FINDINGS FROM THE 2017 DATA & ANALYTICS GLOBAL EXECUTIVE ST

FINDINGS FROM THE 2017 DATA & ANALYTICS GLOBAL
EXECUTIVE STUDY AND RESEARCH PROJECT
# 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.
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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
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-
4 MIT SLOAN MANAGEMENT REVIEW • SAS INSTITUTE
INC.
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
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.
INC.

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.
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.

INC.
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.
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%

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
Marketing
Operations
Product 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.
10 MIT SLOAN MANAGEMENT REVIEW • SAS
INSTITUTE INC.
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 company bor-
ders at much higher levels than other organizations:
80% of these organizations report sharing data inter-
nally, compared with 53% of other organizations.
Yet, in many organizations, data remains stuck in
functional silos or within departments. Nearly
half of respondents say that their companies are
secretive or somewhat secretive about sharing data
(internally and externally). Less than 10% describe
their companies as open about sharing data. “It’s
a fun topic within our company, because each
division has its own data silos,” says Bridgestone’s
Moody. “We’re slowly starting to break down those
walls and trying to build out an enterprise analyt-
ics sandbox, where we can get all the data together

so we can do a lot of the more advanced analyt-
ics modeling.” Technical barriers to sharing are
diminishing with increased reliance on infrastruc-
ture such as cloud computing, but organizational
barriers are still common impediments to dissolv-
ing data silos and creating broad-based access to
useful information.4
At W.L. Gore & Associates Inc., systems architect
Chris Chen is keenly aware of the need to unlock
siloed data to enable innovation. Gore, a manu-
facturer of advanced materials based in Newark,
Delaware, is a research-driven company that is fa-
mous for its Gore-Tex waterproof fabric. “We have
been running experiments for almost 60 years, but
we should be able to do more with the data,” Chen
says. “If we could look at all the experiments collec-
tively, would we see that we completely missed some
white space in the search? It is hard to answer that if
each experiment is a one-off dataset sitting on indi-
vidual computers.” Sharing is particularly important
Sharing Data Accelerates
Innovation
FIGURE 7: SHARING DATA HELPS ORGANIZATIONS
INNOVATE Organizations with a high ability to innovate share
data the most.
High ability to innovate
Low ability to innovatePercent of
respondents
who somewhat
or strongly
agree that their

organization
makes data
available to
the following
groups
Internal
stakeholders
Suppliers CompetitorsPotential
customers
Existing
customers
35%
63%
38%
21%
53%
80%
15%
8%
43%
58%

for catching errors of omission. Without effective
data-sharing practices, it’s difficult for an organiza-
tion to know whether some analysis has been tried
before, with or without success. Processes need
to be established to record both successful and
unsuccessful results in order to avoid errors. Chen
believes that by combining data from all those ex-
periments, the company might “stumble upon” the
next Gore-Tex, an innovation that nobody knew
was needed but has become essential to outdoor
enthusiasts and workers, as well as a huge success
for the company. “More importantly, is there a more
methodical way to stumble?” he adds. “That’s what
data and analytics lets us do.”
Sharing data across silos is necessary, but by itself,
data sharing is insufficient to generate valuable
insights; companies often need employees with
very different skill sets to collaborate in order to
unite different views about what the data means.
Arabesque Partners, a London-based asset man-
agement firm that invests in companies with good
environmental, social, and governance (ESG)
practices, needs analytics teams and subject-matter
experts to work together to weight a variety of
data inputs, from board composition information
to green supply chains, in order to create the best
algorithms. “Our firm is built on two pillars, sus-
tainability research and the quant skill set, using
artificial intelligence in order to maximize informa-
tion out of that,” says CEO Omar Selim. “I look at
the head of ESG and the head of quant, and think,
‘Thank goodness they are good friends, because they

fight often with each other.’ But the friction is where
we generate the value.”
It is possible, of course, for information sharing to
undermine the innovation that leads to distinctive
products. At Gap Inc., the company’s analytically
oriented CEO Art Peck encourages product teams
from The Gap and Old Navy to meet regularly to
discuss fabric innovations and other issues. But
some analysts believe that Old Navy cannibalized
sales from The Gap, as the two brands now sell simi-
lar merchandise.5 Knowing when and how to share
which information — and why — helps determine
an effective data-sharing practice.
Creating passages between
organizations
Sharing data beyond the bounds of the corporation is
another way in which organizations that use data to
innovate get the most out of analytics. Wahoo Fitness
puts data at the core of its marketing initiatives to de-
velop insights about its customers and how to market
to them and find other individuals like them — such
as identifying those that have the highest lifetime
value — that the company could not generate with its
own data alone. So, for example, it uses insights from
social signals on Facebook and Strava (a fitness app
for cyclists) that in turn provide Wahoo with infor-
mation about the online behavior of those consumers,
including ad impressions that they are exposed to.
Combining multiple data sources, while difficult,
provides insights that are not possible when they are
used in isolation from one another.
German automakers BMW, Daimler, and Volkswa-

gen take the practice of sharing data to a new level.
In 2015, they formed an alliance and bought Berlin-
based HERE, a digital mapping company, to create a
crowdsourcing service that enables drivers to share
detailed video views of traffic jams and other road
conditions on a single platform. “You have compet-
ing brands which are putting their data together to
create very unique services which were not possible
before,” says Bruno Bourguet, HERE’s global head
of sales.6 The new service, expected to go live in the
first half of 2017, will also collect data from brakes,
windshield wipers, headlights, locations systems,
and other sensors from their respective car brands
to deliver real-time alerts to driver dashboards. The
sheer number of customers participating in this
platform is expected to create a service that delivers
more value to each car owner than a comparable
effort from an automaker with fewer customers — a
competitive advantage for the partnership.
Competitors’ willingness to share what they regard as
proprietary information, even with guarantees that
their data will be anonymized and protected, varies
by industry. GE is still trying to convince oil and gas
customers to share performance data for industry-
wide benchmarking. The benefits could be enormous,
INSTITUTE INC.
since even small improvements can be worth hun-

dreds of millions of dollars for major oil companies.
Oil and gas customers tell GE that they would like to
have the benchmark data but are unwilling to con-
tribute their own, so the data sharing is not occurring
— nor is the innovation it might enable.7
Efforts to share data across industry lines — even
when there is little risk that a competitor will gain
advantage — are also fraught. As noted, Bridgestone
sees an opportunity to create a new business model
based on selling proactively, reminding customers
when it’s time to have their tires checked or perform
preventive maintenance. But it does not yet have
access to the telematics data gathered by onboard
computers to make the model work. Auto dealers
do have access to the data, at least when cars are
under warranty. And some insurance companies
also gather telematics data from drivers who permit
them access in order to qualify for discounts based
on what the data shows about their driving habits or
for pay-as-you-go coverage.
Today, neither car manufacturers nor insurers share
telematics data, but Moody is optimistic that they
will. “I think data sharing, especially with another
industry, is really going to start to open up, because
we are going to start competing so much with ana-
lytics and data that the more that we can partner
with others to potentially share data or trade data
between organizations, the better everyone’s ana-
lytics will be,” he says. “I see a huge amount of new
relationships forming to be able to do data sharing
among companies to help improve decisions.”

As these examples make clear, ownership of useful
data is altering power relationships within indus-
tries and even within companies. As organizations
learn how to extract more and more value from data,
incumbents that grew to prominence based on phys-
ical assets now face diminished importance of those
assets due to the rising value of data. Amazon.com
Inc.’s knowledge about what its more than 300 mil-
lion customers are buying, for instance, gives it an
enormous advantage over traditional retailers and
provides market power in its dealings with suppliers.
Data governance liberates
opportunity
Opening the data floodgates between organizations
and industries won’t work without structure. Data
governance encourages data sharing by control-
ling what can and cannot be shared. In health care,
well-established regulations about how patient data
can and cannot be shared can actually encourage
sharing rather than restrict it. In our survey, 25% of
respondents from health care industries said they
are likely to share data with competitors, compared
with 19% of respondents from other industries. (See
Figure 8.) Nearly 40% of companies that have both
high innovation capabilities and are high-sharing
(an overlapping set) agree that good governance is
liberating, while only 14% of companies with low
innovation capabilities see governance as a positive.
Good governance can improve both the effective-
ness and speed with which shared data and analytics
improve innovations:
• When using shared data, organizations are fur-

ther removed from the original source of the data
and may miss important information about the
data. “Effective use requires both stewardship
FIGURE 8: GOVERNANCE CAN LIBERATE
Organizations that share data and innovate say governance
helps.
Percent of respon-
dents who agree to
a moderate or great
extent that their
organization’s data
security practices lib-
erate them to create
value from analytics
HighLow
Ability to innovate
HighLow
Level of sharing
19%
38%
14%
39%

and protocols,” says Peter Levin, a senior research
scientist at Intel Corp. “Stewardship defines both
data and algorithm access, limits, and exchange
rules. Protocols describe the metadata needed to
provide the context.” Good governance practices
promote effective use of data.
• Integrating data from multiple sources can slow
down the data flow, as each step can add delay. At
the Federal Bureau of Investigation, maintaining
security — a form of preventive maintenance in
the public sector — often depends on many dif-
ferent groups sharing data with one another in
a timely manner. “Security events may be con-
nected even though initially they may appear
isolated,” says Kevin Swindon, an FBI special
agent and supervisor of the Boston Division
CYBER Program. “Analytics now lets us uncover
patterns, and these patterns may provide inves-
tigative clues. However, speed is critical. As we
have better defined our processes around data
sharing, we’re able to focus on these types of inci-
dents quickly, rather than spending time figuring
out the mechanics around the data.” Good gov-
ernance practices can also improve the speed of
innovative use of data.
Smart machines create more time for
innovative thinking
Smart machines that can take on tasks that tra-
ditionally required a human have captured the
popular imagination. But the immediate ben-
efits from smarter machines are not in human
replacement. As Tom Davenport, the President’s
Distinguished Professor of Information Technology

and Management at Babson College, has written,
“Of course, automation technologies bring fears of
job loss. I believe that when an organization adopts
these tools, it’s a bad idea to put the primary focus
on eliminating human jobs.”8 Instead of elimination,
liberation and augmentation more aptly describe
the implications of automation for some segments
of the labor market. For example, machine-learning
techniques applied to dull, repetitive, data-cleaning
work allow computers to learn from patterns they
discern in large datasets, enabling companies to
automate some analytical tasks and freeing up data
experts to work on higher-value-added tasks. Data
experts are just one of many pools of workers that au-
tomated work flows may affect in ways that are not
yet known.9
For several years, the more advanced corporate users
of analytics in our surveys have told us they are using
analytics to automate processes in their companies.
This year, 63% of our Analytical Innovators say they
are somewhat or very likely to turn analytical insights
into automated processes. (See Figure 9.) This com-
pares with 14% of respondents in the Analytically
Challenged category. More than 60% of all compa-
nies surveyed say that some organizational tasks once
done by humans in their companies have been auto-
mated, at least to some extent, because of analytics.
More than 40% of companies surveyed say that they
use analytics to augment human tasks, and 70% of
Analytical Innovators say their companies are doing
so. Fewer companies overall and fewer Analytical In-
novators say that tasks are being fully automated. So,

at least for now, rather than always replacing human
skills and jobs, companies use analytics to help hu-
mans work better or complete tasks that they could
Analytically Challenged
Overall
Percent of respondents reporting a
moderate or great extent of change
due to data and analytics
New
human tasks
Humans now but
previously automated
TasksTasksT
augmented
Tasks nowTasks nowT
automated
62%
39%
18%
36%
70%
45%

21%
41%
30%
17%
7%
16%
59%
38%
17%
34%
FIGURE 9: ANALYTICS ENABLES TASK
AUTOMATION AND AUGMENTATION Organizations
increasingly automate and augment, but new tasks for people
may
be the result.
INSTITUTE INC.
not have done themselves, such as scanning millions
of customer records to find patterns.

At Wayfair, Drollette talks about the importance of
automating certain types of work. “I think real time
is incredibly important, but to put a real-time data
feed in front of a person is kind of a recipe to have
them clicking their refresh button a little compul-
sively,” he says. “Instead of having a human try to
watch it and make sense of it, let’s put some complex
event-processing or some other algorithm in front
of it to decide what’s really useful in real time, curate
that, and maybe send an email when there’s some-
thing interesting, when there’s an exception that
needs to be looked into.” Machine intelligence in this
context lets Wayfair business processes use massive
data at scale, matching machines and humans to
their strengths.
Bridgestone found that workers were more than
happy to get assistance from a smart algorithm. For
years, the company used an essentially manual pro-
cess to allocate inventory across the United States.
Detailed segmentation through analytics led to
many specialized and targeted products, but after
a while, some stores had no room left to store ad-
ditional inventory. Physical constraints kept each
location from being able to keep a volume of every
product on hand. So employees then had the new
task of allocation to each individual location based
on its idiosyncratic customer characteristics. Moody
and his team offered to embed the current human
processes into dynamic algorithms that would use
sales data to allocate store inventory. The team that
had been struggling with the inventory process wel-
comed the new system. “They said, ‘Please help us
do this,’ ” Moody recalls. Now, instead of spending

their days trying to set the stock levels across the
entire country, the team is occupied with more stra-
tegic questions and happy to let the model do the
grunt work.
Similarly, at video game producer Electronic Arts
Inc., based in Redwood City, California, the design-
ers who dream up new games are embracing an
analytics system that tells them what characteristics
will make a game attractive to EA’s best customers.
They don’t regard it as a loss of creativity, says the
Wharton School’s Fader, but as a way to succeed.
“The chief analytics guy told me it’s every bit as much
a creative business as it was before, maybe more so,
because instead of trying to come up with a game for
everybody, they are designing for these really valu-
able customers, and it may be even more of a creative
challenge,” he says.
Many functional areas within organizations in-
creasingly look to data and analytics as a source of
knowledge and influence. Nearly 37% of respon-
dents in our 2016 survey say that analytics has
shifted the power structures in their organizations,
and two-thirds expect that analytics skills and con-
trol of data will determine which departments and
managers have influence in the future. Many func-
tional areas report increases in influence within
their respective organizations as a result of their
use of analytics. (See Figure 10.) “IT will continue
to play a critical role,” Moody observes, “but it may
have less influence over how data is consumed
across the company.”
Conclusion

FIGURE 10: CONTROLLING DATA IS A SOURCE OF
ORGANIZATIONAL INFLUENCE Departments across
organizations agree that knowledge and information affect
influence.
Percent of respondents who report some or
significant increase in the following forms of influence
40% 50% 60% 90%% 980% 9% 9%% 9% 970% 9% 9%%
930%20%10%0%
Formal authority
Control of knowledge
information
Customer service
Finance
General management
Human resources
Marketing
Operations
Product development
Sales
Supply chain

As more companies draw on analytics for a com-
petitive edge and more departments within a given
organization explore the potential of analytics, several
complementary trends are emerging around an orga-
nization’s new emphasis on data (its own and others’):
1. Businesses that take data seriously organize
themselves around data as if it were a valuable
organizational asset. The sources of data-driven
innovation draw from strong data governance
practices and a propensity and ability to share
data. The growing ranks of analytically mature
organizations, the Analytical Innovators, sug-
gest that more organizations are developing
these practices and propensities. This doesn’t
mean that an organization should rely exclu-
sively on its own data; nor does it mean relying
exclusively on others’ data. Data from other or-
ganizations can augment organizational insights
around customer behavior and market segmen-
tation. Having strong governance practices
that enable data sharing, both within the enter-
prise and across enterprises, may be critical to
innovation that relies on integrated datasets. Ex-
ecutives need to carefully weigh the trade-offs
that come with developing an in-house capabil-
ity for integrating and analyzing datasets versus
relying on external providers who can scale but
may not be able to custom fit — for example,
explain your company’s customer behavior at a

level that has genuine business value. In either
case, creating processes that ensure confidence
in the data is critical.
2. Data sharing requires many parts of an organi-
zation to work together, sometimes in tandem
with other organizations. Awareness is critical
— who else in your organization is working with
data that may intersect with your own uses of
data? Creating mechanisms for understanding
how other business silos use data can deepen
innovation opportunities within a given silo.
Cultural norms that encourage managers to
use these mechanisms are also necessary. Data
sharing, and related practices, are not merely
tactics for deriving business value. To be effec-
tive over time, they must be embedded in the
culture of the organization. Cultural norms for
data sharing will vary depending on whether a
company is in a more or less heavily regulated
industry. But even in the most heavily regulated
industries, such as health care and finance, a
fair amount of data sharing occurs within and
sometimes across the industry. Regulations
and data governance remove uncertainty about
what can be shared, how, and by whom.
3. Innovating with data also means ensuring that
functional areas have the data and analytics
capabilities to apply data to specific business
problems. In some respects, this involves de-
mocratizing access to data. But that is surely not
enough. One oft-cited goal of the chief infor-
mation officer is “to get the right information to

the right person at the right time.” But a critical
flaw with this formulation is that creating busi-
ness value from data drawn from different parts
of an organization or from across organizations
often depends on the right people having the
right information — and these people may have
different views about how to interpret or weight
the information. Unhealthy organizational
behavior about how to adjudicate or manage
diverse interpretations may compromise the
value produced from the data.
4. As organizations everywhere increase their use
of analytics, differentiation will become in-
creasingly important, and elusive. Our research
indicates a rise in the number of organizations
gaining advantage through analytics. But ad-
vantage for one organization in an area means
disadvantage for another organization. As
a result, organizations may decrease activi-
ties where they are not able to gain advantage
in favor of activities where they can obtain
advantage. The upshot: Analytics may help
organizations narrow their strategic focus to
where their advantage is strongest.
Reprint 58380.
Copyright © Massachusetts Institute of Technology, 2017.
All rights reserved.
INSTITUTE INC.

REFERENCES
1. These figures are for the entire Bridgestone North
America retail operation, which includes stores operated
under the Firestone name.
2. Third-party data vendors have, and likely will continue
to have, a large role in helping companies understand
customer behavior. Indeed, Nedbank Group Ltd., the
Johannesburg, South Africa-based financial institution,
offers a data service to its small- and medium-sized mer-
chant customers, using credit and debit card transactional
data. This gives its business customers insights into their
own customers that would have been impossible for
them to do themselves. However, other companies are
becoming less dependent on third-party vendors and are
now developing their own data capabilities to build their
own distinctive perspectives on their own customers.
3. See also B.H. Wixom and J.W. Ross, “How to Monetize
Your Data,” January 9, 2017, http://sloanreview.mit.edu.
4. S. Ransbotham, D. Kiron, and P.K. Prentice, “Beyond
the Hype: The Hard Work Behind Analytics Success,” MIT
Sloan Management Review, March 2016,
https://sloanreview.mit.edu.
5. K. Safdar, “As Gap Struggles, Its Analytical CEO Prizes
Data Over Design,” Wall Street Journal, Nov. 27, 2016.
6. E. Auchard, “HERE, Automakers Team Up to Share Data
on Traffic Conditions,” Sept. 25, 2016, www.reuters.com.

7. L. Winig, GE’s Big Bet on Data and Analytics, MIT Sloan
Management Review, February 18, 2016, https://sloanre-
view.mit.edu.
8. T.H. Davenport, “IT Drinking Its Own Automation Cham-
pagne,” Nov. 10, 2016, http://data-informed.com.
9. J. Manyika, M. Chui, M. Miremadi, J. Bughin, K. George,
P. Willmott, and M. Dewhurst, “A Future That Works: Au-
tomation, Employment, and Productivity,” January 2017,
www.mckinsey.com.
ACKNOWLEDGMENTS
Ravi Bapna, Carlson Chair in Business Analytics
and Information Systems, University of Minnesota
Ken Cartwright, senior director of software devel-
opment, Transaction Network Services
Chris Chen, core technology global engineering
leader, W.L. Gore & Associates
Peter Fader, professor, University of Pennsylvania
Nathan Falkenborg, global cards and loans analyt-
ics leader, HSBC
Alvin Glay, head of digital marketing, Wahoo Fit-
ness
Sean Kent, director, product management, Trans-
action Network Services
Peter Levin, senior research scientist, Intel
Joe Malfesi, vice president, Infrastructure Services,
Transaction Network Services
Kristina McElheran, assistant professor of strategy,
University of Toronto
Keith Moody, director of analytics, Bridgestone
Retail Operations
Omar Selim, CEO, Arabesque Partners

Kevin Swindon, special agent, Federal Bureau of
Investigation
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58380Wx.pdfboilerplate.pdfGlobalAccelerated Innovation: The
New Challenge From ChinaAccelerated Innovation: The New
Challenge From ChinaThe Push to Accelerate InnovationAbout
the ResearchIndustrializing the Innovation ProcessPushing the
Boundaries of Simultaneous EngineeringCycling Rapidly
Through “Launch-Test-Improve”Combining Vertical Hierarchy
With Horizontal FlexibilityImplications for Global
CompetitionResponding to the New China
ChallengeReengineering Established Innovation
ProcessesFocusing R&D Activities on Leveraging Accelerated
Innovation CapabilitiesExploiting the Potential of Alliances
With Chinese PartnersAbout the AuthorsReferences

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