The data directive - The EIU report on how data is driving corporate strategy

The data directive
How data is driving corporate strategy—and what still lies ahead
An Economist Intelligence Unit report
Commissioned by

1© The Economist Intelligence Unit Limited 2013
The data directive: How data is driving corporate strategy—and what still lies ahead
Contents
About the research 2
Executive summary 4
Introduction: The data revolution: optimisation gain, strategic strain? 7
Case study: Turning digital risk into opportunity at Universal Music 8
Functional gains: The organisational data leaders 10
Case study: Optimising sales at Anheuser-Busch 15
Case study: Rethinking customer value at a retail bank 16
Insight-led business? A sector perspective on data 17
Case study: Transitioning from physical products to data services at Xerox 20
The outliers: Who’s ahead on the data game? 21
Noise, overload and trust: The data challenges 26
Case study: Building a data-centric business at Samsung 29
Case study: Cargill, strategy and the rise of the so-called super quants 30
Conclusion: The leadership considerations ahead 31
Appendix: Survey results 33

2 © The Economist Intelligence Unit Limited 2013
About the
research
The data directive is an Economist Intelligence Unit (EIU)
report, commissioned by Wipro. It seeks to explore the degree
to which the ongoing data revolution within business is
delivering truly strategic change within companies, as opposed
to more incremental optimisation gains. Although many of
the issues discussed here stray into the realm of so-called “big
data”, this report is not explicitly focussed on that topic and
does not deal with any technology-related issues. Instead, it
seeks to explore how the wider trend toward a greater reliance
on data is affecting the strategic management of businesses at
a C-suite level, across a range of industries. The findings and
views expressed in this report are those of the EIU alone and do
not necessarily reflect the views of the sponsor.
The research draws on two primary inputs:
l The first is a wide-ranging survey of 318 C-suite executives
or their direct reports, divided between CEOs (13%), CFOs
(20%), COOs (13%), CIOs (12%), CMOs (9%), and other C-suite
roles. From an industry perspective, all major sectors were
represented, with the largest being manufacturing, including
chemicals and aerospace (18%), retail and consumer goods
(11%), technology, media and telecommunications (15%),
financial services (10%), and professional services (9%).
Regionally, respondents hailed from North America (48%),
Europe (29%) and Asia-Pacific (24%).
l The second key input is 20 in-depth interviews with business
executives and experts, as listed below. In addition, we
conducted extensive desk research into the broader topic.
James Watson is the author of the report and David Line is the
editor. Kim Thomas, Sarah Fister Gale, Priyanka Mehra Dayal,
and Fergal Byrne assisted with further interviews. We would
like to thank all survey respondents and interviewees for their
time and insights (listed alphabetically by company name):
l Rajendran S, chief marketing officer, Acer India
l David Johnston, group chief operating officer, Aimia
l David Almeida, vice-president: sales, Anheuser-Busch
l Philip Clement, global chief marketing officer, Aon
l Irvin Newbitt, vice-president: enterprise IT, AstraZeneca
l Rudy Puryear, director and global head: IT practice, Bain Co
l Julian Gray, chief information officer, BP Alternative Energy
l Professor Andy Neely, director, Cambridge Service Alliance
l Mike Balay, vice-president: strategy and business
development, Cargill
l Ged Brannan, head of Coutts Experience, Coutts
l Greg Nichelsen, head of business intelligence, ING Direct
l Christian Nelissen, director: customer analytics and
decisioning, Royal Bank of Scotland
l Asim Warsi, vice president: mobile, Samsung India
l Jason Trost, chief executive officer and co-founder, Smarkets
l Michel Allé, chief financial officer, SNCB Holding
l Kenneth Cukier, data editor, The Economist

l Paul Gathercole, vice-president: digital tools, Universal
Music
l Christa Carone, chief marketing officer, Xerox Corporation
l Chief operating officer, Fortune 100 retail bank
l Chief financial officer, Fortune 100 chemicals business
l Chief financial officer, Fortune 500 retailer

In July 1995, a former hedge fund executive
launched a website that aimed to upend the
traditional way of selling consumer goods,
starting with books but soon expanding into a
diverse array of products. Dubbed Amazon.com,
Jeff Bezos’s business went on to disrupt not only
the way people typically shop, but also the book
publishing industry itself, en route to becoming
the world’s largest online retailer. It now serves
as a prime example of how technology can
enable innovative companies to fundamentally
rethink the way business is done, as opposed to
merely optimising existing processes.
In much the same way that the internet was
the fundamental disruptive technology driving
corporate strategic change in the 1990s,
today the myriad forms of data emerging from
our internet-enabled digital world promise a
similar transformation. Very few executives
need convincing that having more data about
their products, people and processes can be
empowering—and profitable. But how far has
the data-driven revolution gone? How many
companies have followed pioneers like Amazon
into transforming their strategy—and potentially
their industry—through data-driven insights?
This study seeks to examine the progress that
executives have made in using information to
transform their business. It highlights some of
the striking gains that are being achieved so far,
as companies rush to capture all kinds of new
insights. From beer companies like Anheuser-
Busch using data to understand where, how and
when to place its products to optimise sales,
through to businesses like Universal Music using
data to better identify up-and-coming artists.
But the research also uncovers how much work
remains to be done, in terms of genuinely
making use of the transformational potential of
data within business. Right now, however, most
businesses are focused on using this to optimise
their existing approaches and processes; far
fewer have found wholly new ways of operating,
or breakthrough opportunities.
The report’s key findings include:
l A strong relationship between earnings
growth and strategic use of data. Although
correlation does not suggest causality, this study
finds sharp differences between “high growth”
firms, based on their EBITDA performance over
the past three years, as compared to their “no
growth” peers. For example, even though both
sets of firms collect lots of data, high-growth
firms make far better use of it, while no-growth
firms are more likely to find themselves swamped
by the data they hold. Similarly, twice as many
high-growth firms consider themselves highly
effective at extracting insights from data, as
compared to no-growth companies. High-
growth firms have also done more to reform
Executive
summary

their structures and leadership around data,
suggesting greater internal maturity on this
issue. In particular, they are more than twice as
likely to have a well-defined data management
strategy in place.
l All C-suite functions are finding uses for
data, but so far data has proved most valuable
for marketing leaders. From better ways to
segment the customer base, to rethinking the
ideal product mix in a retail store, marketing
leaders are finding wide-ranging uses for
their data to help improve how they market
their company’s wares. Already, 50% of chief
marketing officers say they have tried and found
a clear, positive difference in using data to
improve their understanding of customers, as
just one of a range of successful applications.
This is a markedly higher proportion than their
C-suite peers.
l The financial services sector, technology
companies, and professional services firms
are most prepared for the data age. One of the
key indicators identified within the survey is
the degree to which a company has established
a well-defined data management strategy.
Considering this core metric, three sectors stand
out as being most prepared for the data age: the
financial services sector (where 22% have this in
place); the technology industry (30%); and the
professional services sector (40%). By contrast,
data management strategies are least often found
amongst manufacturers (16%) or retailers (13%).
l Many companies are unsure about
the extent of data-fuelled strategic
transformation within their business.
Companies of all kinds are finding impressive
ways to use data to optimise their sales,
marketing and other functions. Moreover,
68% of respondents think their strategy has
improved in the past two years as a result
of having more data. But only 18% see a
significant improvement in strategy, and few
have found ways to use data to make a genuinely
transformational shift in the business. Some
35% of executives agree that data has been more
useful with operational choices and actions,
rather than strategic ones. Just 22% disagree,
while 41% are unsure. Moreover, while 72% of
companies regard themselves as effective at
extracting strategic insights from data, only
12% of executives polled for this study consider
their companies to be “highly effective” in this
regard. Similarly, for what is usually a confident
executive audience, unusually high proportions
of survey respondents regard themselves as
below average on this issue, including 20% of
COOs and 17% of CFOs.
l Businesses are stockpiling an ever-growing
range of data and expect data gathering to
continue to expand rapidly. Whether social
media sentiment, machine-generated data via
sensors, staff emails, market data or otherwise,
firms of all shapes and sizes are now collecting
more information than ever before. At least seven
in ten companies collect syndicated third-party
data, such as weather information (72%), or
government data (70%), while many gather
anything from internal staff data (66%) to some
kind of location-based information (41%), among
many other types. Two-thirds of business leaders
say the range and types of data have expanded
in the past two years, while about three-quarters
expect this data stockpiling to expand yet further
in the coming two years.
l Working out which data matters most is the
top challenge for firms, but there are wide-
ranging issues to overcome. For companies
seeking to gain more strategic insights from
their data, many hurdles await. Whether
organisational silos, a lack of skills, the usual
disconnects between IT and the business, or
worries over data quality, few consider the
challenges and gaps easy to bridge. But clarity
on which data matters most, amidst the data
stockpiling now under way, is what tops the list
of barriers, according to 40% of respondents.
Furthermore, 34% of executives worry that
the quality of their decisions are actually being
impaired by data overload.

l Those companies that claim to be best
at extracting insights from data are not
necessarily looking to their CIOs to lead on this
initiative. While the CIO is the default leader of
choice across the vast majority of firms surveyed,
there is a spike to preference towards the CEO and
other C-suite leaders among those companies
best at extracting strategic insights from data.
This group is also nearly eight times as likely to
have a data management strategy; and four times
as likely to have changed the way they make
strategic decisions. As with the high-growth firms
sub-set, they are typically far more likely to see
scope for radical transformation of the business
through better use of data.

This report agrees wholeheartedly that many of
these new bets on data will pay off. However,
for the majority of businesses, much evolution
lies ahead. Philip Clement, the global chief
marketing officer of Aon, an insurance company,
gives the analogy of how Amazon.com, in the
mid-1990s, promised to become one of the
biggest global retailers. It seemed like a joke at
the time, he says. “It was true, but it just took 15
years longer than anyone imagined.”
This is not to suggest that the issue of data is
not a priority in boardroom discussions today.
This survey of over 300 C-suite executives,
spanning a range of industries and functions,
suggests that only a tiny fraction (3%) are not
currently prioritising the collection of data.
Whether social media feeds, machine-generated
data via sensors and telematics, call-centre
recordings, staff emails, syndicated information,
or data from other more exotic sources, a high
proportion of companies are storing increasing
1
“Big data: The next frontier
for innovation, competition
and productivity”, McKinsey
Global Institute, May 2011
The data revolution: optimisation gain,
strategic strain?
There is much hype about the strategic impact of data, and many new bets on it will
undoubtedly pay off. However, while some companies are making strategic changes based
on data, as distinct from optimisation gains, many are yet to do so.
Introduction
Discussions about the transformational power
of data have been hard to miss in recent years.
Indeed, speculation over the impact of all aspects
of data, including so-called “big data”, has
become so frequent and over-hyped that it is
difficult to separate the signal from the noise.
It is now two years since the McKinsey Global
Institute released its landmark study on the
subject, which marked an inflection point for
business.1 McKinsey argued that the subject was
now relevant “for leaders across every sector”,
with business “on the cusp of a tremendous wave
of innovation, productivity and growth” as a
result of data.
In many respects, the current data gold rush
is reminiscent of the latter part of the 1990s
and the rush to get to grips with the emerging
Internet era. Today, just as then, profound
promises are being made about the strategic
impact of data, but for the time being examples
of the potential coming to fruition remain few.

amounts of data in the belief that it will be of
value to them. McKinsey’s 2011 report already
highlighted that 15 out of 17 sectors in the US
had more data stored per company than the
information contained in the US Library of
Congress.
Moreover, while it is true that 72% of executives
polled consider themselves effective in translating
these data sources into insightful information
that can drive changes of strategy (Figure 1),
only a small minority (12%) consider themselves
“highly effective” in this regard. As this report
will argue, there are many impressive examples of
how companies are using data to optimise many
aspects of their business. However, there is far less
evidence that corporate leaders are systematically
using it to strategically transform their companies
or business models. “This is the continuum,”
argues Kenneth Cukier, The Economist’s data
correspondent and author of the recently released
Big data,2 “By being a data-driven company, are
you using data to optimise what you’re doing
today, or are you taking this information to do
something totally new?”
From data to insights
Organisational effectiveness at translating new data sources/types of data
into insightful information to drive strategic change
(% respondents)
Figure 1
Highly effective
Somewhat effective
Somewhat ineffective
Highly ineffective
Neither effective nor ineffective
12
60
20
6
2
Source: Economist Intelligence Unit survey
2
Big Data: A Revolution
That Will Transform How We
Live, Work, and Think, Viktor
Mayer-Schonberger and
Kenneth Cukier, March 2013
What a difference a few years make. Back
in 2006, Universal Music made headlines
with a lawsuit filed against MySpace, the
entertainment-oriented social network,
for copyright infringement. Since then, the
world of music has gone increasingly digital,
with wholly new music distribution models
emerging, even as MySpace has faded. But
Universal Music has been constantly evolving
over the past decade too, using data to help it
shift from a defensive risk-oriented strategy to
an offensive, opportunity-led one.
In many respects, this is a very literal
transition: the same data team that once
worked to contain digital piracy risks has been
transformed into a source of insight for signing
and marketing new hits. This Digital Tools team,
lead by Paul Gathercole, has spearheaded the
creation of a powerful data platform, which now
helps to guide executives on their decisions and
investments. This platform draws on diverse
data, from online music services’ streaming
data, to social media and web sentiment
analysis, to physical sales, to stars’ concert and
TV appearances, among other things, and it is
reshaping how the company works and thinks.
“Our data team now handles queries from
all parts of the business, such as asking for a
precise breakdown of how popular a given artist
might be across various digital and analogue
channels,” explains Mr Gathercole.
As this data platform has grown in scale and
capability, it is in turn changing the business.
“The key thing that can change people’s
perceptions is the nuance you can get from
the data,” says Mr Gathercole. “It’s a long
path, but Universal has changed a lot in the
last three years and the degree of openness to
data has changed dramatically. The questions
we receive are better and meatier, which is
indicative of an organisation educating itself
and raising its ability to use and converse
about data meaningfully.”
Case study: Turning digital risk into opportunity
at Universal Music

Of course, there are many examples of companies
who’ve made data the core of their business
models and who couldn’t exist without it. These
firms are predominantly within the high-tech
sector and financial services sector—whether
Google, Amazon, or one of many hedge funds
that compete on their ability to uncover insights
from information. Overall, a small core of
businesses are genuinely rethinking what data
means for them, and how to organise themselves
around it. But apart from these outliers, there
are relatively few that have worked out how to
truly reshape their business around data. Quite
simply, for many companies today, the real data
revolution still lies ahead. “This is the breakout
moment,” believes Mr Cukier. “Everyone has been
learning about it, figuring out what it can do, but
for most companies, they haven’t done it yet.”

Across the sweep of companies surveyed for this
report, a diverse array of data sources are being
tapped into, as the rush to stockpile information
gathers pace. From syndicated third-party data,
such as market data (which 72% of companies
say they collect; Figure 2), to open data supplied
by governments (70% collect), to staff emails
or messaging (66% collect), to contact centre
data (62% collect), collection from all sources
is on the rise. Customer-related information is
also increasingly common, from social media
feeds (42% collect) through to location-based
information of some kind (41% collect).
And this is hardly all. When asked to specify
some of the types of information collected,
respondents listed examples ranging from
competitor information and web sentiment
data, through to point of sale feeds, machine-
to-machine data, news and media, loyalty
information, search engine analysis, census
bureau data, and RFID tags, to name just a few.
Overall, two-thirds (67%) of executives say the
range and types of data they rely on to make
decisions have expanded over the past two years;
and three-quarters (74%) expect this trend to
Functional gains: The organisational
data leaders1
So far, CMOs are reporting the most clear gains from their companies’ data strategies.
From data to insights
Non-traditional data collection trends
(% respondents)
Figure 2
Syndicated data from third-party data providers
(eg, market data, weather, etc)
Open data (eg, data released by governments)
Contact centre data (eg, audio conversations,
text chats, customer emails, etc)
Machine generated data (eg, sensors, smart
grid, RFID, network logs, telematics, etc)
Social media (eg, Facebook, Twitter, YouTube,
blogs, etc)
Location-based information (eg, GPS, mobile
logins, etc)
Staff data (eg, Emails, calendars, instant
messaging, etc)
72.2 12 11.7 4.1
70.3 9.5 15.5 4.7
65.5 10.8 18 5.7
62 11.1 23.4 3.5
51.1 29.212.7 7
42 3617.7 4.4
41 31.422.5 5.1
Collects Plans to collect Does not collect Don’t know

continue over the coming two years. In short, it is
difficult to find examples of data that companies
do not seek to squirrel away for possible use. But
which functions within the business are actually
making real use of this data?
A marketer’s delight
Of all the C-suite roles examined for this report,
the function that stands out as the single
largest successful user of data appears to be
the chief marketing officer. Of those polled,
50% report having tested and seen a clear,
positive difference in using data to improve their
understanding and segmentation of customers
(Figure 3). A further 40% see similar merit in
helping to increase sales, among various other
benefits. This degree of usage is well ahead of
nearly all other C-suite roles.
This is consistent with the views of many
marketing and sales leaders interviewed for
this report. Ged Brannan, Head of Coutts
Experience at the private bank, says that his firm
has invested significantly in a new technology
platform to create the opportunity to capture and
leverage data, as part of a new strategic focus
under its latest CEO. The opportunity arising
from this investment relates to significantly
improving its ability to make use of both
financial and non-financial information to better
understand clients’ behavioural patterns. “We
want to serve the clients in the markets we want
to be in, rather than trying to be all things to all
people,” explains Mr Brannan. The core of this is
still coming in place, but Mr Brannan is working
to create a more evidence-based management
approach. One example of the type of opportunity
that exists could be tracking the working habits
of high performing staff, to discern if any traits
can be more widely adapted elsewhere.
At Aon, Mr Clement says the scope of how data
can improve marketing is “absolutely huge,”
as he puts it. Automotive marketers would use
analytics to simply try to identify the typical
characteristics of consumers who might, say, be
interested in a car type - like muscle cars. “Now,
it’s just the opposite, you literally know who the
person is that likes muscle cars,” he says. Across a
range of interviews, many such examples emerge,
especially within customer-centric organisations,
of how data are helping improve customer
understanding and, ultimately, sales.
Even outside of the sales and marketing
function, other executives are often involved
in supporting such initiatives. At Royal Bank of
Scotland (RBS), some of the most high-profile,
data-centric initiatives of the past few years have
related to trying to gain a far more advanced
understanding of customers. “There’s been a
huge shift in the organisation, driven by an
understanding of how customers bring value to
the bank. It might sound obvious, but once you
start seeing these values, you start to actually
Where data delivers for CMOs
Top strategic aspects of role in which data has made biggest positive
difference
(% respondents selecting in top three choices)
Figure 3
Increasing customer understanding and
segmentation
Increasing sales
Improving return on marketing
Optimising marketing mix
Improving cross channel experience
Other, please specify
None of the above
Improving pricing optimisation
Increasing cross-selling efforts
Improving customer service
Helping assess potential demand for new
products/services
50
40
37
27
23
17
17
17
13
7
0
7
Gaining better grasp of competitive landscape

Figure 4: Where data delivers…
Top strategic aspects of role in which data has made biggest positive difference
CEO % CFO % COO %
Geographic expansion 40.0 Improving scenario planning and forecasting 40.3 Improving operational efficiency 48.8
Identifying new business models 35.0 Improving financial close management 33.9 Improving product research and/or
development
39.0
Gaining better insights into the
competitive landscape
32.5 Improving profitability 32.3 Improving product/service quality 36.6
CMO % CSO % CIO/CTO %
Increasing customer understanding and
segmentation
50.0 Identifying new business models 31.8 Managing/controlling IT spending 45.8
Increasing sales 40.0 Identifying new revenue streams 31.8 Allowing for better business processes 44.1
Helping assess potential demand for new
products/services
36.7 Identifying new markets 31.8 Improving identification of risks / risk
management
28.8
Figure 5: …and where it has greatest potential to deliver
Top strategic aspects of role in which data has potential to make biggest difference
CEO % CFO % COO %
Improving risk management 32.5 Improving financial risk management 24.2 Delivering process innovation 31.7
Improving organisational productivity 30.0 Improving corporate reporting / dashboards 22.6 Improving resource usage 31.7
Identifying new revenue streams 25.0 Identifying cost efficiencies 21.0 Improving inventory management 24.4
CMO % CSO % CIO/CTO %
Increasing cross-selling efforts 33.3 Improving medium-/long-term planning 36.4 Identifying business transformation
opportunities
25.4
Optimising marketing mix 26.7 Improving scenario planning 31.8 Improving support for data-enabled
strategic decisions across the business
23.7
Improving pricing optimisation 26.7 Gaining better grasp of competitive
landscape
27.3 Improving scenario planning and
forecasting
22.0
understand all sorts of interesting things,”
explains Christian Nelissen, the bank’s director
for customer analytics and decisioning. This helps
the bank rethink how it incentivises behaviour
internally, to focus more on building the value
of the customer relationship through meeting
their needs and working to keep them within the
bank. “This means staff become interested in
how happy the customer is, which changes the
dynamic,” says Mr Nelissen.
Professor Andy Neely, director of the Cambridge
Service Alliance, a group of companies and
academics, adds that data provides vast
opportunity to innovate on service delivery and
sales. “It will go a long way beyond ‘how do I sell
more strawberry pop tarts’ to actually ‘how do I
really deliver value to my customers’,” he says.
Back-office boost
Marketing is currently the most immediately
visible user of data but other functions in the
business are making wide-ranging use of data
to support myriad initiatives. As businesses
globalise, for example, data has made a clear
positive difference to CEOs in how they plan
for geographic expansion (40% say they’ve
benefitted from this). For four in ten CFOs, it’s
opened up a raft of possibilities around scenarios
and forecasting. Nearly half (49%) of COOs say
data has helped to bolster operational efficiency,

while about one-third (32%) of chief strategy
officers are using it to help identify new revenue
streams or markets or business models. And many
CIOs are using it to control IT spending or improve
business processes (see Figures 4 and 5 for a full
overview).
Across varying roles, there are wide-ranging
examples of how data are being used. The CFO
of a Fortune 100 chemicals company, who asked
to remain anonymous due to the sensitive
nature of the work, says his function has been
significantly increasing its use of data to improve
its competitive intelligence and thus pricing
structures. “As we’ve begun to learn more
about data opportunities, it’s allowed us to
better analyse the competitive set that we play
against in terms of pricing, cost structures, the
ingredients of their products versus ours, cost
positions, and market approaches,” he explains.
This is all analysed in the pursuit of competitive
gaps. Further downstream, it also drives other
finance-led forecasting, such as how projected
demand is affected by price shifts, the ability to
supply that demand, and where excess capacity
might lie. “It gives us a better sense of our
capacity to respond,” he says.
At BP Alternative Energy, CIO Julian Gray notes
that his company is a huge user of data, across
nearly every part of the business, from seismic
modelling in its upstream exploration, through to
in-depth farming and meteorological data in its
biofuels business. “All of those areas are growing
immensely,” he says. Inevitably, though, some
functional roles take differing views on data. The
CFO, for example, is often closely engaged with
data issues, given the nature of the role. But as
our survey suggests, CFOs often seek to act as
the voice of caution on data—many more see a
risk that data overload provides false comfort to
the business, for example. They are also far more
likely to note difficulties in identifying which data
are truly relevant to strategy, and which to ignore
(a subsequent report will examine the role of the
CFO in more depth).
Tactical or strategic?
This chapter has highlighted a few of the key
areas where C-suite leaders are making use of
Uncharacteristically uncertain
Usage of data compared to industry peers
(% respondents)
Figure 6
CIO/CTO
CSO
COO
CFO
CEO
CMO
1.7 45.8 40.7 3.43.4 6.1
13.6 31.840.9 9.1 4.5
6.7 50 30 10 3.3
39 36.6 17.1 2.4 4.9
8.1 14.5 1.625.8 45.2 4.8
15.4 35.9 10.335.9 2.6
Significantly above average Somewhat above average About average
Somewhat below average Significantly below average Don’t know

data today. But two things stand out from an
analysis of this. The first is that for a cohort of
typically confident and bullish executives, today’s
leaders are unusually humble about their grasp
of data. Just 15% of CEOs consider themselves
“significantly above average” in their use of
data, while nearly the same amount think they’re
below average (Figure 6). One in five COOs regard
themselves as below average, as do 17% of
CFOs—all unusually high proportions.
The second, more important point that stands
out from the survey, and which is confirmed
by interviews for this report, is that the use of
data today often centres on optimising existing
processes and tactics, rather than driving
genuine strategic transformation.
For example, although one in two executives
agree that strategic decision-making has
improved since they began prioritising data,
there is far less certainly about the degree
to which data are more useful for day-to-day
operational issues versus genuinely strategic
ones. About one in three (35%) agree that
greater use of data has been more pertinent
for operational choices and actions, rather
than strategic ones, while only about one-fifth
(22%) disagree. The biggest proportion (41%)
remains undecided. “It’s within the day-to-day
perspective where the data use is more heavily
focussed right now, as opposed to the longer-
term strategic planning,” says the chemical
company CFO.
David Johnston, now group chief operating
officer at Aimia3, a loyalty-scheme management
company, agrees that a lot of the data in business
today is being used to help optimise processes,
versus more fundamental change. Nevertheless,
he sees a growing number of examples where it is
helping spark new products, ideas or businesses.
Take one of his company’s latest offerings, a
partnership between Yahoo and Aimia’s loyalty
brand Nectar, which offers customers points
in exchange for search-related advertisements
that are far more precisely targeted. “We can
deliver far more targeted adverts, because we
know your purchasing behaviour,” explains
Mr Johnston. This also enables advertisers to
more explicitly match their before and after
advertising performance against actual sales for
a given audience. “This is one of several other
opportunities and new business models we see
out there,” says Mr Johnston.
Data-driven strategy
Corporate planning and decision making
(% respondents)
Figure 7
Highly data-driven
Occasionally or partly data-driven
Somewhat data-driven
32
25
21
25
5
5
42
45
Rarely data-driven
Strategic planning Strategic decision making
3
At the time of being
interviewed, Mr Johnston
was the company’s
executive vice president,
president and chief
executive officer for EMEA.

While some companies are basing strategic
planning and decision making on data, some
are yet to leverage data for strategic use. About
one-third of executives overall consider their
company’s strategic planning to be highly data
driven, although 42% say it is somewhat data-
driven (Figure 7). Fewer think the same with
regards to strategic decision-making, where
just 25% say it is “highly” data-driven and 30%
reckon this is only occasionally or rarely the
case. This is not to say that they don’t recognise
the importance: four in ten consider increased
data volumes to be “highly” important for such
strategic issues, while only a tiny minority think
it unimportant. Instead, this further showcases
the fact that few companies have got to grips with
how to rethink their companies around data, even
as they rush to collect more of it.
For global beer brand Anheuser-Busch, the
science of selling beer has long been as
rigorous a process as its efforts to create
compelling new craft brews. As early as 2004,
the company was cited as a leader for its effort
to create BudNet, its data platform for tracking
sales. But over the past two years, explains
David Almeida, the company’s vice-president of
sales, it has invested substantially to develop
this platform further.
“It now means that I can look at any given
account, see a detailed plan of the store
and all our SKUs [stock-keeping units], with
related data on local households from a range
of sources. I can then use the data to choose
the optimal assortment of beer for that store,
knowing which SKUs will perform better, what
the best display options are, and the best
configuration of brands for that demographic,”
explains Mr Almeida. “We think this data will
separate us from our competitors.”
Of course, as he readily highlights, there’s a
constant challenge of ensuring that this data
doesn’t overwhelm sales. Or, more crucially,
that niche fads are not chased at the expense
of mainstream sales. “One of the biggest
challenges has been the move towards growing
craft segments, which leads to more SKUs
and more choice, but which pose a complexity
challenge,” he says. By blindly chasing this
high-growth, but niche, market, Mr Almeida
says the industry has hurt sales by crowding
out shelf space for mainstream brands. “Our
analysis shows that even craft beer buyers only
buy such brands 30-40% of the time. For the
rest, they’re buying premium or discount beers,
so the craft infatuation loses shoppers when
core brands are out of stock more often.”
All this has led to greater refinement of the
firm’s portfolio approach across its 500 SKUs.
“I’m trying to generate an environment where
the vast majority of our decisions are based
on data, and knowing what will work,” says Mr
Almeida.
Case study: Optimising sales at Anheuser-Busch

All businesses have a core of most valuable
clients. The challenge lies in identifying who
these are, and then considering how best to
reshape the business around them. This has
been one of several data-intensive exercises
that a major European retail bank has gone
through in recent years.
“We all know the 80:20 rule, but we’ve reviewed
the distribution of our revenues across our
customer base, in order to split these into
distinct cells,” explains the bank’s COO, who
asked to remain anonymous given the sensitive
nature of its work. The resulting findings were
“pretty astounding”, he says, and have reset
long-held assumptions within the leadership
team by showing the high concentration of
revenue within a relatively small number of
customers. “It really highlights the fact that
about half of your customer base essentially
generates no revenue,” he explains.
Many companies segment customers like this,
but today’s technology is allowing for far
deeper analysis. “We analysed the bottom 50%
of customers, to see what products they use,
how many ATM withdrawals they make and how
many phone calls we get from them. It proves
that these customers take up a substantial
amount of operational capacity,” says the COO.
“The normal reaction is that you always need
the volume to cover your fixed costs, but that’s
absolutely wrong. They simply do not generate
enough revenue to do so.”
This poses a challenge to management. Given
that no bank could “fire” half of its customer
base, how can this issue be addressed? How
does the bank identify valuable clients from
those who only use the bank as a secondary
account? And how do you restructure your
business for those clients that do matter most?
These are the kinds of questions that are now
being grappled with internally. But it is clear
that such insights have reset core internal
assumptions: “Seeing results that no-one
thought possible has really changed mindsets,”
says the COO. “And we all have preset ideas
about what reality looks like.”
Case study: Rethinking customer value in retail
banking

Insight-led business? A sector
perspective on data2
Although it is not difficult to find examples of
data-centric companies in nearly any industry, it
is also obvious that some sectors have embraced
data more rapidly than others. The most high-
profile are Silicon Valley’s high-tech companies.
But they are not alone. As this survey highlights,
many companies within financial services and
professional services are highly proficient in
making use of data. Overall, these three sectors
are the most likely to have created a well-defined
data management strategy, or are marshalling
resources toward this (see Figure 8).
At Aon, for example, data continues to reshape
how its products and services are conceived and
developed. Whether proxy data on housing starts
feeding into construction insurance, or more
granular life insurance models based on a better
High-tech, financial services and professional services companies are the most advanced
in using data and hence are getting more out of it, while some sectors such as retail are
constrained by poor industry conditions.
Data-driven industries
(% respondents)
Figure 8
Manufacturing Financial services Professional services Retail and consumer goods Technology, media and telecoms
0
10
20
30
40
50
60
15.8
40
13.3
21.9
30.4
36.8
56.3
30
45.7
35.1
12.5
20
6.7
6.7
8.8
6.5
37.8
15.2
6.3
3.5
3.1
3.3
2.2
2.2
40
We have a well-defined
data management
strategy that focuses
resources on collecting
and analysing the most
valuable data
We understand the value
of our data and are
marshalling resources
to take better advantage
of them
We collect a large amount
of data but do not consistently
maximise their value
We collect data but they
are severely
underutilised
We do not prioritise
data collection

grasp of life expectancy, its products are built
on the back of data. “Ten years ago we already
had an unbelievable ability to deal with data,
but our business decision making and acumen
hadn’t caught up with it,” says Mr Clement. “It is
changing our business model and the fact is that
data and the analytics on that data are becoming
of equal value to the work we have traditionally
done. This is a fundamental change for a company
like ours.”
This in turn is reflected in Aon’s workforce.
In contrast to a decade ago, statisticians,
actuaries, meteorologists, and other data
specialists make up a far higher proportion of
its staff. At a macro level, demand for data skills
is currently concentrated in a few industries,
such as finance or biotech (with consequent
shortfalls in such talent widely forecast).3
The pharmaceutical company AstraZeneca, for
example, is now reshaping both its process for
discovering and developing new drugs, as well
as the way it commercialises these, around data.
This is drawing on wide-ranging and complex
information, from individuals’ genetic data at
one extreme, through to industry insights about
prescriptions and clinical efficacy, explains
Irvin Newbitt, the company’s vice-president for
enterprise IT.
Still getting started
By contrast, other sectors are not yet consistently
prioritising data-led initiatives or skills. For
example, our survey shows that while both
the manufacturing sector and the retail and
consumer goods sector collect large volumes of
data, they more often admit to not consistently
maximising use of this resource than their peers
in finance or high-tech.
Across a range of data types, barring some
exceptions, these sectors less often routinely
collect information; and when they do, they less
frequently analyse it for insight. For example,
The collectors
Types of data collected and analysed, by industry
(% respondents)
Figure 9
Manufacturing Financial services Professional services Retail and consumer goods Technology, media and telecoms
0
10
20
30
40
50
60
70
80
3.5
23.3
11.4
15.6
37.5
23.3
25
31.9
10.5
14
9.7
6.7
15.9
12.8
40.4
35.5
36.7
34.1
55.3
10.5
25
13.3
20.9
19.1
17.5
15.6
30
14
42.6
17.5
15.6
20
11.6
31.9
29.8
46.9
36.7
48.8
48.9
26.3
18.8
30
32.6
40.4
38.6
46.9
36.7
20.9
36.2
22.8
9.4
13.8
13.3
27.7
49.1
71.9
62.1
44.4
48.9
16.1
9.4
17.2
17.8
21.3
55.4
65.6
58.6
60
55.3
44.7
Collects Collects
and analyses
Social media (eg,
Facebook, Twitter,
YouTube, blogs etc)
Machine generated
data (eg, sensors,
smart grid, RFID,
network logs,
telematics, etc)
Location-based
information (eg,
GPS, mobile
logins, etc)
Contact centre data
(eg, audio
conversation, text
chats, customer
emails, etc)
Staff data (eg,
Emails, calendars,
instant messaging,
etc)
Open data (eg,
data released by
governments)
Syndicated data
from third-party
data providers (eg,
market data
weather, etc)
Collects Collects
and analyses
Collects Collects
and analyses
Collects Collects
and analyses
Collects Collects
and analyses
Collects Collects
and analyses
Collects Collects
and analyses
3
For more on this, see “Big
data: The next frontier for
innovation, competition
and productivity”, McKinsey
Global Institute, May 2011

nearly four in ten retailers have no plans to
collect social media data, while a further 23% are
still putting collection plans in place. While one-
quarter do collect and analyse such information,
this is still well below the rate that both financial
services firms and technology companies do
(Figure 9).
Not all of this is through a lack of desire, but
it also reflects the fact that creating a leading
data competency requires investment in both
technology and skills, which lower-margin sectors
will find more challenging. “We see the need to
move into the 21st century and a much more
advanced IT setup, which we know holds huge
possibilities,” explains the CFO of a major Spanish
retailer, who asked not to be named, “but the fact
is that these are not the best times, which makes
the investment into the technology, and the
talent needed to exploit that, difficult to justify.”
At an aggregate level, executives from a wide
range of sectors see scope to bolster their
company’s strategy on the back of a greater
reliance on data. Financial and professional
services firms are typically ahead on this and thus
have a far higher proportion of executives who
admit to seeing the benefits. Others, though, do
hold greater hope for the future as they get to
grips with the issue (Figure 10).
Even the leaders have a lot of headway still to
make, reminds RBS’s Mr Nelissen. “There’s a huge
swathe of companies who would like to do this
better and are not quite sure what to do next,
but recognise they need to,” he says. “On a two
by two grid, where one axis is the amount of data
and other is the use of data, companies like eBay
and Amazon are industry leaders on using data,
but they only have a very partial set of data about
customers. We’re at the other end, we have a huge
amount of data on customers, but are still moving
towards using it more.”
Often, this requires the company to gain an
internal recognition of the value of data. “The
fact is we have all the data, but we didn’t put any
value on that data,” says Mr Nelissen. “That’s
crazy, because one of the most valuable assets
you have is knowing your customer. But that’s
Strategic benefits
Respondents seeing/expecting significant improvement in strategy as a result of having more data
(% respondents)
Figure 10
Manufacturing
Professional services
Financial services
10.5
20
27.6
26.7
19.1
43.5
11.1
22.7
25
19.4
Retail and consumer goods
Technology, media and telecoms
Prior 1-2 years Next 1-2 years

Xerox offers a compelling example of how
data can shift from being an optimisation
tool to something that can unlock wholly
new offerings. It increasingly draws on
data to bolster how it sells its printers and
business services—helping decide the mix of
sales people in a given area, for example, or
the number of channel partners needed to
maximise marketing in a region. “These are
important sales strategies that we’re now able
to really fine-tune because we have access to
more insightful data,” explains Christa Carone,
the company’s chief marketing officer.
But as the world becomes more digital, data
is also helping refine the company’s pitch.
For example, its managed services division
conducts analytics on its customers’ usage
patterns, from types and volumes of printing
needed, through to what features are in
demand across varying departments. This in
turn has reshaped the firm’s value proposition
in its printer business and how it handles
corporate proposals, by helping highlight
wastage and cost saving opportunities.
So far, so tactical. But as data becomes part of
the lifeblood of the company, it is also opening
the door to new kinds of offerings. For example,
Xerox already handles various city services,
such as processing speeding tickets. This has in
turn led to a project with the city of Los Angeles
to conduct analysis of how to optimise the time
that people take to find a parking spot. “We’re
studying traffic and parking patterns and using
that to create a more dynamic pricing system
for parking, based on supply and demand,”
says Ms Carone. This is now just one of a range
of service offerings being rolled out to other
cities, as part of the company’s wider transition
into a services business. Today, more than half
of Xerox’s revenue comes from services, and
the company has set a goal of getting 75% of its
revenue from such services by 2017.
Case study: Transitioning from physical products
to data services at Xerox
absolutely changed, because everyone gets it
now, they understand the value,” he says.
Indeed, Rudy Puryear, global head of the IT
practice at Bain Company, a consulting firm,
argues that while data doesn’t appear much on
the balance sheet of any company, CEOs ought
to be considering it in that way. “They have
to think about the data, and the health and
accessibility of the data, as either an asset that
is deteriorating or increasing in value as you add
more to it,” he says.

As many experts would insist, building a data-
centric business is hardly a new phenomenon.
The poster company of the data era, Google,
will celebrate its 15th birthday this year. Even
so, those companies that have used data to
strategically transform their businesses remain a
relative minority today. But as investment grows
into the realm of data, it is inevitable that various
companies have moved further, and faster, to
turn data into a genuine competitive advantage.
To try to unpick this, we have looked for
distinctions between those companies that have
outperformed the market over the past—where
average EBITDA growth, over the past three
financial years, has exceeded 10% (high-growth
firms)—against those who’ve experienced zero
EBITDA growth, or have declined (no-growth
firms). Of course, such correlations cannot
suggest causality, but it is nonetheless instructive
to compare how high-performing firms differ.
From this analysis, five key points emerge:
1. High-growth firms make better use of their
data. All firms collect data, but high-growth
firms are far more successful at turning this
information into something useful to drive
new strategies, or changes in strategy. In fact,
across several areas of data, such as machine-
generated data or location-based information,
no-growth firms collect easily as much data
as high-growth firms do. But the latter are far
more likely to actually analyse the data being
collected. For example, while 55% of no-growth
firms collect their contact centre data and 66%
of high-growth firms do the same, only about
half of the former that collect data actually
analyse it, versus three-quarters of the latter.
Nearly twice as many no-growth firms admit
to collecting lots of data but not consistently
maximising it (38% versus 20%). Furthermore,
among high-growth firms, 15% consider
themselves highly effective at extracting useful
insights from this analysis, about twice the rate
of their no-growth rivals (7%). And while 17% of
no-growth firms rate themselves as ineffective at
this, just 4% of high-growth firms say the same
(Figure 12).
2. No-growth firms are more likely to run into
data paralysis. While executives at both sets of
companies consider their decision-making to
be based on data, rather than purely instinct,
high-growth firms are far more likely to believe
that key characteristics of their strategic change
initiatives have improved as a result of data.
Twice as many (62% versus 31%) believe the
speed of change has improved, while many more
also think the quality or effectiveness of change
is higher (about 58% versus 38%; Figure 13).
Crucially, while many more high-growth firms see
improved decision-making on the back of being
more data-driven (60% versus 38%), no-growth
companies are far more likely to be worried
The outliers: Who’s ahead on the data
game?3
There is a clear correlation between high-growth companies and use of data. Undoubtedly
key to their success is the fact they have done more to reorganise their structures and
leadership around data and to introduce data-management strategies.

Making better use of data
Types of data collected and analysed, by firm performance
(% respondents)
Figure 11
High-growth firms No-growth firms
0
10
20
30
40
50
60
Collects Collects
and analyses
Social media (eg,
Facebook, Twitter,
YouTube, blogs etc)
Machine generated
data (eg, sensors,
smart grid, RFID,
network logs,
telematics, etc)
Location-based
information (eg,
GPS, mobile
logins, etc)
Contact centre data
(eg, audio
conversation, text
chats, customer
emails, etc)
Staff data (eg,
Emails, calendars,
instant messaging,
etc)
Open data (eg,
data released by
governments)
Syndicated data
from third-party
data providers (eg,
market data
weather, etc)
Collects Collects
and analyses
Collects Collects
and analyses
Collects Collects
and analyses
Collects Collects
and analyses
Collects Collects
and analyses
Collects Collects
and analyses
16.5
14.3
33.8
11.9
10.7
14.3
46.6
33.3
16
11.9
26
16.7
16.7
26.2
49.2
28.6
25.8
28.6
45.5
23.8
23.5
21.4
53.8
35.7
18.2
21.4
58.3
42.9
More and more effective
Organisational effectiveness at translating new data sources/types of data into insightful information
to drive strategic change
(% respondents)
Figure 12
No-growth firms
High-growth firms
507.1 26.2 9.5 7.1
15 66.2 15 3
0.8
High effective Somewhat effective Neither effective nor ineffective
Somewhat ineffective Highly ineffective
about the quality of decisions being impaired by
information overload (45% versus 31%).
3. While no-growth firms worry about the
unknown, high-growth firms worry about how
to resource their data plans. When considering
the barriers ahead on data issues, there are
many commonalities between weaker and
stronger firms. Both worry about the challenge
of assessing which data are truly useful, data
quality issues, and the related technology
problems. But while high-growth firms are far
more likely to raise concerns about a lack of
necessary skills and expertise, no-growth firms
are in turn far more likely to express a concern
about a fear of the unknown (Figure 14).

Change for the better
Respondents citing improvements in aspects of strategic change as a result of having more data
(% respondents)
Figure 13
No-growth firms
High-growth firms
38.1
38.1
57.9
61.9
31
55.7
Quality of changes made Effectiveness of change Speed of change
4. High-growth firms have done more to
reform their structure and leadership around
data. High-growth firms are more likely to
have changed the way they handle strategic
decisions, as a result of data. For example,
twice as many (46% versus 23%) no-growth
firms say they’ve not changed, and don’t even
plan to change in the coming year or two, the
way they tackle such decisions. Meanwhile,
far more executives at high-growth firms see a
need to radically transform their management
techniques to keep pace with what technology
is making possible (62% versus 33%). And while
half of high-growth firms are rethinking their
approaches to strategy and decision-making to
make it more data driven, just 36% of no-growth
firms are doing the same.
5. High-growth firms more often have a
solid data management strategy in place. A
robust data platform often appears to be the
foundation upon which more strategic use of
data is made. As such, high-growth firms are 2.5
times more likely to have a well-defined data-
management strategy that focuses on collecting
and analysing the most useful data. Looking
specifically at the sample of executives who
say their firm has a strong data strategy, these
companies are typically at least twice as likely to
be more data-driven in their decision-making,
and are about three times as likely to think
their strategy has improved as a result of data.
And they are about four times as likely to have
converted information into insights (Figure 16).
This correlation is observed elsewhere, including
within research conducted by Bain Company.4
“We’ve seen a strong correlation between
Barriers to using data in strategy
Top three barriers to effective use of larger data volumes in strategic
planning or decision making
Figure 14
0
10
20
30
40
50
Data quality
issues
Difficulties in
assessing which
data is truly useful
(data overload)
Lack of
necessary
skills/
expertise
Difficulties in
assessing which
data is truly useful
(data overload)
Data quality
issues
Fear of the
unknow among
key managers
41
37.3
35.1
40.5
38.1
23.8
4
For more, see: “Decision
insights: Measuring
decision effectiveness”,
Bain Company, 2012

decision-making effectiveness and financial
results,” explains Mr Puryear. “Making decisions
requires insights you can count on, so there’s
a strong correlation between really being
able to leverage the data assets and ultimate
performance from that.”
Characteristics of the data insight
masters
A further set of comparative findings shows even
more striking insights. In many conversations
with C-suite executives, a central issue was how
to use data to find the proverbial needles in the
haystack—uncovering the valuable insights that
no one else had spotted.
To this end, this research cross-analysed the
results of the small cluster of executives who
professed to have been highly effective at
generating insight from data and compared it
with a far larger grouping that admitted to being
weakest at this. Again, while such correlation
cannot claim causality, the contrasts between
these groupings are stark.
Overall, those that are highly effective at
garnering insights are:
l Nearly eight times as likely to have a well-
defined data management strategy in place.
l More than four times as likely to have changed
the way they tackle strategic decisions, as a
Data-driven management change
Respondents agreeing with the statement
(% respondents)
Figure 15
21st-century management techniques must be
radically transformed to keep pace with what
technology is making possible
We are rethinking our approaches to strategy
and decision-making processes, to make it
more data-driven
61.9
33.3
50.4
35.7
The importance of a good data platform
Organisational effectiveness at translating new data sources/types of data into insightful information
to drive strategic change
(% respondents)
Figure 16
The rest
Good data strategy
7.2 59.8 23.9 8
30.2 60.3 6.3
1.2
3.2
High effective Somewhat effective Neither effective nor ineffective
Somewhat ineffective Highly ineffective
NB: “Good data strategy” cohort is firms agreeing with the statement: “We have a well-defined data management strategy that focuses resources on
collecting and analysing the most valuable data.”

result of the data they have, despite most other
attributes of their strategic decision-making
process being the same.
l Almost universally providing their senior
executives with new data and information to
support their roles and decisions (92% are,
versus just 35% of ineffective firms).
l Twelve times more likely to consider their
strategic planning and decision-making to be
data-driven.
l Unique among all clusters of respondents
in having most often put their CEO in charge
on data-related initiatives within the business,
ahead of the CIO, which is the stock answer for
nearly all other sets of respondents, including
the high-growth companies outlined above.

The shift to a data-centric world is opening up
new opportunities all the time. But this does
not mean it is an easy task for companies to take
advantage of them. Uncertainty over the future
and difficulty in filtering the signal from the
noise; immature tools; limited skills and expertise
in this domain; the challenges of dealing with
IT—all such concerns come into play, just as they
did at the dawn of the internet era.
Across all functions and industries, a fairly
consistent set of barriers emerged. The most
pressing worry related to the difficulty in
assessing which data are truly useful, selected
by four in ten respondents (see Figure 17). “The
ability to separate noise from the important
things is a challenge, you don’t learn it
overnight,” says a Fortune 100 chemical company
CFO. “The access to quality data becomes more
and more important with each passing year. And
it also creates a thirst for more data, as you begin
to understand what can be done with it.”
Concerns over data quality followed closely
behind, along with a related worry about data
overload. About one-third (34%) of respondents
agree that they are increasingly worried about
the quality of strategic decisions being impaired
by information overload. In part, as BP’s Mr Gray
argues, this can be a function of maturity within
the business—and a willingness for leaders to
push back on reporting purely for reporting’s
sake.
“We went through this process a few years back,
and ran into a break point when an executive
questioned one day who was actually reading
yet another thick report on some issue,” he says.
“It was a watershed moment, as other leaders
starting asking the same question, so a lot of this
got cut back very quickly. We’ve been through
that, but it is absolutely a risk for people to be
aware of.”
Beyond these data-specific concerns, a lack of
skills (35%), issues relating to organisational
silos (25%), disconnects between IT and the
rest of the business (16%), and a fear of the
unknown among the management team (15%)
round out the key barriers. Each of these can
be major issues in their own right. For example,
Mr Puryear notes that many companies are
grappling with the accumulation of 20 years of
legacy systems and applications. “This notion of
unnecessary complexity in the IT environment is
something that crops up a lot, and the reality is
that so much of the data that companies need are
locked up within these systems,” he says.
The fine line on trust
Elsewhere, other concerns come into play for
either specific industries or countries and
regions, from worries over tightening privacy
regulations that are restricting the access
or availability of consumer data, through to
considerations of trust. Coutts provides a useful
illustration. As Mr Brannan explains it, while an
Noise, overload and trust: The data
challenges4
The list of challenges to companies seeking to become more data-centric is long, but
leading it is the ability to determine which data are truly useful.

Barriers to progress
Barriers to effective use of larger data volumes in strategic planning or decision making
(selected responses)
Figure 17
Fear of the unknown among key managers
Lack of necessary skills/expertise
Technology/systems issues
Data quality issues
Disconnects between the IT function and the
rest of the business
Lack of leadership from the top
Difficulties in assessing which data is truly
useful (data overload)
15.8
9.4
13.3
15.6
6.4
45.6
40.8
40
37.8
38.3
22.8
12.5
10
13.3
12.8
17.5
6.3
6.7
11.1
6.4
28.1
46.9
43.3
26.7
46.8
19.3
12.5
23.3
24.4
31.9
28.1
37.5
40
53.3
23.4
Manufacturing Financial services Professional services
Retail and consumer goods TMT
online retailer like Amazon might reasonably be
expected to track a customers’ purchases and
insights in order to sell them more, a private
bank has to tread far more carefully. “Our
relationships are based on discretion and trust.
If we sit there and talk about the fact that we
know a lot about you, we can easily step over that
very fine line,” says Mr Brannan. “So there’s a
question of how we present our insights back to
clients and whether that’s acceptable to them.
Anything that erodes trust is a huge risk to us.”
Professor Neely argues that reputational risk is a
significant issue for firms. “As you pull different
sources of data together, you get to know more
about people, whether customers or employees,
and you have to be very careful about what you
want to do with some of that information,” he
says. At Aimia, Mr Johnston explains that such
concerns are paramount within the business,
which has in turn led it to develop a core set
of values relating to data, dubbed TACT (for
Transparency, Added value, Control, and Trust).

New needs, new skills
These challenges apply just as much to using
data for optimisation purposes as they do for
more strategic ones. But as leaders seek to make
the evolution from one to the other, several
issues become more pressing. One that crops
up repeatedly is the move beyond the skills of
the traditional business analyst role, to bring
in an advanced new skillset, matched with deep
industry expertise.
At the core of many challenges, argues Mike Balay,
vice-president for strategy at Cargill, the global
food manufacturer, is the need to better align the
management team’s assumptions about the world
with reality. This gets to the core of management
theorist Peter Drucker’s “Theory of the business”,
which argues that when these assumptions
are true, whether about customer needs or
organisational strengths, it is far easier for the
business to be successful.5 “What’s brilliant about
Drucker’s theory is that when these assumptions
are in alignment, your business successes will
seem effortless,” says Mr Balay. “The companies
that don’t do well are those that don’t share the
same assumptions internally or don’t sync them
up to the world outside.”
What’s important here is that data essentially
supercharges this theory, by providing far
more information and options, such as the
possibility to segment customers in multiple
new dimensions. But this comes with tough new
challenges: “How is your organisation going to
process the insight? How are you going to get
connect insight to an actionable set of strategic
choices? And how are you going to get the
organisation aligned behind you?” says Mr Balay,
who makes a strong case for the need to invest in
advanced skillsets to support this (see the case
study below for more).
This is crucial. As Aon’s Mr Clement advises,
one of the challenges is that data capture the
world as it exists today, not what might be
possible. “The biggest opportunities come from
fundamental change. If you take the old horse
and buggy example, the data would have pointed
you to adding more horses, not to inventing the
automobile,” he says. To help Aon improve on
that front, the company has been continuously
investing in its skills and ability to interpret
data. “Ten years ago we had a lot of analytical
capability, but the organisation just wasn’t ready
to take advantage of it,” says Mr Clement. “You
need the talent and we are catching up on that.”
All this points to a marketplace in transition.
As highlighted earlier, only a small minority
(12%) of executives think their organisation has
been “highly effective” at translating data into
useful and insightful information so far. “Access
to data is no longer a challenge these days. We
have a tremendous amount of access and we can
measure just about anything,” says Xerox’s Ms
Carone. “To me, where we continue to have some
challenges is how to translate this data into real,
actionable results for the company,” she says.
5
“Theory of the business”,
Harvard Business Review,
Peter F. Drucker, September-
October 1994

Over the past few years, Samsung has risen
to become one of the biggest consumer
electronic brands in business. In 2012, its
popular smartphones accounted for the highest
proportion of global sales. That success has come
on the back of its innovation, design and other
factors. But behind the scenes, the company’s
achievements have also been driven by its efforts
to become a far more data-centric business.
Asim Warsi, vice-president for mobiles at
Samsung India, explains that the company
now tracks the sale of every single one of the
tens of thousands of handsets it sells on a daily
basis, which in turn powers the rest of the
business. “This data is the basis on which our
demand planning is done, which is the basis
for production planning, and in turn the basis
for raw material sourcing, so it’s really the
lifeblood of the organisation,” he says.
From a sales perspective, the company knows
every sale that happens, and where and when
it took place, which helps supply the analytics
that determine which sales channels and
markets are performing, how much stock to
hold, where credit could be considered, and
more. “If there is a particular item not selling,
or slowing down, we immediately know of it and
can take proactive action,” says Mr Warsi. “By
being able to track our business down to the
lowest metric and building our analytics back
from that, it gives us an enormous visibility on
our markets, channels, supply chain, customer
behaviours and more. If this is not powerful,
then I don’t know what would be,” he says.
At the same time, the company is strict about
cutting away the superfluous data that might
obscure this picture. This is just as important in
running a data-centric business. “We purge all
data that is non-decisionable, so we only really
take the data that matter,” says Mr Warsi. This
is a challenge that many other firms are still
grappling with.
Case study: Building a data-centric business at
Samsung

All companies thrive on insights at some level.
But as the world becomes more data-centric,
these insights become increasingly difficult for
companies to find. Mike Balay, vice-president
for strategy at Cargill, gives a simple business
scenario that his strategy team might grapple
with: resetting pricing strategies for a particular
product line. As this division gets more data,
it can start to do more complex modelling of
its customer segments. “You suddenly find out
that it’s not just a customer, but it’s a customer
in a particular place, with a certain product
mix, a specific history with us, and a particular
risk management strategy, all of which raises
incredible complexities,” says Mr Balay.
By overlaying this against price strategies
and asset utilisation, the strategy team might
uncover that the unit is actually throwing away
30% of its profitability with the last 10% of
sales it is doing, as one example. Such insights
can transform the fortunes of a division, but
the volume and complexity of the data make
such findings harder to uncover than they
were historically. How to solve this? The simple
answer: hire the smartest possible people.
“The stars of the future are the people able to
work with these large data sets and turn them
into something actionable,” he argues. This
new breed of “super quants”, or what Mr Balay
dubs “elite symbolic analysts”, are what will be
needed to successfully grapple with the firm’s
complex network optimisation challenges.
But getting such rare skills requires a major
corporate commitment. “Are you willing to
hire and pay those people? Give them freedom
and recognition? Make them partners in your
business?” asks Mr Balay. None of this is easy,
not least as this talent is rare, expensive and
often unconventional. “But the conventional
wisdom is not interesting. It is the weirdoes who
are interesting,” he notes.
Case study: Cargill, strategy and the rise of the
so-called super quants

The leadership considerations ahead
This report has made the case that most
companies are now realising that the era of data
could prove just as significant as the shift to
internet-enabled business. A growing proportion
recognises the transformational power of data for
their business, although this group also admits to
being humble about the learning curve ahead.
This is right: relatively few have fully grasped the
power of data and how to truly take advantage of
it. Many of the high-profile examples of data use
so far have largely focused on optimising existing
processes, and are yet to use data for strategic
transformations. But as the latter starts to come
into the frame, it will by definition be harder to
get right. In turn, it is becoming clear that there
are several distinct organisational and leadership
issues for CEOs and their C-suite peers to consider.
First, as with any strategic initiative, making the
most use of data requires leadership from the top.
This involves not only a commitment to invest
in the necessary systems and skills, but also to
demonstrate that these are being taken seriously
at an executive level. “We’ve got to go back to
first principles of making sure we’ve got absolute
clarity across the leadership team, as we build
this out,” insists Coutts’ Ged Brannan. At one
level, this brings a need to consider how best to
overcome organisational silos and departmental
fiefdoms that often jam up the flow of data within
a business.
Second, as data initiatives do start to become
more powerful, there will likely need to be
an evolution in how senior decision-making
is handled. Whether this involves a shift to
more collaborative and multi-functional teams
considering decisions, or other shifts, it is clear
that work remains to be done to adapt 20th-
century management techniques to the data-era.
In particular, organisations will need to find more
effective means of making strategic decisions,
with new tools to aggregate the information they
have to hand. “There will be some fundamental
changes,” says Mr Brannan. “Our executive
committee will have richer data available to them
and they will, therefore, be able to make more
informed decisions.”
Third, businesses should be aware that there are
major questions yet to be fully resolved about
how data initiatives are structured and managed
internally. While in many instances, it is the CIO
Conclusion

who instinctively will be given the lead on this,
this research has highlighted that those firms
that do best on extracting insights have often
looked to their CEO, or other leaders with clout
internally, to take the lead. “This tends to be
structured differently, and it should be. You need
someone who has oversight responsibility at that
very high level to shepherd it through and act on
it and care about it,” argues The Economist’s Mr
Cukier. Professor Neely agrees: “The technological
infrastructure is there, but the organisational
infrastructure is often not.”
Fourth, for those companies making the shift
from the tactical to the strategic use of data,
there will be a need for management flexibility
and open-mindedness as to the new possibilities
emerging. The shift also requires CEOs to have the
courage to take the leap towards the unexplored.
“It’s very difficult for boards to want to consider
fundamentally changing the business model,”
says Aimia’s Mr Johnston. “So much management
time is dedicated to short-term performance
targets and cash management and so on, that
it’s difficult to make a leap like this,” he says.
But as the value of data becomes more apparent,
the need for this shift will become even more
apparent. As one executive interviewed for this
report has argued, some companies should even
seek to explicitly value their data in order to more
clearly represent it as the asset it is.
Indeed, as Jason Trost, the CEO of Smarkets, an
online betting platform, argues: “For companies
to stay competitive, they need to rearrange their
structures so that they can pivot their business
model, pivot their brand, pivot their products.
That’s the crucial thing business leaders should
be looking at.”

Note: Percentages may not total 100 due to rounding or the ability of respondents to choose multiple
responses
1. Which of the following best describes your job title?
(% respondents)
Chief executive officer, or equivalent
Chief financial officer, or equivalent
Chief operating officer, or equivalent
Chief marketing officer, or equivalent
Chief strategy officer, or equivalent
Chief risk officer, or equivalent
Chief information officer, or equivalent
Chief technology officer, or equivalent
Other C-suite role, please specify
13
19
13
9
7
4
12
7
16
Appendix:
Survey results

2. In which region are you personally located?
(% respondents)
North America
Western Europe
Asia-Pacific
Eastern Europe
47
27
24
2
3. In which country is your company headquartered?
(% respondents)
United States of America
United Kingdom
Canada
Germany
India
France
45
9
5
4
4
3
Italy
3
Switzerland
China
Hong Kong
3
2
2
20
Other

4. What is your primary industry?
(% respondents)
Manufacturing
Financial services
Professional services
Healthcare, pharmaceuticals and biotechnology
IT and technology
Consumer goods
Energy and natural resources
Construction and real estate
Automotive
12
10
9
9
9
7
6
5
4
Retailing
4
Transportation, travel and tourism
3
Chemicals
3
Telecommunications
3
Aerospace/defence
3
Entertainment, media and publishing
3
Logistics and distribution
3
Agriculture and agribusiness
2
Government/public sector
2
Education
2
5. What are your organisation’s global annual revenues in US dollars?
(% respondents)
$500m to $1bn
$1bn to $5bn
$5bn to $10bn
$10bn or more
23
34
14
29

6. How has your company performed, in terms of average EBITDA, over the past three financial years?
(% respondents)
Exceptional growth: 25% or higher
Strong growth: 10-25%
Some growth: up to 10%
No growth
5
37
45
7
Decline
6
7. Which of the following statements best describes your organisation’s approach to data management?
(% respondents)
We have a well-defined data management strategy that focuses resources on collecting and analysing the most valuable data
We understand the value of our data and are marshalling resources to take better advantage of them
We collect a large amount of data but do not consistently maximise their value
We collect data but they are severely underutilised
20
44
27
6
We do not prioritise data collection
3
8. Which of the following non-traditional sources of data does your organisation collect, or plan to collect? Select all that apply.
(% respondents)
Social media (eg, Facebook, Twitter, YouTube, blogs, etc)
Machine generated data (eg, sensors, smart grid, RFID, network logs, telematics, etc)
Location-based information (eg, GPS, mobile logins, etc)
Contact centre data (eg, audio conversations, text chats, customer emails, etc)
15
134011
232417
114220
27 18 36 4
29 7
31 5
23 3
Staff data (eg, Emails, calendars, instant messaging, etc)
113728 618
95020 16 5
Syndicated data from third-party data providers (eg, market data, weather, etc)
125616 12 4
8123 22 56
3
Collects Collects and analyses Plans to collect Does not collect Don’t know

9a. Which of the following types of strategic actions has your company undertaken in the past 1-2 years? Select all that apply.
(% respondents)
Entered a new geographic market(s)
Developed and/or launched a new product(s)/service(s) in your core sector
Undertaken a sales strategy transformation
Undertaken a merger or acquisition
Undertaken a back-office transformation
Undertaken a corporate restructuring (eg closing an unprofitable division)
Undertaken a marketing strategy transformation
Executed a change in core business model
Undertaken supply chain transformation
Entered a new line of business separate from your core sector
Started a new collaborative venture
None
67
67
46
44
44
42
40
36
35
35
28
3
3
9b. Which of the following types of strategic actions does your company plan to undertake in the next 1-2 years?
Select all that apply.
(% respondents)
Develop and/or launch a new product(s)/service(s) in your core sector
Enter a new geographic market(s)
Undertake a merger or acquisition
Start a new collaborative venture
Undertake a corporate restructuring (eg closing an unprofitable division)
Undertake a marketing strategy transformation
Undertake a sales strategy transformation
Enter a new line of business separate from your core sector
Undertake supply chain transformation
Execute a change in core business model
Undertake a back-office transformation
None
42
39
35
32
31
31
30
25
25
24
23
3
6

10a. Which of the following statements best characterises the typical frequency of your organisation’s major strategic
management changes in the past 1-2 years?
(% respondents)
Strategic changes are usually made on an annual basis
Strategic changes are usually made every six months
Strategic changes are usually made quarterly
Strategic changes are usually made monthly
43
7
6
2
Strategic changes are made regularly, as needed, rather than on any fixed schedule
41
10b. How do you expect the typical frequency of your organisation’s major strategic management changes to alter in the next
1-2 years?
(% respondents)
Strategic changes will usually be made on an annual basis
Strategic changes will usually be made every six months
Strategic changes will usually be made quarterly
Strategic changes will usually be made monthly
26
15
8
5
Strategic changes will be made regularly, as needed, rather than on any fixed schedule
46
11a. In your view, to what extent would you consider your company’s strategic planning to be data-driven?
(% respondents)
Highly data-driven
Rarely data-driven
32
42
21
5
11b. In your view, to what extent would you consider your company’s strategic decision-making to be data-driven?
(% respondents)
Highly data-driven
Rarely data-driven
25
45
25
5

12a. How important would you say increased data volume has become to your company’s strategic planning?
(% respondents)
Highly important
Somewhat important
Of limited/occasional importance
Not important
39
46
10
4
12b. How important would you say increased data volume has become to your company’s strategic decision-making?
(% respondents)
Highly important
Somewhat important
Of limited/occasional importance
Not important
40
43
13
5
13a. To what extent would you say your company’s strategy has improved, or worsened, over the past 1-2 years, as a result of
having more data?
(% respondents)
Improved significantly
Improved moderately
No change
Worsened moderately
Worsened significantly
Don’t know
18
51
22
4
3
3
13b. How do you expect your company’s strategy to change, in the next 1-2 years, as a result of having more data?
(% respondents)
Improve significantly
Improve moderately
No change
Worsen moderately
Worsen significantly
Don’t know
26
46
18
3
4
4

14. Has your company changed the way it tackles strategic business decisions as a result of data?
(% respondents)
Yes
No, but we will do so within the next 1-2 years
No
Don’t know
35
24
33
7
15. To what extent would you say that your organisation has been effective at translating any new data sources, or types of
data, into useful information to drive changes of strategy?
(% respondents)
Highly effective
Somewhat effective
Neither effective nor ineffective
Somewhat ineffective
Highly ineffective
12
60
20
6
2
16a. To what extent has the range and types of data you’re relying on for your changes of strategy or new strategies either
expanded or contracted over the past 1-2 years?
(% respondents)
Major expansion
Moderate expansion
Neither increased nor contracted
Moderate contraction
Major contraction
14
52
28
4
2
16b. How do you expect the range and types of data you’re relying on for your changes of strategy or new strategies to change
in the next 1-2 years?
(% respondents)
Major expansion
Moderate expansion
Neither increase nor contract
Moderate contraction
Major contraction
22
52
20
3
2

17. Which of the following types of data would your company benefit from the most in deciding on changes of strategy or
developing new strategies? Select all that apply.
(% respondents)
Syndicated data from third-party data providers (eg, market data, weather, etc)
Machine generated data (eg, sensors, smart grid, RFID, network logs, telematics, etc)
Contact centre data (eg, audio conversations, text chats, customer emails, etc)
Social media (eg, Facebook, Twitter, YouTube, blogs, etc)
Staff data (eg, Emails, calendars, instant messaging, etc)
Location-based information (eg, GPS, mobile logins, etc)
None of the above
52
48
40
36
35
33
28
3
4
18. Do you agree or disagree with the following statements?
(% respondents)
Our business has consistently sought to provide our senior executives with new data and information to support their roles and decisions
We have been able to create useful new management metrics to track performance, on the back of new data inputs
Improvements in how we interpret our data, such as visualisations, would enable us to capitalise on potential opportunities faster
Our strategic decision-making has improved on the back of being more data-driven
62
124442
83257
93950
28 9 1
2
3
3
We are increasingly worried about the quality of strategic decisions being impaired by information overload
234134 3
The merits of “big data” for strategic planning and decision-making are substantially overhyped
224329 5
Data is shared effectively between different functional groups within the organisation
303631 3
Greater use of data is more pertinent for our operational choices and actions, rather than our strategic ones
224135 3
3
Agree Neutral Disagree Don’t know

20. Who takes the primary lead on data-related initiatives within your business?
(% respondents)
Chief information officer
Chief operating officer
Chief financial officer
Chief executive officer
Chief marketing officer
Chief strategy officer
Other C-suite role, please specify
Line of business heads
Dedicated role (eg, chief data officer)
Not applicable, we don’t have a dedicated role taking the lead on this
28
9
15
13
9
8
1
6
1
1
9
19. What do you see as the biggest barriers to making effective use of larger volumes of data within your strategy planning or
strategic decision-making? Select up to three.
(% respondents)
Difficulties in assessing which data is truly useful (data overload)
Data quality issues
Lack of necessary skills/expertise
Organisational silos
Technology/systems issues
Disconnects between the IT function and the rest of the business
Lack of a sufficient data-led culture
Fear of the unknown among key managers
Lack of leadership from the top
40
37
35
25
22
16
15
14
11
Uncertainty about the return on investment
11
Uncertainty about the degree to which this would genuinely improve strategy or decisions
10
Insufficient resources/funding to prioritise such initiatives
9
Legal barriers to the sharing of data (data protection laws)
8
1
Don’t know
3

21. Do you agree or disagree with the following statements?
(% respondents)
We have a very difficult time identifying which data are relevant for strategy and which to ignore
We see a clear risk that data overload provides false comfort (ie, that issues are drowned out amid too many data points)
21st century management techniques must be radically transformed to keep pace with what technology is making possible
We are rethinking our approaches to strategy and decision-making processes, to make it more data-driven
36
173942
73258
133846
38 25 1
2
3
3
Big data has had more of an effect on our regular, tactical decision-making, as opposed to our strategic decisions
164632 5
Agree Neutral Disagree Don’t know
22. How have the following attributes of strategic change projects/initiatives within your business developed as a result of
having more data?
(% respondents)
Speed of change
Effectiveness of change
Quality of changes made
50
74249
54250
42 6 2
2
3
Improved No change Worsened Don’t know
23aa. In which of the following strategic aspects of your role do you believe that having more data has made the biggest
positive difference to the choices and actions you have made? Select up to three. - CEO
(% respondents)
Geographic expansion
Identifying new business models
Identifying new revenue streams
Gaining better insights into the competitive landscape
Improving organisational productivity
Improving strategic planning
Improving risk management
Increasing organisational transparency
Ensuring regulatory compliance
Improving resource allocation
40
35
33
33
30
30
25
20
15
15
0
None of the above
0

23ab. Which of the following strategic aspects of your role have you not yet tried, but believe would hold significant potential
with more data? Select up to three.
- CEO
(% respondents)
Improving risk management
Improving organisational productivity
Identifying new revenue streams
Geographic expansion
Identifying new business models
Improving resource allocation
Gaining better insights into the competitive landscape
Ensuring regulatory compliance
Improving strategic planning
33
30
25
25
23
23
20
18
18
10
3
None of the above
3
24a. How would you compare your own use of data as opposed to other CEOs in your industry?
(% respondents)
Significantly above average
Somewhat above average
About average
Somewhat below average
Significantly below average
Don’t know
15
36
36
10
0
3

25a. What do you consider the key barriers or issues that prevent you from making greater use of data within your executive
decisions? Select all that apply. - CEO
(% respondents)
Lack of skills/insufficient expertise
The underlying data is simply not good enough
This is more useful for my immediate team, rather than myself
Limited direct benefit to my kind of role
Our organisational silos
The related systems are not sufficiently mature
CEOs already deal with too much information; we need less, not more
Related tools are too complicated/time-consuming
Don’t believe big data is that useful for CEOs as yet
I don’t yet sufficiently understand this concept
35
33
25
23
23
18
15
13
13
3
3
23ba. In which of the following strategic aspects of your role do you believe that having more data has made the biggest
positive difference to the choices and actions you have made? Select up to three. - CMO
(% respondents)
Increasing customer understanding and segmentation
Increasing sales
Helping assess potential demand for new products/services
50
40
37
27
23
17
17
17
13
7
None of the above
7
0

23bb. Which of the following strategic aspects of your role have you not yet tried, but believe would hold significant potential
- CMO
(% respondents)
Increasing customer understanding and segmentation
Increasing sales
Helping assess potential demand for new products/services
33
27
27
27
23
17
13
13
13
13
None of the above
7
0
24b. How would you compare your own use of data as opposed to other chief marketing officers in your industry?
(% respondents)
About average
Don’t know
7
50
30
10
0
3

25b. What do you consider to be the key barrier or issue that prevents you from making greater use of data in your executive
decisions? Select all that apply. - CMO
(% respondents)
The related systems are not sufficiently mature
The underlying data is simply not good enough
Our organisational silos
Related tools are too complicated/time-consuming
Lack of skills/insufficient expertise
Chief marketing officers already deal with too much information; we need less, not more
Data privacy concerns and/or data regulation issues
Limited direct benefit to my kind of role
This is more useful for my immediate team, rather than myself
Don’t believe big data is that useful for chief marketing officers as yet
I don’t yet sufficiently understand this concept
43
33
33
27
20
20
17
13
10
3
3
0
23ca. In which of the following strategic aspects of your role do you believe that having more data has made the biggest
positive difference? Select up to three. - CRO/CFO
(% respondents)
Improving scenario planning and forecasting
Improving financial close management
Improving profitability
Improving financial risk management
Improving corporate reporting/dashboards
Identifying cost efficiencies
Improving operating risk management
Improving corporate compliance
Identifying new revenue opportunities
Lowering cost of capital
Improving business partnering/support
36
33
32
25
24
23
20
19
13
12
5
Facilitating access to capital markets
3
None of the above
7
4
0

23cb. Which of the following strategic aspects of your role have you not yet tried, but believe would hold significant potential
- CRO/CFO
(% respondents)
Improving financial risk management
Identifying new revenue opportunities
Improving scenario planning and forecasting
Improving profitability
Improving operating risk management
Identifying cost efficiencies
Improving business partnering/support
Improving corporate reporting/dashboards
Facilitating access to capital markets
Improving corporate compliance
Improving financial close management
24
24
20
19
19
19
19
19
17
11
11
Lowering cost of capital
9
None of the above
7
9
0
24c. How would you compare your own use of data as opposed to other CFOs or chief risk officers in your industry?
(% respondents)
About average
Don’t know
7
25
47
13
4
4

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