authorized for use only by Alma Gonzalez ([email protected]). Copying or posting is an infringement of copyright. Please contact customerse[email protected] or 800-988-0886 for additional copies.

62 MIT SLOAN MANAGEMENT REVIEW FALL 2011 SLOANREVIEW.MIT.EDU

T H E N E W I N T E L L I G E N T E N T E R P R I S E

techniques to data to create simulations, prototypes

and scenarios to better understand how changes —

from internal investments or external forces — will

impact processes, revenue growth and operating

costs. These predictive analytic techniques help

managers understand what is probable rather than

just what is possible.

Robert Gooby, vice president of process redesign

at McKesson, the North American pharmaceutical

distributor, is developing a data system that tracks

every element in its pharmaceutical distribution sup-

ply chain:

It gives us a model of the whole operation. Most

models are simplifications of the physical world.

You have to hope that the as