Eiu alteryx-big-data-decisions

A report from the Economist Intelligence Unit

Big data and the democratisation of decisions

Sponsored by

Democratisation of big data decisions

© The Economist Intelligence Unit Limited 20121

Preface 2

Introduction 3

Orchestrating the big data strategy 4

Making cross-functional teams big data strategy architects 6

Leveraging key data and departments 8

A paradigm shift 9

Appendix: survey results 10

Contents

1

2

3

4



Preface

Big data and the democratisation of decisions is an Economist Intelligence Unit research report, sponsored

by Alteryx. Our thanks go to all survey respondents and interviewees for their time and insight. The author

was Carolyn Whelan and the editor was Riva Richmond. The fi ndings and views expressed in the report do

not necessarily refl ect the views of the sponsor.

October 2012

In August 2012 the Economist Intelligence Unit

conducted a survey sponsored by Alteryx of 241

global executives to gauge their perceptions of big

data adoption. Fifty-three percent of respondents

are board members or C-suite executives,

including 66 CEOs, presidents or managing

directors. Those polled are based in North America

(34%), the Asia-Pacifi c region (27%), Western

Europe (25%), the Middle East and Africa (6%),

Latin America (5%) and Eastern Europe (4%). Half

of executives work for companies with revenue

that exceeds US$500m. Executives hail from

18 sectors and represent 14 functional roles,

including general management (30%), strategy

and business development (18%), fi nance (17%)

and marketing and sales (10%).

About the survey



Much as the Internet upended industry during the

last two decades, big data’s potential to provide

companies with deeper insight into their

businesses seems destined to generate both

upheaval and endless opportunity. The tumble and

roll should only increase as the data heap grows.

Abundant data and the technology to crunch

them are now enabling more fi rms, large and small,

to extract value from fresh insights. The data swell

is fed by billions of transactions, web clicks and

sensor readings, and the buzzing of smartphones

and social media sites. Thanks to cheap servers for

storing data and affordable supercomputers,

algorithms and visualisation tools for mining it,

companies around the world are better able to spot

and act on new trends and intelligence.

This expansion of corporate access to “big data”

is now being met by a democratisation of which

employees inside companies are able to tap data,

draw lessons and make business decisions. The

tyranny of technology and data specialists is

breaking down. Now, players from many

departments are harnessing data to make better

tactical decisions about how to respond to

emerging trends, fi nd new business opportunities,

retain more customers—and goose their

companies’ bottom lines.

According to a survey of 241 global executives,

conducted by the Economist Intelligence Unit and

sponsored by Alteryx, nearly half (48%) of

respondents say someone other than the chief

information offi cer (CIO) drives big data processes.

This trend should accelerate as corporations work

to capture more value from their information

assets. Over three-quarters of respondents say

businesses must empower more employees to

access and make decisions using big data.

“[We] rely on data for most things,” says one

respondent from the publishing industry. “Our

biggest challenge is to get info in the hands of

those who can use it to grow the business.”

Survey respondents believe that tapping big

data will aid decision-making around seizing

market opportunities (66%), holding on to

customers (55%), competing with rivals more

effectively (41%) and boosting fi nancial

performance (35%). They also believe that

allowing more workers to harness big data will help

them make better and faster decisions (63%),

illuminate business opportunities that were not

previously apparent (45%) and identify and exploit

opportunities more quickly (37%).

Tellingly, the data most available to executives—

internal information—are also what they most

value, our research shows. To extract the most value

from big data, companies’ fi rst order of business

may be to eliminate data silos between departments

and fi nd better ways to work together.

Introduction {{Nearly half (48%) of respondents say someone other than the chief information offi cer drives big data processes.||

70%

say a positive

outcome resulted

from their last

major decision in

which data played

a pivotal role,

while

45%

say more big data

would have helped.



Orchestrating the big data strategy1Some large organisations have long leveraged

giant data sets. For decades household names like

Wal-Mart, Tesco and Kroger—and successful fi rms

in industries like fast food, fi nancial services and

healthcare—have sifted through transactional

information, US Census Bureau records, company

e-mails and even genomic patterns to help inform

decisions. This has helped guide actions around

stocking shelves and opening new stores, battling

fraud and gauging the probability of success or

failure for a new drug.

Many of these trailblazers offer useful lessons.

The most important is to start with a highly focused

problem, question or business priority. From there,

managers empower teams to get answers and apply

fi ndings. For example, a question like “Where do

people walk within a store?’ can be answered with

the help of facial-recognition software. Then a

marketing team can improve product displays and

selections.

At most companies, democratisation of data-

driven decision-making is largely aspirational.

Though our survey respondents acknowledge the

importance of big data and its link to driving better

performance, only 17% of respondents consider

themselves leaders in this area. Forty percent say

their organisations are adequate at the collection

and analysis of big data, while a startling 41% say

they are somewhat or completely inadequate. “It’s

about access but also about understanding,” says

one survey respondent. “We all need to get better

at amalgamating and analysing disparate data

sources.”

Often “it’s the hammer looking for the nail,”

says Venkat Rao, a research associate with the

Leading Edge Forum at Computer Sciences Corp, a

technology systems integrator. Executives try to

‘bang out’ insights from a raft of data rather than

let business problems or priorities dictate the

research. “Most people are trying to wrap their

heads around this,” he says.

Many fi rms are hamstrung by the lack of a

cohesive big data strategy. Costly legacy

technology infrastructures, rigid technology teams

and old ways of working that prize intuition and

experience over data-driven decisions are also

common obstacles.

“In the Don Draper era, the person who could

convince someone of something was in charge. In a

data-driven world, whoever can ask the right

questions is the leader,” explains Alistair Croll, an

analyst at Solve for Interesting, a fi rm that studies

emerging technologies. “Data make everything

very much a meritocracy. It’s OK to be wrong if it’s

quick and you learn each time. Organisations that

can’t increase the speed at which they learn will

die. The cycle time is scary.”

Despite the imperative to act, investment is

lagging. Nearly half (47%) of respondents say they

do not expect to invest in expanding their ability to

use big data in decision-making in the next three

years, with fi nancial constraints cited as the

biggest barrier (37%). Larger fi rms are a step

ahead, perhaps because of deeper pockets. Survey

respondents who work for companies with revenue

of US$5bn-10bn are more likely (39%) to consider

{{In the Don Draper

era, the person

who could

convince someone

of something was

in charge. In a

data-driven world,

whoever can ask

the right

questions is the

leader.

||

Alistair Croll, analyst at

Bitcurrent



themselves industry leaders; 86% of this group

believes it is important to empower employees with

more data to make better business decisions.

Corporate culture can also get in the way.

Interestingly, North American respondents

consider culture to be the top obstacle to sharing

big data more widely (it ranked second globally).

Some executives fret that employees will be

overwhelmed by a tsunami of messy information

that may confuse or distract already overloaded

departments. After all, the volume, variety and

velocity of information are both empowering and

intimidating. A lack of consensus at leadership

levels can impede the creation of a cohesive overall

strategy, as can concerns about privacy and

security breaches.

Even fi rms willing to channel funds into better

big data capabilities struggle with talent

acquisition. Data specialists are in short supply and

often lack industry expertise. “There are not

enough foot soldiers to fi ght the war,” says CSC’s

Mr Rao.

Often, rank-and-fi le employees are stepping in,

on their own, to do battle. As with the Arab Spring,

much of the democratisation of data use is emerging

from the grassroots, as employees seeking ways to

do their jobs better, fi nd and mine data with tools of

their own. “Marketing departments are going

rogue,” says Solve for Interesting’s Mr Croll,

describing shadow data-collection systems that use

tools like Google Analytics.

To avoid duplication, use data effi ciently and

conserve resources, the C-suite would be wise to

commit to a thoughtful big data strategy—and to

leverage this entrepreneurial mindset across the

enterprise.

Four years ago, computer giant Dell asked

executives worldwide to make their data available

to other business units. But there was resistance.

Executives were reluctant to share proprietary

information that had been costly to collect and

process, and little sharing occurred.

That changed quickly when Chief Executive

Michael Dell took charge, illustrating the

importance of executive leadership in creating

an environment that gives employees autonomy

to leverage big data for better decision-making.

He required executives to supply data and issue

regular reports. Next, he embedded data scientists

in each business unit to decentralise access to the

data and action on new insights.

“Disparate systems didn’t speak the same

language,” recalls Rob D Schmidt, executive

director for business intelligence in the CIO’s

offi ce. “We had to break down barriers around data

ownership [and] technology and defi ne a common

model. Only when Michael [Dell] required everyone

to standardise on a single reporting format did we

get there. Now we are very much aligned.”

Today, Dell pushes data from businesses

and regions around the globe onto a common

platform. It collects and acts on intelligence about

the activities of prospective buyers on its global

websites to assess tastes and response times. It

watches for intruders, to detect and stop security

breaches. Some 9,000 servers send regular reports

to technicians who fi x problems. And it tracks key

social media infl uencers who are applauding or

complaining about products or services, so Dell can

respond in real time.

Revenue and effi ciency gains have totalled many

millions of dollars, Mr Schmidt says. And efforts to

extract more value from additional data types are

under way.

Taking big data across Dell: leadership matters



Making cross-functional teams big data strategy architects 2

Pioneers in the use of big data task mid-level

manager teams with strategy development that is

rooted in unanswered questions and business

problems. Marcia Tal, founder of consultancy Tal

Solutions and a former executive at Citi who was

responsible for building and leading the company’s

global decision-management system, recommends

creating cross-functional teams that use data and

analytics to develop products and services that

solve problems. Together, they can prioritise their

most pressing data needs while focusing on data

that are accurate, relevant and actionable.

Based on the collective judgment of the team,

data, analytic fi ndings and insights can be applied

to business decisions, she says. But to get there,

vision, strategy and investment are required.

“You need connectors—people that can

translate the business strategies, intentions and

opportunities to the particular program

initiatives,” Ms Tal explains. “Corporations need to

blend art and science, employing creative ‘data

artists’ that draw the picture, which data scientists

have discovered. In a structured but challenged

department that needs to meet its numbers, it’s

EMI Music wants even its most junior employees to

be able to make quick decisions about how to best

promote its artists around the world. “It’s their

job to combine their skills and judgment with the

vision of the artist and the cold, hard data to come

up with a plan,” says David Boyle, senior vice-

president of insight at EMI Music. At a company like

EMI, “relying on analysts or data experts to make

decisions using data doesn’t scale.”

Employees’ fi rst task is to identify the audience

for an artist or piece of music using EMI’s own

consumer research of a million in-depth interviews

with consumers around the world. Then, ten

web-based applications—accessible to all on

PCs, and iPads during meetings—help them make

decisions about products, pricing, sales channels

and marketing approaches. For instance, if an

employee promoting The Beatles in France decides

the best targets are cash-strapped young people

and gadget lovers, she might offer digital products

and promote them on social networks.

Mr Boyle says his top challenge has been to

provide employees with high-level information at

a glance and the ability to go deep to answer tough

questions. “I want quick, but I want complex. And

that’s incredibly diffi cult,” he says. One trick: colour

coding. For instance, blue highlights excellence—

and a lack of blue signals the need to dig deeper.

Finding a balance has paid off. Many employees

make well-supported data-based decisions daily

and with confi dence and speed. Partners get on

board more quickly, product and pricing choices

are smarter, and marketing is more focused and

effective—and EMI artists are better served.

EMI’s success by numbers



hard for people to get their arms around that.

These are diffi cult discussions.”

“Are you learning as fast as the world is

changing?” she asks. Because traditional “data and

management structures are not usually set up to

create value across business entities.” For these

transformational shifts to take root, “we need

leaders to recognise the importance of analytic

business leaders within an organisation,” she says.

When formulating a big data strategy, tapping

business-unit managers who are already capturing

value from actionable insights may also yield useful

guidance about key issues like risks, pricing and

customer behaviour.

Democracy, of course, is on a continuum, and

leadership styles and cultures shape how data are

shared. These styles can range from democratic to

autocratic, says Jeffrey Stanton, a big data expert

at Syracuse University. Most organisations work

more effectively when senior management

articulates a vision for how data should be used, he

says. “But that vision should be compatible with

the organisation’s mission and culture.”



Leveraging key data and departments 3Internal data sit high atop our survey respondents’

ranking of the most benefi cial big data types (61%).

Signifi cantly, fewer respondents cite external

website content as benefi cial (50%), and even

fewer cite much-hyped social media content (39%),

though it remains early days for social media.

Much of today’s data-driven decision-making

also incorporates targeted, discrete data revealing

market dynamics bought from external data

aggregators. The sweet spot for sharing and

spreading useful information seems to be in the

product-line area. There, executives on the front

lines are combining a keen understanding of their

customers with internal big data and relevant

external information about broader product trends

purchased for relatively small sums. The

combination means more effective decisions about

which products to offer.

Indeed, our research shows that analysing big

data sets, whether from internal databases or

culled from the Internet, are most valuable when

viewed in the context that outside market and

customer data can provide. More than half of our

survey respondents believe there is particular

value in analysing internal data alongside

competitive intelligence, customer-segment

information and market-condition models that

companies normally purchase.

According to our survey, the job functions that

stand to benefi t the most from big data analysis

capabilities are customer service, marketing, strategy

and business development, general management,

and information and research. Customer-service and

marketing departments have long profi led current

and potential customers by dissecting demographic,

relational and contextual data. So it comes as no

surprise that over half of our respondents single out

customer retention and spotting new market

opportunities as business decisions that most benefi t

from tapping big data.

The empowerment of employees to access and

analyse data must be accompanied by freedom to

act on their insights. “Data empowerment is

completely toothless unless you combine it with

empowerment to take risks,” Mr Rao says.

And of course, companies need to protect

sensitive data. “Not everyone should be—nor has

the skills to be—stewards, observers or analysts of

big data,” cautions Ms Tal. “This requires very

specialised skills.” Companies should take steps to

ensure that they control who sees and uses what

information.



Unleashing the power of big data enables

innovative executives and employees to fi nd new

market niches, tweak product offerings, reduce

inventory and boost profi t margins. But capturing

value from actionable insights requires a paradigm

shift in culture and execution.

Through a cohesive, integrated strategy that is

backed by the C-suite, designed by cross-

functional teams and executed by line managers

who are permitted to experiment and take

calculated risks, companies can discover exciting

new opportunities and take the swift and savvy

decisions that deliver triumphs.

A paradigm shift 4



Appendix:survey results

Percentages may not add to 100% owing to rounding or the ability of respondents to choose

multiple responses.

Industry leading (my company has a comprehensive strategy for gathering and gaining insight from a broad range of data sources)

Adequate (my company has a basic approach for gathering and gaining insight from a limited number of data sources)

Somewhat inadequate (my company has a limited ability for gathering and gaining insight from data. Only a select few executives or specialists have this ability)

Completely inadequate (my company has no formal way to gather and gain insight from more than a few data sources)

Don’t know

Based on your observations, how does your company’s ability to gather and analyse big data compare to that of competitors within your industry? (% respondents)

17

40

31

10

2

Strongly agree Agree Neither agree nor disagree

Disagree Strongly disagree

Don’t know

Typically, businesses must empower more employees with access to big data so they can make informed business decisions

In general, the more data that are shared within an organisation, the more effective the decision making process

The biggest barrier to getting useful insights and results from big data is the available technology

In most cases, if data results are incomplete or inconclusive, it’s the fault of the data source

The CIO drives all of our big data processes

To what extent do you agree with the following statements: (% respondents)

26 51 15 6 1

27 47 15 10 1 0

9 34 24 28 4 1

5 15 31 37 11 2

5 18 24 36 12 5



Customer service

Human resources

General management

Strategy and business development

Legal

Marketing

Finance

Risk/Security

Operations and production

Regulatory compliance

Sales

Information and research

R&D

Supply-chain management

Procurement

IT

Operations, real estate planning

Other

None of the above

Which job functions do you feel currently have the least extensive big data capabilities or limited developed data systems?Please select the top 4.

(% respondents)

40

32

28

28

24

22

20

20

19

18

18

16

14

13

13

10

8

0

4

Customer service

Marketing


General management


Finance

Sales


R&D

Risk/Security

Human resources

Procurement


IT

Regulatory compliance

Legal

Operations, real estate planning

Other

None of the above

Which job functions need to benefit from big data capabilities today? Please select the top 4.

(% respondents)

41

41

33

27

26

24

24

23

18

16

13

13

12

10

9

8

5

0

2

Market opportunity and segmentation

Customer retention

Product and service mix

Competitor performance

Financial performance

Capital investments

Risk mitigation

Customer purchase

Geographic market

Fraud mitigation

What kinds of business decisions in your organisation would benefit the most from the inclusion of big data? Please select all that apply.

(% respondents)

66

55

44

41

35

29

29

26

26

19



Internal data

External website content

Social media data

Internet connected device data

Location-based data

Internal log files

RFID data

Sensor-generated data

Other

None of the above

Which of the following types of big data would your company benefit from the most in making strategic decisions? Please select all that apply.

(% respondents)

61

50

39

34

34

21

19

17

3

3

Competitive intelligence

Customer segment data

Market condition models and data

Sales results and forecast data

Internal customer data

Third party market data (eg, Demographics)

Point of sale data

Other

None of the above

Which of the following data types would be most useful to analyze in combination with the above big data types in making strategic decisions? Please select all that apply.

(% respondents)

66

65

54

43

39

37

30

2

1

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Think about the last major decision your team made. Approximately what percentage of internal and external big data did you take into account?(% respondents)

12

14

20

17

15

9

7

3

2

2

Positive

Negative

No effect (the decision had no impact)

Now think about the result of that decision. What was the outcome?(% respondents)

70

5

25



Yes

No

Do you feel you had enough internal and external big data to make the decision?(% respondents)

55

45

Strongly agree

Agree

Neither agree nor disagree

Disagree

Strongly disagree

Not applicable (data did not play a part in the decision/outcome)

If you did not feel you had enough internal and external big data to make the decision, to what extent do you agree with the following statement: Adding more sources of data would have helped ensure a positive business decision and outcome. (% respondents)

16

60

15

3

1

5

Yes

No

Over the next three years, do you expect your company to invest more in expanding big data decision capabilities to more employees?(% respondents)

53

47

Financial (high costs are prohibitive)

Cultural (not an executive priority)

Talent (skilled worker shortage)

Security (too many variables)

Technical (too complex to integrate)

Organisational (not everyone would benefit)

Logistical (too many data sources)

Time (data capabilities take too long)

What would you consider the biggest obstacles to expanding big data capabilities to more employees? Please select the top 2.

(% respondents)

37

32

27

22

22

20

17

11



Better decisions are being made faster

Identifying business opportunities that were not previously apparent

Identifying opportunities and exploiting them faster

Planning scenarios with greater confidence

Managing risks more effectively

Increasing revenues

Bringing products and services to market faster

Adapting to change is easier

Predicting outcomes with greater accuracy

Maximising business processes is easier

Reducing costs

Measuring outcomes has improved

Responding to third parties is more effective (suppliers, partners, customers, etc)

Other

Don’t know/Cannot predict

What do you consider to be the most important outcomes if your company expanded big data capabilities to more workers? Please select the top 4.

(% respondents)

63

45

37

31

31

29

28

23

18

18

10

10

8

1

2

United States of America

Canada

India

United Kingdom

Australia

Hong Kong, China, Singapore

Italy, Netherlands, Belgium, Brazil, France, Greece, Russia

Germany, New Zealand, Switzerland, Denmark, Hungary, Indonesia, Ireland, Malaysia, Mexico, Nigeria, Poland, Portugal, Romania, South Africa, Taiwan

In which country are you personally located?(% respondents)

25

9

8

6

4

3

2

1

North America

Asia-Pacific

Western Europe

Middle East and Africa

Latin America

Eastern Europe

In which region are you personally located?(% respondents)

34

27

25

6

5

4



49

11

17

6

16

$500m or less

$500m to $1bn

$1bn to $5bn

$5bn to $10bn

$10bn or more

What are your organisation’s global annual revenues in US dollars?(% respondents)

Board member

CEO/President/Managing director

CFO/Treasurer/Comptroller

CIO/CTO/Technology director

Other C-level executive

SVP/VP/Director

Head of Business Unit

Head of Department

Manager

Other

Which of the following best describes your title?(% respondents)

5

27

10

2

9

14

8

7

12

7

Professional services

Financial services

IT and technology

Healthcare, pharmaceuticals and biotechnology

Manufacturing

Entertaining, media and publishing

Consumer goods

Energy and natural resources

Education

Chemicals

Automotive

Government/Public sector

Retailing

Transportation, travel and tourism

Telecoms

Construction and real estate

Agriculture and agribusiness

Logistics and distribution

What is your primary industry?(% respondents)

14

12

11

11

9

5

5

5

4

3

3

3

3

3

2

2

2

2

General management


Finance

Marketing and sales

IT


R&D



Customer service

Legal

Risk/Security

What is your main functional role?(% respondents)

30

18

17

10

5

5

4

4

2

1

1

1



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 white paper or any of the

information, opinions or conclusions set out in the white paper.

Cover: Shutterstock

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