In June and July 2015, with sponsorship by SAP, The Economist Intelligence Unit (EIU) carried out a survey of more than 300 executives who are familiar with their company's data and analytics practices. The goal was to assess trends in the use of market-facing advanced analytics. To add insights to the survey findings, the EIU conducted interviews with several advanced analytics practitioners. This executive summary describes the top findings of this research.

1. An report by the Economist Intelligence Unit
Competing
smarter with
advanced
data analytics
Sponsored by

2. © The Economist Intelligence Unit Limited 20151
Competing smarter with advanced data analytics
Introduction 2
Companies take to the offense with data analytics 4
Focusing data analysis on competitors 5
External and internal data 6
Business challenges and data challenges 7
Satisfaction levels 8
Keys to success 9
Conclusion 10
Contents
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2
3
4
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3. © The Economist Intelligence Unit Limited 20152
Competing smarter with advanced data analytics
Introduction
In June and July 2015, with sponsorship by SAP,
The Economist Intelligence Unit (EIU) carried out a
survey of more than 300 executives who are familiar
with their company’s data analytics practices. The
goal was to assess trends in the use of market-
facing advanced analytics. The sample includes 50%
C-level executives and represents companies from
Asia-Pacific, North America, Western Europe and
Latin America. All of the respondents are from
companies with at least US$500m in annual
revenue, with half of them reporting US$1bn or
more. To add insights to the survey findings, the
EIU conducted interviews with several advanced
analytics practitioners. This Executive Summary
describes the top findings of this research.
The survey found that companies are moving
beyond first-generation big data applications
based on internal assets and are reporting
considerable success with innovative market-facing
initiatives that use a wide range of transactional
and external data. Competitor-focused initiatives
are given the highest priority, with customer- and
operations-focused measures comprising a
significant number of initiatives.
The survey also found that the biggest technical
challenge was the need to identify and integrate
multiple data types from both internal and external
sources. When it comes to internal challenges
within an enterprise, data and analytics silos stand
out, largely because market-facing advanced
analytics initiatives tend be driven by individual
lines of business.
Despite these challenges, executives
overwhelmingly rate these advanced analytics
initiatives as successful and point to multiple
simultaneous benefits. This broad success is driving
continued innovation and experimentation, with
technical challenges seen as minor obstacles
compared with the need to select the right
initiative and the right team.

4. © The Economist Intelligence Unit Limited 20153
Competing smarter with advanced data analytics
The EIU, with sponsorship from SAP, is
conducting a major research programme on “The
hyperconnected economy”. This describes the
quantum leap in linkages among people and
companies being driven by mobility, social media,
the Internet of Things and other emerging
technologies.
One of the important outcomes of
hyperconnectivity for business is the creation of
new fields of competition. Data are being
developed within companies, from public sources,
by third-party vendors that provide multiple
linkages on products, pricing, branding and sales.
From proactive pricing to tracking the branding of
competitors’ products, hyperconnected data
present a new basis for competition.
Competing in the hyperconnected economy
The ongoing research on The hyperconnected economy can be found here: www.economistinsights.
com/technology-innovation/analysis/hyperconnected-economy

5. © The Economist Intelligence Unit Limited 20154
Competing smarter with advanced data analytics
In version 1.0 of data analytics, most companies
focused on internal initiatives such as operating
efficiencies. But with increased computational
power and new data sources, they are
experimenting with “offensive moves”. The number
and variety of initiatives is very broad— and Ben
Alves, Market Intelligence and Customer Analytics
Manager at Autodesk, doesn’t find that at all
surprising. “Everything comes back to big data,” he
says. “There’s more and more of it available, and
more and more companies are finding unique and
creative ways to create insights from those data. It’s
in their blood to be constantly pushing the limits.”
Proactive price optimisation stands out as the
most common market-facing data analytics
initiative, but seven others are cited by between
35% and 44% of the respondents, with the median
number of initiatives being four. A number of
forces have combined to generate this diversity.
First, innovation in this space is typically driven
by lines of business, each with its own needs.
Second, emerging big data tools are flexible and
often cloud-based, making it easier for business
users to experiment with new applications even
when they can’t predict return on investment
(ROI). And third, lessons learned from this
experimentation accumulate, encouraging
innovation in different areas. “It’s a very
innovative space and it’s early days yet,” says Mr
Alves. “Whether companies are testing, evaluating
or piloting, there’s a lot of innovation going on and
you need to see if it’s the right fit for you.”
The interpretation that much of this activity
entails innovation and experimentation is
supported by the fact that only 17% of respondents
say they have developed ongoing competitor
intelligence programmes, indicating that they are
not yet ready for comprehensive approaches.
Companies take to the offense with
data analytics1
Source: The Economist Intelligence Unit.
Has your company launched any of the following market-facing advanced data analytics initiatives?
(% of all respondents)
External and internal data to support a proactive price optimisation
Data to track competitors’ brand performance, awareness and market share
Predictive analytics to support market demand forecasting
Social media to track trending of competitors’ products and brand
Data analytics to push point-of-sale offers
Data analytics or social media to target customers of competitors
Market and internal data to support product/service launches, etc.
Third party data to generate and track sales leads through the marketing funnel
Geospatial analytics to optimise outlets, manufacturing, distribution, etc.
Data analytics to support an ongoing competitor intelligence programme
50
44
42
41
39
37
37
35
27
17

6. © The Economist Intelligence Unit Limited 20155
Competing smarter with advanced data analytics
A pattern emerges when respondents are asked to
cite the initiative that is the highest priority for
their company. The top three initiatives are all
competitor-focused, including proactive price
optimisation, tracking competitors with social
media and tracking competitor brand performance
and market share.
“Competitor-focused initiatives are one of the
main drivers for organisations to integrate
external data with internal data at the outset,”
says Dr Amy Shi-Nash, Chief Data Science Officer at
DataSpark, Singtel’s analytics subsidiary. “There’s
an element of self-defence to it—the thinking is,
‘If I can use data better than my competitors, not
only will I not be left behind, but I can also seize
the competitive advantage’.”
On the other hand, there is also considerable
activity spread over several categories of
customer-/operations-focused initiatives, with the
overall total nearly equally split between the two
types. The higher priority attributed to competitor-
facing initiatives may result in a greater allocation
of resources, which may partly explain the fact that
satisfaction is higher with competitor-focused
initiatives. The proportion of executives who report
being “somewhat” and “very” satisfied with their
primary initiative is 93% for competitor-focused
initiatives and 78% for those that are customer-
and operations-focused.
Focusing data analysis on
competitors2
Source: The Economist Intelligence Unit.
Please select the primary initiative—the one that you believe is the highest priority for your company
(% of respondents who designated a primary initiative)
External and internal data to support a proactive price optimisation
Social media to track trending of competitors’ products and brand
Data analytics to track competitors’ brand performance, awareness and market share
Predictive analytics to support market demand forecasting
Third party data to generate and track sales leads through the marketing funnel
Geospatial analytics to optimise outlets, manufacturing, distribution etc.
Market and internal data to support product launches, promotions, and offers
Data analytics or social media to target customers of competitors
Data analytics to push point-of-sale offers
Data analytics to support an ongoing competitor intelligence programme
Competitor focussed Customer and operations focussed
18
17
14
11
11
10
9
6
3
1

7. © The Economist Intelligence Unit Limited 20156
Competing smarter with advanced data analytics
The survey finds that companies are combining
many types of data to carry out their advanced
analytics initiatives. Most of them mix multiple
types of internal and external data. The survey
found that the average initiative uses three
internal and two external data sources for a total of
five. Moreover, every type of internal and external
data included in the survey is being used by a
significant number of respondents, the lowest
being sensor-based data with 19% and aggregated
third-party tracking data with 21%.
While the power of advanced market-focused
analytics is greatly enhanced by this ability to
integrate disparate data sources, this is also the
root of the most important challenges. “There’s an
overwhelming amount of internal and external
data available for analysis, and companies are
struggling to capture and process all of this data
into a format that balances analysts’ need for
speed and computational power without
overburdening the organisation with enormous
hardware and storage costs,” says Amy Gershkoff,
Chief Data Officer at Zynga. “But those that
successfully capture the wide array of available
data—integrating it into a unified, easy-to-use
database, hiring terrific analytical talent and
empowering that talent to uncover actionable
insights—have a crucial competitive advantage.”
The survey confirms that the need to access and
integrate internal and external data from multiple
sources and technologies are the principal
challenges confronting advanced data analytics
initiatives. The top four challenges all involve
either identifying or integrating different types of
data and are cited by between 37% and 43% of
respondents. Accessing, cleaning and integrating
data from different technologies are also
significant hurdles.
External and internal data
3
Source: The Economist Intelligence Unit.
Which of the following transactional data sources did
your organisation use to support this initiative?
(% of all respondents)
Which of the following external data sources did your
organisation use to support this initiative?
(% of all respondents)
Social media data
Third-party marketing analytics
Data from public/government databases
Credit rating data
Geolocation data
Aggregated tracking data from 3rd parties
Customer data
Sales transaction data
Pricing data
Supplier/Supply chain data
Ecommerce data (internal)
CRM data
Manufacturing data
Mobility analytics
Sensor-based data
56
44
36
33
31
29
26
24
19
46
39
35
33
33
21

8. © The Economist Intelligence Unit Limited 20157
Competing smarter with advanced data analytics
When asked which business-related challenges are
the biggest obstacles to the successful execution of
advanced analytics initiatives, executives most
frequently point to data and analytics silos within
their organisations (43%). Other top challenges
include gaining sufficient executive support,
analysing data across silos to develop a holistic
view, and lack of personnel with sufficient data
expertise (all 41%). All of these challenges appear
to stem from the fact that new and innovative data
analytics initiatives are most commonly driven by
lines of business, which is not where data analytics
expertise usually resides.
Several factors are behind this trend. Line-of-
business owners are often the first to perceive needs
and the first to recognise the benefits of innovation.
Moreover, a range of new tools gives them access to
advanced analytics independent of their enterprise
IT functions. “Sales units can use both big data and
data-mining tools to categorise customers and
develop new products to maximise profits,” says
Atlas Lu, Vice President of China Airlines Information
Management division. “Managers can use business
intelligence tools to quickly analyse current
operations data and facilitate new strategic
planning, while IT personnel maintain clear lines of
communication and supplement missing data.” And
finally, the expected cost of initial forays into big
data is generally low enough that line-of-business
owners do not need to demonstrate ROI for an
experimental initiative. In fact, demonstrating ROI
is the least important challenge, cited by only 10%
of executives.
The situation can change once experimental
innovations have proven successful, since at this
point proponents have an interest in broadening
support and resources and this generally requires
support from enterprise leaders. There is
substantial reason for optimism on that front. “By
using relevant marketing analytics, we can find
hidden and unforeseen patterns among large
amounts of internal and external data to build our
initiatives,” says Mr Lu. “Our hope is that the
relevant personnel can use this method to examine
current market and sales strategies, developing new
ones to improve service quality across the board.”
Business challenges and data
challenges4
Source: The Economist Intelligence Unit.
What were the most significant business-related challenges that your organisation faced in the
execution of this initiative?
(% of all respondents)
Data and analytics silos within the organisation
Analysing data across analytics silos to develop a holistic view
Sufficient executive support
Using personnel with sufficient data expertise
Engaging business users, through self-service functions or otherwise
Sufficient financial resources
Providing decision-makers with analytics-based insights
Finding the right analytics software
Demonstrating sufficient ROI on the project
43
41
41
41
33
33
32
16
10

9. © The Economist Intelligence Unit Limited 20158
Competing smarter with advanced data analytics
Survey respondents report high levels of
satisfaction with their big data analytics initiatives.
Overall, 80% say they are satisfied, including 23%
very satisfied and 57% somewhat satisfied. These
results are supported by a broad range of specific
benefits that executives report. Reduced costs are
the most frequently cited benefit-surprising, as
reduced costs were not among the top objectives of
respondents’ advanced analytics initiatives. To
some extent this may reflect unexpected cost-
savings from parallel actions such as moving to
cloud-based analytics platforms. Another
consideration is that reduced costs are easy to
recognise while other benefits can take time to
appear.
But China Airlines’ Atlas Lu cautions that
seeking cost reductions can be a distraction. “Our
goal [with data analytics initiatives] is to find
hidden information with potential for results that
surpass all imagination,” says Mr Lu. “Through data
analytics we can identify our customers’
consumption habits, stimulate purchasing
behaviour and increase corporate earnings on a
basis of increased customer loyalty-reaching our
long-term goal of corporate sustainability. Cost
reductions are not our main concern.”
Aside from cost-savings, respondents point to
multiple benefits from both competitor-focused
and customer-focused efforts. New business
opportunities (33%) and increased revenues from
existing lines of business (26%) are ranked second
and third, but additional customers and increased
market share are also cited by more than one in five
respondents. “Competitive advantage is about
more than just sizeable increases in bottom-line
revenues and top-line cost reductions-even though
one or both of those goals is usually the primary
impetus for organisations to undertake large-scale
data integrations,” agrees Amy Gershkoff of Zynga.
“It provides seismic strategic benefits to the
organisation, including the ability to forecast shifts
in the industry, determine the optimal new
products to develop, identify the need to shift
brand positioning and much more.”
Satisfaction levels
5
Source: The Economist Intelligence Unit.
What were the greatest benefits achieved by the initiative?
(% of all respondents)
Reduced costs
New business opportunities
Increased revenues from existing lines of business
New, additional customers
Increased market share
Improved operations
Increased customer satisfaction
Deeper market or competitive insights
41
33
26
25
21
19
13
7

10. © The Economist Intelligence Unit Limited 20159
Competing smarter with advanced data analytics
The high degree of satisfaction with past and
current analytics initiatives has engendered
optimism about the future. More than 90% of
respondents say that they are likely to pursue
further market-facing advanced analytics
initiatives.
The executives surveyed have clearly learned
from their experiences and are now ready to
innovate further. They report that selecting the
right data-driven initiative—and assembling the
right team to execute it—are the most important
success factors.
This is another indication that considerable
experimentation is still ongoing. Collaborating,
garnering senior executive support and choosing
the right technology are also important success
factors cited by at least one-third of respondents.
“There are two main talents you need from [your
team],” says Ben Alves of Autodesk. “First, they
need to be able to understand what’s being done
with the data at a high level and to figure out ways
of how it can be beneficial to the pilot, group or
company—and communicate that business strategy
to the data scientists. Second, you need someone
to encourage buy-in, capable of explaining how
these tools can be beneficial not to a single group
but to the whole organisation.”
Priorities for market-facing advanced analytics
over the next 12-18 months are just as varied as
they have been in the recent past. Various
competitor-focused initiatives are anticipated by
between 36% and 41% of respondents, followed
closely by customer-/operations-focused projects
ranging from 30% to 36%.
Keys to success
6
Source: The Economist Intelligence Unit.
Which of the following factors are most important in determining the success of market-facing data initiatives?
(% of all respondents)
Selection of the right data-driven initiative
Having a team with the right skills
Selection of best technology/software
Obtaining senior executive support
Collaboration of data specialists with business stakeholders or lines of business
Access to suitable internal data
Access to suitable external data
Skills and patience in integrating data
Sophisticated analysis and interpretation of data
42
38
34
33
33
27
23
18
9

11. © The Economist Intelligence Unit Limited 201510
Competing smarter with advanced data analytics
First-generation big data applications focused on
internal initiatives such as supply-chain
optimisation or customer segmentation—because
that was where the data were and could be used.
As companies gain expertise, and as software
grows more sophisticated, industry leaders are now
expanding their data priorities to include market-
facing initiatives. These are external analyses,
sometimes leveraging external data sources that
are used to undercut competitors’ pricing, build
new business opportunities and increase revenues.
However, these more complex initiatives create
commensurate challenges. Data and analytics
silos, multiple data sets and the integration of
externally curated data are the primary problems.
The initial benefit is cost-reduction, as data
enables more efficient approaches and as the move
to cloud lowers direct costs. But users cite further
benefits, including increased revenue, new
business opportunities and the ability to cross-sell
existing products to customers. In sum, data are no
longer just about analytics, they are about creating
a whole new enterprise.
The keys to success are finding the right
initiative, mobilising qualified personnel and
selecting the right software and technologies.
High levels of satisfaction are found in these
early users, with four out of five satisfied with their
current initiatives, and nine out of ten planning
market-facing data initiatives in the near future.
Conclusion

12. © The Economist Intelligence Unit Limited 201511
Competing smarter with advanced data analytics
Whilst every effort has been taken to verify the accuracy of this
information, neither The Economist Intelligence Unit Ltd. nor the
sponsor of this report can accept any responsibility or liability for
reliance by any person on this report or any of the information,
opinions or conclusions set out in the report.
Cover:Shutterstock

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