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# M I T S M R r e p o r t

R E P R I N T N U M B E R 5 8 3 8 0

Analytics as a
Source of
Business
Innovation
The increased ability to innovate is
producing a surge of benefits
across industries.

SPRING 2017

RESEARCH
REPORT

By Sam Ransbotham and David Kiron

Sponsored by:

R E S E A R C H R E P O R T A N A L Y T I C S A S A S O U R C E O F B U S I N E S S I N N O V A T I O N

Copyright © MIT, 2017. All rights reserved.

Get more on data and analytics from MIT Sloan Management Review:

Read the report online at http://sloanreview.mit.edu/analytics2017

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Contact us to get permission to distribute or copy this report at smr-help@mit.edu or 877-727-7170

AUTHORS

CONTRIBUTORS

SAM RANSBOTHAM is an associate professor in
the Information Systems Department at the Carroll
School of Management at Boston College, as well as
guest editor for MIT Sloan Management Review’s
Data & Analytics Big Ideas initiative.

DAVID KIRON is the executive editor of MIT Sloan
Management Review.

Nina Kruschwitz, senior project manager, MIT Sloan Management Review

The authors conducted the research and analysis for this report as part of an MIT Sloan Management
Review research initiative sponsored by SAS.

To cite this report, please use:
S. Ransbotham, D. Kiron, “Analytics as a Source of Business Innovation,” MIT Sloan Management Review,
February 2017.

ANALYTICS AS A SOURCE OF BUSINESS INNOVATION • MIT SLOAN MANAGEMENT REVIEW 1

CONTENTS
RESEARCH
REPORT
SPRING 2017

5 / Resurgence in
Competitive Advantage
from Analytics

• Channeling the data deluge

• Concentrating analytics on
specific business issues

• A tide of innovation

6 / Analytical Innovators at
a High-Water Mark

8 / Navigating Data-Driven
Innovation

• Beyond incremental
improvement

• Functional areas that excel
with data

10 / Sharing Data
Accelerates Innovation

• Creating passages between
organizations

• Data governance liberates
opportunity

• Smart machines create
more time for innovative
thinking

14 / Conclusion

16 / Acknowledgments

ANALYTICS AS A SOURCE OF BUSINESS INNOVATION • MIT SLOAN MANAGEMENT REVIEW 3

Analytics as a
Source of
Business
Innovation

N
ot long ago, Keith Moody was the only data analyst at Bridgestone Americas
Inc. He was located in the credit division in Brook Park, Ohio, and saw
analytics take off — in other companies. When Bridgestone Americas named
a data-savvy executive, Gordon Knapp, as chief operating officer in March
2014, Moody was given the opportunity to build a new analytics department
for Bridgestone Retail Operations, the company’s U.S. network of tire and

auto repair stores. Today, Moody reports to the interim president, Damien Harmon, as director of
analytics for Bridgestone Retail Operations, where he is making up for lost time.

Moody’s team is influencing management practice in virtually every part of the organization. Work-
ing with the real estate department, the analytics team pinpoints the best locations for new stores.
Working with operations, it automates provision of inventory to 2,200 stores.1 Working with human
resources, it determines the best allocation of 22,000 employees so that Bridgestone retail locations
have the right people on-site to deal with peak demand — and don’t have workers sitting around
with time on their hands. What’s more, Moody’s team is looking for ways to use driver data, such as
odometer readings and other telematics data, to encourage car owners to come in for new tires or a
tune-up before they hear a rattle under the hood and have to look for the nearest repair shop. This
new reliance on analytics to inform executive decision making and to develop new services reflects a
cultural shift for Bridgestone’s operations in the United States.

What’s happening at Bridgestone provides a window into the state of analytics across industry. After
years of enthusiasm and frequent disappointment, a growing number of companies are developing
the tools and, increasingly, the skills to move beyond frustration. They are progressively able to ac-

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cess large pools of data and use analytics to inform
decision making, improve day-to-day operations,
and support the kinds of innovation that lead to stra-
tegic advantage and growth.

MIT Sloan Management Review’s seventh annual
data and analytics survey, conducted during 2016,
reveals a sharp rise in the number of companies re-
porting that their use of analytics helps them beat
the competition. These survey results include re-
sponses from 2,602 managers, executives, and data
professionals from companies around the globe.
(See “About the Research.”) The findings reverse
a three-year trend in our survey data (2013-2015),
in which fewer companies year over year reported
a competitive advantage from their use of analytics.

So, why the reversal? What changed? Our findings
offer clear signals that companies are increasing
their use of data and analytical insights for strate-
gic purposes and are using data and analytics to

innovate business functions as well as entire busi-
ness models. Indeed, analysis of our survey results
and interviews with more than a dozen executives
and scholars indicates that the ability to innovate
with analytics is driving the resurgence of strategic
benefits from analytics across industries. In this re-
port, we delve into the enablers of innovation with
analytics and find that data governance capabilities,
especially around data sharing and data security,
form the foundation for these innovation processes.

The four key findings from our research are:

• More companies report competitive ad-
vantage from their use of data and analytics,
re versing a three-year trend. According
to several indicators in our 2013, 2014, and
2015 surveys, fewer companies were deriving
competitive advantage and other important
benefits from their investments in analytics
than in previous years. According to this lat-
est survey, however, that trend seems to have
reversed, and more companies are now seeing
gains. This is due to several factors, including
wider dispersion of analytics within companies
and better knowledge of what analytics can do,
as well as a stronger focus on specialized, inno-
vative applications that have strategic benefits.

• Innovation from analytics is surging. The share
of companies reporting that they use data and
analytics to innovate rose significantly from last
year’s survey. Organizations with strong analyt-
ics capabilities use those abilities to innovate not
only existing operations but also new processes,
products, services, and entire business models.

• Data governance fosters innovation. Com-
panies that share data internally get more
value from their analytics. And the companies
that are the most innovative with analytics are
more likely to share data beyond their company
boundaries. Survey results show that strong
data governance practices enable data sharing,
which then enables innovation. To be most ef-
fective, data governance needs to be embedded
in an organization’s culture. Tactics are not the

This is the seventh MIT Sloan Management Review research
study of business executives, managers, and analytics
professionals. This year’s 2,602 survey respondents were drawn
from a number of sources, including MIT Sloan Management
Review subscribers. They represent organizations around the
world and from a wide range of industries.

The research also includes interviews from experts from a
number of industries and disciplines. Their insights into the
evolving uses of analytics have enriched our understanding of
the survey data. In addition, we incorporate case examples that
document how analytics are being used.

In this report, we use the term “analytics” to refer to the use of
data and related business insights developed through applied
analytical methods — using statistical, contextual, and
predictive models, for example — to drive fact-based planning,
decisions, execution, management, and learning.

ABOUT THE RESEARCH

ANALYTICS AS A SOURCE OF BUSINESS INNOVATION • MIT SLOAN MANAGEMENT REVIEW 5

same as cultural norms. Data governance needs
to be more than a system of tactics to derive
business value — it must actually influence or-
ganizational behavior.

• Smart machines create opportunity for in-
novative thinking. Smart machines that draw
inferences from data on their own and learn by
using algorithms to discern patterns in masses of
data are no longer confined to research labs and
limited applications such as speech recognition.
The most analytically mature companies use ar-
tificial intelligence to augment human skills and
to take on time-consuming tasks, freeing man-
agers to spend more time on strategic issues.

From 2013 to 2015, our annual surveys showed a
steady ebb in the percent of companies reporting a
competitive advantage from their use of data and an-
alytics. As analytics became more widespread, and
therefore a more common path to value, it became
more difficult for companies to gain or maintain a
competitive edge with data. “Those big early adopt-
ers got an early benefit,” notes Kristina McElheran,
assistant professor of strategy at the University of To-
ronto. She points out that in many cases, even early
adopters hit a slow patch after their initial successes
with analytics because they weren’t embedding ana-
lytics into the organization. “Until it becomes an
engine for learning, until it transforms your cost
structure or value to customers in a way that’s dif-
ficult for your competitors to imitate, then I don’t see
analytics as a silver bullet that lets firms get in front
of the pack and stay there,” she explains.

In 2016, managers in more companies said they are
getting ahead of the pack. This is a marked reversal
of the trend of the previous three years. The share
of respondents who say that analytics provides com-
petitive advantage rebounded to 57%, still off the
2012 peak of 67%, but well above the 51% of 2015.
(See Figure 1.)

Several factors contribute to the resurgence in com-
panies gaining a competitive advantage from data
and analytics: success applying data-driven insights
to strategic issues; application of analytics to a wide
range of business issues; technology advances, such
as cloud computing and distributed storage; and
data-driven innovations that make a material con-
tribution to the company’s competitiveness.

Channeling the data deluge

Our survey first tracked managers’ access to useful
data in 2012. In each of the five surveys since then,

Resurgence in Competitive
Advantage from Analytics

20112010 2012 2013 201620152014

Percent believing
that business
analytics creates
a competitive
advantage for
their organization

40%

50%

60%

70%

30%

20%

10%

0%

FIGURE 1: COMPETITIVE ADVANTAGE FROM
ANALYTICS RESURGES From 2015 to 2016, the share of
organizations reporting that analytics creates a competitive
advantage rose 6 percentage points.

Percent of respon-
dents reporting
a somewhat
or significant
increase in access
to useful data over
the past year

Percent of
respondents who
are somewhat or
very effective at
using insights to

guide future
strategy

esssssssssss
ve

2012 2013 201620152014

70%

56%

75%

55%

77%

52%

73%

49%

76%

55%
aaa
v
u

FIGURE 2: MORE ORGANIZATIONS TURN DATA
INTO STRATEGIC INSIGHTS From 2015 to 2016, the share
of organizations that report that they effectively use data for
strategic insights rose 6 percentage points.

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seven out of 10 managers reported a “somewhat” or
“significant” increase in their access to useful data
from the year before. Not surprisingly, over this
same period, the share of respondents who said
that they were “somewhat or very effective” in using
insights from analytics to guide strategy steadily
dropped, evidence that the flood of data hampered
rather than enhanced managers’ ability to translate
data to business value.

Our 2016 survey demonstrates a sharp reversal in
this trend. While access to useful data continues to
increase, 55% of companies said they were effective
at using data to guide future strategy, up from 49%
last year. (See Figure 2, page 5.)

Concentrating analytics on specific
business issues

This improved ability to apply insights to strat-
egy may reflect organizational changes in the way
managers use data to improve decision making
and enhance processes across the enterprise. As
McElheran points out, identifying useful data and
performing analyses is only part of the process. To
implement data-driven approaches that generate
measurable results, companies also need to make ad-
justments throughout the organization — in process
design, in supply chain operations, in compensation
and training, and in mindsets and behaviors across
the board. Those adjustments, McElheran says, take
time, which may help explain why fewer companies
reported competitive advantage and strategic in-
sight from 2012 to 2015.

Another reason for the improved ability to apply
insights to strategy is management’s application
of analytics to address specialized business is-
sues, such as understanding individual customer
behavior, that yield high-value results. More orga-
nizations are translating knowledge of their own
customers into specialized models that lead to
unique insights, rather than depending on exter-
nal data providers for more generic insights into
their customers’ behavior. Wayfair Inc., a Boston-
based online home goods retailer, is an example
of how analytics use is evolving from the general-

purpose to more specific, customized applications.
For years, the company used an outside vendor to
analyze data and optimize display-advertising pur-
chases. David Drollette, senior director of analytics
at Wayfair, brought the function in-house because
he believed that Wayfair would do better with ana-
lytics that were customized for its operation. “We
took a small team of data scientists, paired them
with business analysts, and created a display-adver-
tising functionality that beat our vendor, which is
a multi-hundred-person company, where that’s the
only thing they focus on,” he says. “So we were able
to take those costs off our books, take that ability
in-house, and really optimize a pretty important
channel for us.” General Mills Inc. and Entravi-
sion Communications Corp., the California-based
Spanish-language media company, are two other
companies wresting control from data vendors over
how they understand customers.2

More generally, as managers in various departments
and functions become more adept at analytics them-
selves, they are developing specialized approaches,
uniquely optimized to their situation, that answer
specific questions and solve problems. “We are
clearly seeing a specialization story playing out with
some of our repeat clients who are slowly but surely
realizing the vast potential of business analytics,”
says Ravi Bapna, who runs the Carlson Analytics
Lab at the University of Minnesota’s Carlson School
of Management. “A client that started three years
ago with an exploratory, unsupervised machine-
learning project to optimize aspects of a nationwide
product mix has now evolved into using individual-
level predictive modeling to tackle idiosyncratic
employee churn.” McElheran further observes that

“specialization is going to come rapidly on the heels
of a broad-based diffusion.”

A tide of innovation

Specialization, in turn, can direct analytics toward
innovations that deliver or contribute to com-
petitive advantage. In 2016, 68% of respondents

“somewhat agreed” or “strongly agreed” that analyt-
ics has helped their organizations innovate, up from
52% in 2015.

ANALYTICS AS A SOURCE OF BUSINESS INNOVATION • MIT SLOAN MANAGEMENT REVIEW 7

This finding suggests that the poster children for
data-driven innovation, such as General Electric,
Google, IBM, Airbnb, and Uber, are not lone stars.
Bridgestone and Nedbank Group Ltd., discussed
below, are two examples of traditional companies
now using data and analytics to improve their exist-
ing operations and create new business.

At Bridgestone, analytics allows the company to
innovate new processes in key areas, such as site se-
lection and staffing. A new staffing program, using
predictive analytics, determines the appropriate
allocation of 22,000 workers across 2,200 stores —
putting enough workers in stores for peak demand
while avoiding unneeded labor costs when business
is slower. “The headcount model we built is based
on standard industry practice, but it’s groundbreak-
ing here at Bridgestone,” says Moody. The payoff will
be millions of dollars per year in efficiency gains
and increased sales, he says. The key advantage for
Bridgestone is applying those industry standard
practices in ways that capitalize on Bridgestone’s
unique capabilities.

At Nedbank, the fourth-largest bank in South Af-
rica, analytics targets bank marketing efforts more
precisely. The bank tracked customer profitability
by product for many years, but when it combined
several sets of product and customer data, branch
managers could then identify the most profitable
customers and offer special discounts and other in-
centives to increase patronage. At Nedbank, analytics
goes beyond just improving existing processes; the
bank also developed an entirely new service line for
commercial customers based on its growing exper-
tise in analytics. Market Edge is a web-based service
that lets Nedbank’s merchant customers identify
their own best customers, based on the bank’s analy-
sis of transactional credit- and debit-card data.

For the past five years, we have assessed an organi-
zation’s analytical maturity in terms of its ability to

innovate with data and to gain a competitive advan-
tage from analytics. With the surge in organizations
reporting data use along both of these dimensions,
analytics maturity within the corporate landscape
has shifted. Figure 3, on page 7, illustrates this shift.

2012 2013 201620152014

Percent of
respondents
classifed in
each level
of analytical
maturity Analytically

Challenged

Analytical
Practitioners

Analytical
Innovators11% 12% 12% 17%

60% 54% 54%
49%

29%
34% 34% 33%

10%
41%

49%

FIGURE 3: THE NUMBER OF ANALYTICAL
INNOVATORS JUMPED FOR THE FIRST TIME The share of
organizations that qualify as Analytical Innovators rose from 10% to 17%.

Analytical Innovators at a
High-Water Mark

THREE LEVELS OF ANALYTICS MATURITY

In our research, we categorize companies based on their level of so-
phistication in analytics and their success in using data to innovate
and to build competitive advantage.

Analytical Innovators
These companies have an analytics culture, make data driven deci-
sions, and rely on analytics for strategic insights and innovative ideas.

Analytical Practitioners
Analytical Practitioners have adequate access to data and are work-
ing to become more data driven. They use analytics primarily to
effect operational improvements.

Analytically Challenged
The least advanced companies still rely more on management intu-
ition than data for decision making. They struggle with data access
and quality and lack data management skills.

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Figure 3 depicts the sharp rise in the number of Ana-
lytical Innovators — those organizations that use data
and analytics to innovate and obtain a competitive ad-
vantage to a moderate or great extent. This is the first
time that the share of respondents in this category has
exceeded 10%-12% of survey respondents. (The side-
bar, “Three Levels of Analytics Maturity,” describes
the characteristics of companies in each category.)

The level of Analytically Challenged companies, the
least-advanced category, fell to 33% in 2016, down
from its 2015 high of 49%. Meanwhile, the share of
Analytical Practitioners — companies that are work-
ing to become data driven and are adopting some
complex approaches to analytics — rose to 49% in
2016 after having dropped to a five-year low of 41%
in 2015.

Analytical Innovators use data and analytics both
to innovate incrementally in existing products, ser-
vices, and processes and to create all-new products,
services, and business models. (See Figure 4.) Ana-
lytical Innovators are more than 60% more likely
than Analytical Practitioners to use analytics for in-

novations that lead to new products, services, and
processes or improve existing ones.

While conceptually distinct, the edge between incre-
mental innovation and the kind of innovation that
enables a new business model may not be clear in
practice. At the University of Pennsylvania’s Whar-
ton School, professor Peter Fader and the team at
his predictive analytics startup, Zodiac, developed a
system to crunch various types of data to determine
which customers are most valuable — that is, most
likely to use a company’s products and services again
and most likely to buy a new product. Based on this
analysis, the system predicts a total lifetime value for
each individual customer. Marketers can then pri-
oritize them accordingly.

That may seem like an incremental improvement
on customer segmentation, but that’s not how Alvin
Glay, head of digital marketing for Wahoo Fitness,
sees it. Wahoo Fitness, based in Atlanta, Georgia,
makes sports and fitness products, including work-
out apps and smartphone-connected fitness devices,
such as heart rate monitors, indoor smart-bike
trainers, and GPS bike computers. When he learned
about Fader’s approach, he saw a new business op-
portunity. “We sent them detailed, non-personally
identifiable information [non-PII] transactional
data. We also sent them geography information and
the category that customers purchase in,” says Glay.

“They came back and said, on a customer-by-cus-
tomer basis, these are the customers that essentially
have a high value. We said, let’s take the top 20% of
cyclists in terms of customer lifetime value and run
digital campaigns for our new bike computer prod-
uct targeting those customers, instead of everyone
who purchased a bike computer in our database.
The results we saw with this approach were amazing,
and we are looking forward to exploring this further.”

Beyond incremental improvement

Well over 80% of Analytical Innovators and half of
Analytical Practitioners use analytics to innovate
new products, services, and processes. What kinds
of innovations are they pursuing? At Bridgestone,
Moody describes an idea that would radically alter

Navigating Data-Driven
Innovation

FIGURE 4: ANALYTICS FOSTERS MANY WAYS TO
INNOVATE Innovation with data is becoming common practice in a
wide variety of ways.

Analytical Innovators
Analytical Practitioners
Analytically ChallengedPercent of

respondents
reporting that
analytics has
helped the
following types
of innovation to
a moderate or
great extent

New
product/service

New
processes

Existing
product/service

Existing
processes

90%

56%

20%

93%

18%

88%

54%

16%

87%

50%

17%

58%

ANALYTICS AS A SOURCE OF BUSINESS INNOVATION • MIT SLOAN MANAGEMENT REVIEW 9

his company’s business model. If the company
could gain access to telematics information about
how many miles a car has been driven — a big “if ”
at this point — it could create a new way of sell-
ing. Instead of waiting for a car owner to drive in
for replacement tires, for example, the company
could tell the customer when the car is due for new
tires and craft a custom offer to encourage driv-
ers to come into the nearest Firestone Complete
Auto Care store. This approach, which depends
on data navigating its way between automobiles
and Bridgestone, could be used to offer preven-
tive maintenance, encouraging drivers to bring
their vehicles in for service before they hear an
ominous knocking under the hood or the brakes
start to fade. “This predictive analytics approach
changes entirely the way that we look at our role in
the business,” says Moody. “We’re trying to get in
front of the event rather than behind it.”

Like Bridgestone, some companies that are re-
vamping their business models with data-driven
innovations are discovering new levels of customer
engagement with analytics and new opportunities
to engage with organizations in their business value
chain. In the Bridgestone example, for instance, the
tire manufacturer could offer a new service to cus-
tomers but only if it first works with automakers or
software providers to make the requisite data shar-
ing possible. Furthermore, what Bridgestone then
learns about automobile performance and customer
behavior might have value on its own that then could
be the source of unknown new revenue opportuni-
ties. Indeed, a growing number of organizations
have begun monetizing analytical capabilities that
they have produced in the course of developing
data-driven innovations, including companies as
diverse as Entravision, GE, and the pharmaceutical
distributor McKesson Corp.3

Functional areas that excel with data

Within companies, innovation with data varies
across departments and functions; for example, de-
partments may emphasize incremental innovation
or more radical innovation. In Figure 5, a score of 50
indicates an even mix; the higher the score, the more

FIGURE 6: FEW DEPARTMENTS USE ANALYTICS
HEAVILY FOR ALL TYPES OF INNOVATION
Beyond relative differences in emphasis, departments also vary in
their absolute amounts of innovation through analytics.

Percent of respondents reporting that analytics
has helped the following types of innovation to a
moderate or great extent.

Improving processes
Improving products/services
Developing processes
Developing products/services

Customer service

Finance

General management

Human resources

Information technology

Marketing

Operations

Product development

Research and development

Risk management

Sales

Supply chain

40% 50%30% 60%

What percentage of your functional
area’s use of data and analytics is
being spent improving processes,
products, and services vs.
developing new ones?

Developing
new

processes

Improving
existing
processes

Customer service

Finance

General management

Human resources

Information technology

Marketing

Operations

Product development

Research and development

Risk management

Sales

Supply chain

50%20% 80%

39%

39%

43%

44%

40%

45%

38%

47%

48%

40%

46%

44%

FIGURE 5: INNOVATION EMPHASIS VARIES BY
DEPARTMENT Departments mix their use of analytics between
incremental and radical innovation.

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radical innovation in products, services, and pro-
cesses is taking place in the department on average.

Figure 5, on page 9, shows a detailed breakdown of
innovation activity by department. It shows that the
departments that use data to innovate new prod-
ucts are sales (58%) and human resources (56%)

— ahead of product development (52%) and R&D
(49%). Surprisingly, human resources also leads in
innovation of new processes, followed by supply
chain and finance. One possible explanation for this
finding is that it may be easier for some departments
to innovate new processes when use of analytics is
still relatively new; the differences we observe be-
tween organizations in analytics adoption is also
true within organizations.

Figure 6, on page 9, also shows that only a few depart-
ments use analytics for innovation across the board;
most focus on either new products, services, and
processes or improving existing processes — but not
on both. An exception is human resources. Finance
departments, which are known for their embrace of
analytics, reported relatively limited use of analytics
for new products, services, and processes.

The ability to innovate with data is clearly tied to
having effective data-sharing practices (though to a
lesser extent in some — but not all! — heavily regu-
lated industries). (See Figure 7.) Organizations with
a high ability to innovate (those that somewhat or
strongly agree that analytics helps them innovate)
share data both internally and beyond …

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