The road to big data adoption in Asia-Pacific

The hype and the hope
The road to big data adoption in Asia-Pacific
A report from The Economist Intelligence Unit

Commissioned by

The hype and the hope: The road to big data adoption in Asia-Pacific

Contents
Introduction

2

1) Survey demographics

6

2) The big data big picture

8

3) Barriers to adoption

12

4) Belief in benefits

15

5) Is bigger better?

20

Conclusion

23

Appendix: Survey responses by country

24

© The Economist Intelligence Unit Limited 2013

1


Introduction

“Big data”—the analysis of large quantities
of data to gain new insights—has become a
ubiquitous phrase in recent years. Businesses
now have the ability to collect incredible
quantities of information about their customers,
and managers recognise the need to analyse it
carefully. But across different geographies and
industries the adoption of big data technologies
and strategies has been far from uniform.
How far have businesses in the Asia-Pacific region
progressed with this trend? Who is adopting big
data, what gains do executives think their firm
can make from it and what factors might inhibit
its adoption? And, crucially, is there consistency
between how executives on the front line and
senior management understand its role within
their organisation? Is hype about big data
justified by its practical usage throughout the
business?
To find the answers to these questions The
Economist Intelligence Unit surveyed over 500
executives across Asia-Pacific, from a range of
industries and representing a range of functions.
Some 69% of respondents were from companies
with over US$500m in annual revenues.
Importantly, the survey respondents are a
mixture of senior and frontline managers, rather
than C-suite executives ultimately responsible for
2


decisions about big data strategy and investment
who are more likely to have a vested interest in
claiming the success of such initiatives.
The results show Asia-Pacific firms have had
limited success so far in implementing big
data practices. While a third say they are well
advanced, more than half say they have made
only limited progress. There is no single reason
for this, but overwhelmingly the issues are
internal. All but 9% of respondents cite factors
within their own firms as barriers to adoption,
including a group of issues involving difficulty
in sharing information across organisational
boundaries.
Significantly, companies slow to adopt big
data also tend to be poor in communicating to
employees their big data strategies. In fact,
over 40% of respondents were either not sure
whether their company had a big data strategy
or it had been poorly communicated to them.
Poor communication appears to be especially
problematic for frontline managers, who are
potentially among the biggest beneficiaries of
the insights that big data can bring. More than
twice as many frontline staff as senior executives
think their firm’s big data strategy hasn’t been
well communicated. Some 80% of frontline
employees believe improved access to data is


critical, but only 19% say they can always access
the data they need.
Despite the lack of progress, respondents
believe in the ability of big data to improve
their business: almost half think it can improve
revenue by 25% or more. This confidence is
shared even by those yet to adopt big data, where
more than 70% believe it can deliver gains in

productivity, profitability and innovation. Big
data may not yet be widely deployed in AsiaPacific, but this survey makes clear that it is
highly anticipated.
All figures within this report are derived from
data collected from a survey conducted by The
Economist Intelligence Unit.

Four things businesses should know about big
data in the Asia-Pacific region
Adoption is slower than you would expect:
More than half of firms have made little or no
progress in their big data strategies

Frontline employees can’t access the data
they need: 81% of employees say that critical
data resources are not available to them

Internal issues are inhibiting adoption: 91%
of companies cite internal issues as barriers to
big data adoption

Many believe in the benefits of data analytics:
Almost half of firms believe big data can
improve revenue by 25% or more


3

The road to big data adoption in Asia-Pacific

Continued on next page


1

Survey demographics

Figure 1
Geographical location of survey respondents
(% respondents)

Rest of Asia-Pacific

6%

ANZ

30%

14%
6%
15%

29%

Hong Kong

ASEAN

China

India

Figure 2
Industry split of survey respondents
(% respondents)

Other
Financial/professional services

27%

Manufacturing/automotive

IT/media/publishing

6

21%
5%
4%
5%

12%
11% 6%

9%


Healthcare, pharmaceuticals ad biotechnology
Telecommunications
Construction and real estate
Consumer goods/retailing
Government/public sector


Figure 3

Figure 4

Survey respondents by size of company

Survey respondents by job title

(% respondents)

(% respondents)

Other

31%

22%

$10bn or more
Manager

11%

$500m to $1bn

SVP/VP/Director

8%

$500m or less

15%

21%

31%

28%

$5bn to $10bn

$1bn to $5bn

Head of department

14%

19%

Head of business unit

Figure 5
Survey respondents by job function
(% respondents)

General management

4%

20%

Other
Procurement 1%
Supply-chain management 1%

2%
2%
2%

Legal

3%
3%

4%

Finance

7%

14%

8%
14%
Strategy and business development

13%

RD
Information and research
Risk
Human resources
Customer service
IT
Operations and production

Marketing and sales


7


2

The big data big picture

l Firms in the Asia-Pacific region have not
yet embraced big data, but they feel that they
should
Across the region, the majority of businesses
have barely begun using big data. Just over a
third say they have not made much progress while
another 21% have no strategy for using big data
at all (Figure 6). Some 36% of companies are
fairly well advanced in their adoption of big data,
but only 6% believe they are “very advanced”.

The reasons for slow adoption are diverse.
Respondents cite poor internal communication
and information sharing as well as a lack of
in-house skills and software. Nearly two-fifths
say their company’s big data strategy has not
been communicated well to them. The limited
take-up also flies in the face of the wider belief
that effective use of data matters; more than
three quarters believe it is critical to success.
(Figure 7)

Figure 6
Slow advancement in big data adoption
(% respondents)

6

Very advanced

36

Fairly well advanced

34

We haven’t made much progress

21

We haven’t started yet

3

Don’t know

Figure 7
The effective use of data resources will differentiate successful firms from failing firms
(% respondents)

25

Strongly agree

52

Agree

16

Neither agree nor disagree

5

Disagree
Strongly disagree
Don’t know

8

0
1



Figure 8
“Big data” is a confusing phrase and I don’t really understand what
impact it has on me or my organisation
(% respondents)

7

Strongly agree

25

Agree

30


25

Disagree

11

Strongly disagree
Don’t know

2

Figure 9
Failure to communicate
(% respondents)

Yes, my company has a big data strategy;
it has been well communicated to me.
Yes, my company has a big data strategy
but it has been poorly communicated to me.
Yes, I think my company has a big data strategy,
but it has not been communicated to me.
No, my company does not have a big
data strategy.
I don’t know whether or not my
company has a big data strategy.

27
20
18
32
3

l Uncertainty around what “big data” means
may also be hindering adoption
Only just over a third of respondents (36%)
disagree with the phrase “‘Big data’ is a
confusing phrase and I don’t really understand
what impact it has on me or my organisation”
(Figure 8). Despite widespread use of the term,
the survey shows that organisations are not
sufficiently communicating its meaning or value
to their frontline employees. Some 53% of
such employees haven’t been sufficiently well
informed about big data and are unclear about
its role within their organisation (Figure 9).
l Leading economies are lagging in their use
of data
The adoption of data strategies by businesses
in the region has been relatively poor, even in

some of the more advanced economies. The
worst performer is Singapore, where 47% of
respondents admit their organisations have no
big data strategy, as do 36% in Australia and
42% in China (Figure 10). Hong Kong lies at the
other end of the spectrum: only 21% have no big
data strategy.
More obviously, the survey data suggests that
poor adoption stems from poor strategy, with
absolute majorities in the worst-performing
adopters—75% in Singapore and 58% in China
and Australia—admitting they have made little or
no progress in their adoption of big data so far.
The results are supported by other research that
The Economist Intelligence Unit has conducted
on big data strategy that shows companies in the

9


Figure 10
Trailing in big data adoption and strategy
(% respondents)
Singapore

India

Hong Kong

China

Australia

Total

74.5

46.3
43.7

Haven’t made much progress/
haven’t started

58.1
58
54.3
46.5

My company has no strategy

26.3
20.9

41.9
35.8
31.4

Asia-Pacific region lagging their peers in the US
and Europe. For instance, in a separate survey of
317 C-level executives conducted in early 2013,
just 13% of respondents in Asia claimed their
businesses had a well-defined data management
strategy, compared to 21% in Europe and 23% in
the US.1

1
The Data Directive: How
data is driving corporate
strategy, and what lies
ahead, available at http://
www.economistinsights.
com/analysis/datadirective

10

l Many industries lag in their adoption of data
analytics
Aside from the IT and technology sectors,
which are traditionally early adopters of new
technologies, most industries are relatively
slow in their adoption of data analytics, with
many sectors having made or little no progress
at all (Figure 11). Two exceptions are the
telecommunications and retailing sectors, which
show significant levels of advancement in their
big data adoption. Early adoption may stem


from both of these industries generating and
storing vast amounts of customer data, which is
routinely used for marketing purposes through
initiatives such as store loyalty cards.
The healthcare, pharmaceuticals and
biotechnology industries, by contrast, are
the slowest adopters of big data, with 72% of
respondents stating that they have made little
or no progress. The prevalence of large and often
poorly connected IT infrastructures which exist
in many large state run and private healthcare
companies would partly explain the slow
adoption. With regulation around the use and
sharing of patient data continuing to develop,
data-security issues are also an inhibitor to the
widespread adoption of big data by healthcare
companies.


Figure 11
Many industries trail in big data adoption
(% respondents)
Haven’t made much progress yet/haven’t started yet

48


Consumer goods

26
47

Energy and natural resources

45

Financial services

60

72

21
39

Manufacturing

Telecommunications

52

49
46

IT and technology

Retailing

53

38


Professional services

52
65

Education

Healthcare, pharmaceuticals and
biotechnology

Advanced

40

57
56
65

31
32

68
38


62

11


3

Barriers to adoption

l A company’s biggest hindrance to gaining
value from big data is often itself
Virtually all companies surveyed recognise internal
roadblocks that prevent them or slow them from
adopting big data. All but 9% of respondents cite
hindrances of one kind or another. The biggest
obstacles are the lack of suitable software (42%)
and a lack of skills (40%) (Figure 12). To some
extent these can be remedied by external sources,
but other inhibitors stem from issues within the
organisation.

l An inability to share data is depriving firms
of the value that big data can provide
Firms in the Asia-Pacific region are experiencing
significant problems due to limits on sharing
and collaboration. Respondents cite an
unwillingness to share data (36%), a lack of
communication between departments (36%)
and departmental divisions (22%) as the biggest
inhibitors to big data adoption (Figure 13).
These figures are similar both for those who are
advanced in their adoption of big data and those

Figure 12
What’s holding back big data?
(% respondents)

42

Lack of suitable software

40

Lack of in-house skills
Lack of willingness to share data

36

Lack of communication between departments

36
32

Overly complicated reports

31

Lack of analysis yielding usable insights

22

Departmental divisions

18

No buy-in from management

4

Other, please specify
Nothing hinders our use of big data

12

9



Figure 13
Barriers to adoption
(% respondents)

Leaders

Laggards


38.3%
33.8%
49.5%

29.7%

49.2%


A lack of in-house skills and suitable software
are the most significant factors separating big
data leaders and laggards. Encouragingly, these
are areas which are not necessarily a result of
internal structural issues, and can be addressed
through the use of external technology providers
and staff training.


33.8%

35.5%

who are slower to adopt (defined in this report as
big data “leaders” and big data “laggards”).

37.1%

35.9%

26.8%


Figure 14
Firms of all sizes have silo problems
(% respondents)

66.1

$10bn or more

57.4

$5bn to $10bn
$1bn to $5bn

64.6

$500m to $1bn

64.6

$500m or less

l Information silos are stopping many firms
from innovating
Collaboration is essential to innovation: 59%
of respondents from the survey agree that
information silos are the biggest hindrance to
innovation in the ideas economy. That view is
held more widely by those from the very big
firms (66%), in which silos are more likely to
exist, than by those from small firms (47%)
(Figure 14).

posed by information silos, significantly
ahead of the survey average (59%) and
above government officials (63%). This may
well reflect tighter data security policies in
professional services, which provide barriers to
data sharing.

Comparing different industries, professional
services firms show the highest level of
agreement (75%) concerning the problems

47.3

This barrier to the effective use of data is
well recognised. A separate global survey
of 373 senior executives by The Economist

Total


58.7

13


Figure 15
Silo problems exist across Asia-Pacific
(% of respondents who agree)

Singapore


20.9

37.2
28.6

Malaysia

35.7

31.3

India

Hong Kong

18.4

25.8
17.3

Australia

Total

64.3
41.3
41.3

41.9
38.7

30.2
30.9

22.4

Intelligence Unit in August 2012 found that
cross-departmental input is essential for
deriving insights from big data, according to
some 64% of CEOs and 87% of other managers.
But while most CEOs think this is simple to
achieve, with 56% believing cross-department
collaboration is easy, only just over a quarter
of other managers have the same opinion.2
2
In search of insight and
foresight: Making the most of
big data, available at http://
com/analysis/searchinsight-and-foresight

14

62.8

40.5

29.7
China



36.6
36.4

Breaking down information silos is crucial but
not necessarily straightforward.
The new survey shows the silo problem is acute
in Malaysia and Singapore, where around 63%,
or nearly twice the regional average, cite a lack
of communication between departments as the
major inhibitor to effectively using data in their
organisations (Figure 15).


4

Belief in benefits

l Businesses in the Asia-Pacific region may be
slow in adopting big data, but there is a wide
belief in its benefits
In particular, 45% of respondents expect that
big data insights could boost revenue by 25% or
more by improving both decision-making and
understanding of customers (Figure 16). They
are similarly positive about the increased agility
that big data could bring. Across the board, 42%
expect a significant top-line revenue advance
from an improved understanding of new markets
and higher forecasting accuracy (Figure 17).
This complements the view of more senior
executives on the benefits of big data. In a
separate EIU survey of C-suite executives, for
example, 76.3% of those in the Asia-Pacific

region claimed greater volumes of data had led
to improvements in strategy (compared to 70%
in North America and 60% in Europe).3 The new
survey shows frontline employees expect such
strategic benefits to translate into tangible
gains.
l Industries overwhelmingly agree that
significant gains in customer insight will be
derived from the adoption of big data.
Telecommunications, consumer goods and
financial services firms are the most optimistic
industries, with over half of firms in these
sectors anticipating over 25% revenue increases
as a result of better client insight (Figure 18).
The first two of these industries also lead in
adoption, showing that it is potentially easier

Figure 16

Figure 17

Benefit of big data to your firm’s
understanding of customer needs

Benefit of big data to your firm’s ability to
adapt to market conditions

(% respondents)

(% respondents)

No change n/a

1 to 26%

50%

5%

No change n/a

Desterioration
0%

Greater
than 26%

45%

1 to 26%

7%

51%

Desterioration
0%

Greater
than 26%

42%

3
The Data Directive, http://
com/analysis/datadirective


15


Figure 18
Benefit of big data to your firm’s understanding of customer needs
(Estimated gain in terms of additional revenue or efficiency gains)
More than 25% improvement


1-25% improvement

44

8

Consumer goods

48
57

39

4

45

Education

53

3

Energy and natural resources

19

77

3

Financial services

52

43

6

46


3

51

39

Healthcare, pharmaceuticals and
biotechnology

54

7
39

IT and technology

59

32
33

Manufacturing

64

4
42

Professional services

11

47
48

Retailing

0
Telecommunications

52
67

33

0

Transportation, travel and tourism

31
8

for industries that generate a lot of data to use it
and therefore recognise its value.
In order for other industries to catch up in
adopting data analytics greater sharing of
16

No change


62

data will be required across sectors. This is
expected to lead to the trading and sale of
insights generated on consumer and customer
trends. Customer datasets are an increasingly


valuable asset, and industry sectors that
are large generators of this data (notably
telecoms, consumer goods and financial
service companies) will increasingly look to
monetise these asset. For companies to do this
successfully they will need to navigate through
the complex web of data privacy restrictions
which presently exist across the region.
l Companies are excited about big data but
frontline workers may not be aware of its
appeal
Companies are upbeat about the impact of big
data on productivity and profitability, with at
least 73% expecting significant improvement
in each of these (Figure 19). But this doesn’t
appear to be fully communicated down the
line. Comparing those on the front line with
senior executives (SVPs, directors and heads of
departments), nearly half of the former say their
company’s big data strategy has not been clearly
communicated to them—more than twice the
proportion of senior executives who think the
same (23.5%).
The research suggests companies are failing
to communicate their big data strategy
sufficiently to their frontline employees. Poor

communication may lead to employees being
uncertain about the benefits of big data to their
company and therefore being slow to adopt its
practices.
l Big data laggards are still confident about
gaining value from it in the future
Even those firms who have not fully embraced
big data (data laggards) are positive about its
potential. That could be a sign of frustration over
their access to key data— 46% of the laggards in
big data adoption say they can’t always obtain
the information resources they need, a rate two
and a half times higher than the big data leaders
(Figure 20). Even so, 73% of the trailing group
are confident the adoption of big data would
improve their firm’s innovation, while 74% see
a rise in productivity and 71% expect greater
profitability (Figure 21).
l IT professionals in Asia-Pacific are more
positive about big data and its benefits than
other job functions
IT managers, who might be expected to be
more knowledgeable about big data than other
job functions, are also much more confident
about their companies’ usage of big data than
their colleagues. Some 68% believe their

Figure 19
Upbeat about the innovation, profitability and productivity improvements big data can bring
(% respondents)
Improve productivity

Improve profitability

Improve innovation

73

Agree/strongly agree

Disagree

78

2
3
3

Strongly disagree

79

0
1
0

Don’t know

18
18

23


17


Figure 20
Leaders and laggards, accessibility to data resources
(% respondents)
Laggards

Leaders

Total

9.6

Yes, I can always access
the data resources I need.

19.3

32.3
37

Yes, most of the time I can access
the data resources I need.

42.5

Yes, but I suspect there are some
additional data resources available
that would make my job easier.

17

50.2

46.3
33.9

7.1
0.4
4.3

No, I can never access the data
resources I need.

Figure 21
Leaders and laggards, productivity, innovation and profitability benefits
(% respondents)
Laggards

Leaders

73.6

Productivity

73.1

Innovation

Figure 22
IT professionals see benefits
My company is advanced/well-advanced in big data adoption
(% respondents)

67.5

IT
Finance

41.8

General management

41.7
30.4
organisation is advanced or very advanced in big
data, far above the average of 43% and ahead

18

81.3

70.5
76.9

Profitability

Marketing and sales

86.2


of marketing managers (31%) and general
management (42%) (Figure 22). They are also
more confident that they have access to the
data they need to do their job, with 85% of this
opinion compared to the regional average of
62%, Meanwhile 92% believe big data will deliver
productivity gains and 85% expect greater
profitability.
The views of the IT department perhaps need
to be taken with a pinch of salt: IT executives
often have a vested interest in demonstrating
the success of the systems they install; they are


also often better trained in getting the most out
of them. Poor communication between the IT
function and other departments may also hinder
the effectiveness of big data solutions, as the
above findings suggest. Indeed, it is notable that
when it comes to internal communication of big

data strategy, the views of the IT function are
broadly in line with those of other departments.
Just over a third (35%) believe their company’s
big data efforts have been poorly communicated
or not communicated at all, only marginally
below the average of 38%.


19


5

Is bigger better?

l Big companies are more likely to use big data
There is a clear correlation between size of
company and progress in adopting big data: larger
firms in Asia-Pacific are better at adopting big
data strategies than their smaller counterparts.
Among the very largest firms in the survey, with
global annual revenues above US$5bn, 57% report
being well advanced (Figure 23). By contrast,
less than a quarter of the smallest firms—those
with revenues of less than US$500m—describe
themselves as advanced, while fully three quarters
have made little or no progress.
This suggests that large firms with substantial
data resources to manage are more driven to

seek the benefits of large-scale analytics than
smaller businesses. It could also reflect the
understandable resources gap between small
and large firms. Indeed, a lack of in-house skills
is cited as a hindrance to adopting big data by
nearly three-quarters of small companies, far
more than any other revenue group and nearly
twice the survey average (39%).
l Bigger companies expect greater gains in
agility from big data
Large firms in Asia-Pacific have higher
expectations about the value big data will
generate compared to smaller firms, suggesting
a ratcheting effect the more resources can

Figure 23
Small firms trail in big data adoption
(% respondents)
$10bn or more

$5bn-$10bn

$500m-$1bn

$500m or less

38.3
39.4

Haven’t made much progress/
haven’t started

51.7
50

Very or well-advanced

23.1

20

$1bn-$5bn


43.9
48.8

56.7
57.3

74.5


be brought to bear in the application of big
data. Over half of all companies with revenue
of US$5bn or more anticipate a revenue boost
of 26% or more from greater business agility –
significantly higher than the 38% of small firms
(with revenues less than US$500m) (Figure 24).
Some 43% expect a similar revenue boost from
greater insights into new markets, compared
with 32% of small businesses.
Very large firms are also the most optimistic
about the impact on productivity (87%) and
innovation (86%) (Figure 25). However, medium
and small-sized businesses also view big data
positively. Small businesses poll 70% or above
in their expectations for improved productivity,
profitability and innovation.
l Businesses which are advanced in
implementing big data are also the most
effective in ensuring employees understand
the strategy.
The survey suggests early adopters of big data
(data leaders) are making the greatest efforts
at ensuring that staff understand their big
data initiatives. Some 58% of data leaders

Figure 24
Large businesses anticipate larger gains in business agility
(% respondents)

$10bn or more
$500m-$1bn

$5bn-$10bn
$500m or less

$1bn-$5bn
Total

44.1
37.7


54.1

43.8
37.8
41.8

30.8
27.9
36.8
37.5
26.9
31.5

11-25% improvement

11.5

20.8

21.9
13.8
18.6
16.3

1-10% improvement

4.2
6.6
3.5
5.1
No change n/a

8.3

16.8

Figure 25
Large businesses anticipate larger gains in productivity, innovation and profitability
(% respondents)
Productivity

Innovation

Profitability

$10bn

75.7

82
78.7
75.5

$5bn-$10bn

81.6
81.5
78.1

$1bn-$5bn

$500m-$1bn

$500m

87.4
85.8

68.8

75
77.6

72
71.5
69.7


21


have a clearly communicated strategy, while
of those who haven’t implemented big data
(data laggards), 90% either have a poorly
communicated strategy or no strategy at all
(Figure 26).

strategy successful; while a quarter say they
don’t know.
When viewed in terms of company size, a
similar result is apparent. Small companies see
themselves as the least successful in big data
(32%) but also have the highest proportion of
employees who are unsure about their firm’s big
data plans (23%), above the regional averages of
49% and 15% respectively.

Among the leaders, 86% also believe their
data strategy is successful, and just 2% are
not certain. By comparison, 55% of those who
haven’t implemented it do not consider their

Figure 26
Getting the message across
(% respondents)
Leaders

We have a strategy that is
well-communicated

22

26.8

15
13

Uncertain because of
poor communication

Don’t know

57.6

4.8

Our strategy is
poorly-communicated

No strategy

Laggards

22

2.2
0.4
5.1


53


Conclusion

This research has shown that companies in
the Asia-Pacific region are not as advanced
in the adoption of big data analytics as
one might expect, particularly within the
developed markets of Singapore and Australia.
Communication problems appear to be a root
cause of slow adoption, with the presence of
information silos and an inability to share data
inhibiting firms from benefitting from big data.
However, respondents to this survey have shown
that there is a strong appetite for an increased
use of data analytics within their companies.
They recognise the value that increased insight
can bring in terms of productivity, profitability

and innovation. Big data leaders have shown
that an effectively communicated data strategy,
better training and improved access to data can
significantly improve a firm’s ability to adopt, and
gain value from data analytics.
Smaller firms face the greatest challenge in
taking advantage of big data analytics. With
limited technology resources compared to their
larger counterparts, smaller firms can help bridge
the skills and technology gap by leveraging
external technology providers. Encouragingly,
small and large firms alike are confident that,
given the right tools, they can reap the benefits
generated by big data.


23


Appendix:

1.

Survey responses by country

Does your company have a big data strategy? If so, how well have its purpose and goals been communicated to you?
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Yes, my company has a big data strategy; it has been well communicated to me.
25
0

23
28
36
23

Yes, my company has a big data strategy but it has been poorly communicated to me.
19
17
20
25
13

Yes, I think my company has a big data strategy, but it has not been communicated to me.
18
21
23
17
13

No, my company does not have a big data strategy.
36
37
26
21
42

I don’t know whether or not my company has a big data strategy.
3
3
4
2
10

24



2.

How advanced do you think your company is in its adoption of big data?
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Very advanced
4
3
8
11
0

Fairly well advanced
34
29
43
44
39

We haven’t made much progress
31
38
35
31
32

We haven’t started yet
27
28
11
13
26

Don’t know
4
1
4
1
3


25


3.

Which of the following sources of data do you use in your job? Select all that apply.
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Traditional data (eg, databases)
89

92
91

72
90

Social media (eg, Facebook, Twitter, Youtube, blogs, etc)
37
29

38
39

29

Machine generated data (eg, sensors, smart grid, RFID, network logs, telematics, etc)
20

23
33
30

16

Location-based information (eg, GPS, mobile logins, etc)
23
21
16

24
30

Contact centre data (eg, audio conversations, text chats, customer emails, etc)
35
36
39
41
42

Staff data (eg, Emails, calendars, instant messaging, etc)
71
60
64
56

68

Open data (eg, data released by governments)
57
58
41
37

68

Syndicated data from third-party data providers (eg, market data, weather, etc)
53
58
45
37

26

45



4.

Is the data you need for your job easily accessible?
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Yes, I can always access the data resources I need.
19
10
22
25
16

Yes, most of the time I can access the data resources I need.
39
42
38
50
42

Yes, but I suspect there are some additional data resources available that would make my job easier.
38
38
35
23
39

No, I can never access the data resources I need.
5
8
5
2
3


27


5.

What hinders the effective use of big data in your organisation? Select all that apply.
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

31

55

41
30
39

Lack of willingness to share
30
33

41
41

42

40

49

44
41
39

31

35

33

35

26

43
45
33
34

48

No buy-in from management
19
23
6
22

13

18
23

31

18
26

Lack of analysis yielding usable insights
30
30
26

Other, please specify
8
5
4
1
0

Nothing hinders our use of big data
11
8
6
3

28

8


38
45


6a. For each of the following issues how much would insights from big data affect your department? China
(% respondents)

Regulatory compliance
Risk management
Knowledge management and transfer
Forecasting accuracy
Management/strategic decision making

0

3
3

7
7
7
7

Ability to develop new products/services
Understanding of new markets
Agility (adapt to rapidly changing market conditions)
Understanding of needs of customers/clients

10
10

26-50% improvement
10

29
13

29

48

30
26

36

29

11-25% improvement
36

23
29
29

48
30

32

36

32

1-10% improvement
13

16
19
19

7
13

20
16
16

No change
13
7

10

29

36

16

10
10
10

Would lead to a deterioration
0
0
0
0
0
0
0
0

3

N/A
0
0
0

3
3
3
3
3

7


29


6b. For each of the following issues how much would insights from big data affect your department? Hong Kong
(% respondents)

Risk management

8

12

10
6

8

11


14

10
12

26-50% improvement
20
20

21
26
26

27
29

31
31

11-25% improvement
27

35
31
28

34

30

36
35

1-10% improvement
20
21
17

19
19

No change

6

8
8

5

1

13

8
8

10

0
0
0
0
0

1

1

2

1

N/A
0
0
0
0

30

2
1

29

26

2

1
2

27
27

40


6c.

For each of the following issues how much would insights from big data affect your department? India
(% respondents)

Risk management

13

15

9

11

24

13


25

20
38

26-50% improvement
21

28
33

25

33

29

38

28

20

11-25% improvement
17

26
23
21

34

25
29
33

19

1-10% improvement
30

19

14
16

14

22
15
15

23

No change

4

1
1

5
5

6

9

14

4

0
0

1
1

0
0
0
0
0

N/A
3
3
3

6
4
4
4
4

6


31


6d. For each of the following issues how much would insights from big data affect your department? ASEAN
(% respondents)

Risk management

12
12

13
15

10

12


24

13
17

26-50% improvement
13

19
27

15

27

29
31
32

33

11-25% improvement
21

26

29

36

24

32
31

1-10% improvement
29
21
22

13
12
12

21
17

No change
9
9

5
5
8

3

23
12

9

0
0
0
0
0
0
0
0
0

N/A
0

1

3

1
1

32

3

3
3

4


31

35

36


6e. For each of the following issues how much would insights from big data affect your department? Australia/New Zealand
(% respondents)

Risk management

8

12

14
15
13

14


16
16
17

26-50% improvement
12

20
25

27

25
22

27

24
24

11-25% improvement
22

23

28
26

29

37

29

27

30

1-10% improvement
25

23
23
15

11

19
17

21

24

No change
17

7

12

8
7

28

10

11

8

0
0
0
0
0
0
0
0
0

N/A

2
2

3
3
3
3
3

5

5


33


7a. For each of the following issues how much would insights from big data affect your company? China
(% respondents)

Risk management

3

7
7

3
7
7


10
10
10

26-50% improvement
23
19

29
26

42

29
29

39

32

11-25% improvement
19

32
42

26
29

36
39
42

1-10% improvement
13

16
16

10

19
19
19

10

16

No change
10
3
7
3

36

13
19

10
10


0
0
0
0
0
0

3
3

7

N/A
0
0
0
0
0
0
0
0
0

34


45


7b. For each of the following issues how much would insights from big data affect your company? Hong Kong
(% respondents)

Risk management

6

7

10
8
9


10
13
13
10

26-50% improvement
21

24
25

27

25

22

26

23

27

11-25% improvement
33
31

32

37

32
32

39
37
38

1-10% improvement
21
20
20

25
22
26

21
22
22

No change

6

2

3

12

10

8
9
9

11

0

0
0
0

1
1
1
1
1

N/A
1

0
0

1

3
2
2

1
1

35


7c.

For each of the following issues how much would insights from big data affect your company? India
(% respondents)

14

18

9

19
11

20

14

23
30

26-50% improvement
20

23
29

25

34
33

28

35

30

11-25% improvement
25

31

34

21

18

30

33

34

15

1-10% improvement
20

17
19
15
15

24

20
20

18

No change
6
4
4
0

5
6

3

0
0
0
0
0
0
0
0

11
9

Risk management

1

N/A
3
3
3
3
3
3

36

5

5
5



7d. For each of the following issues how much would insights from big data affect your company? ASEAN
(% respondents)

Risk management

10

13

10

21

12

19

13


22

19

26-50% improvement
13

17
29

22

28
28

29

40

24

11-25% improvement
26
24

19

26

28

28

33

24

37

1-10% improvement
28
21

15

22

17
17

31

24

19

No change
12

6
3

5

8
8

21
14

8

0
0
0
0
0
0
0
0
0

N/A
0
0

0
0

1
1
1
1
1


37


7e. For each of the following issues how much would insights from big data affect your company? Australia/New Zealand
(% respondents)
10

13
13

Risk management

15


19

17
17

19
18

26-50% improvement
13

16
19

27

19

28

23

20

27

11-25% improvement
23

28

30

27

29
29

1-10% improvement
22
13
13

22
20

21

24

28

23

No change
16

7
5
6
4

12
8

7

0
0
0
0
0
0
0
0

1

N/A
2
2
2

2

38

4
3
3
3

4


27

31
31

33


8a. Big data would improve productivity in my department
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Strongly agree
19
17
27
15
23

Agree
58
54
57
62
71

Don’t know
19
28
15
22
3

Disagree
2
1
1
1
0

Strongly disagree
1
0
0
1
3

8b. Big data would improve profitability in my department
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Strongly agree
13
14
28
8
10

Agree
48
53
38
60
42

Don’t know
32
29
29
27
45

Disagree
7
4
4
5
0

Strongly disagree
0
0
1
0
3

39


8c.

Big data would improve innovation in my department
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Strongly agree
26
19
27
17
10

Agree
46
59
62
60
68

Don’t know
22
17
10
21
19

Disagree
5
5
1
3
0

Strongly disagree
0
0
0
0
3

8d. Big data would improve productivity in my company
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Strongly agree
22
18
34
16
26

Agree
54
59
51
63
61

Don’t know
21
23
13
18
7

Disagree
3
0
3
3
3

Strongly disagree
1
0
0
0
3

40



8e. Big data would improve profitability in my company
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Strongly agree
15
21
34
16
13

Agree
57
58
39
56
63

Don’t know
24
19
19
25
20

Disagree
4
1
8
4
0

Strongly disagree
0
1
1
0
3

8f.

Big data would improve innovation in my company
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Strongly agree
24
19
34
16
13

Agree
53
62
51
61
71

Don’t know
19
14
14
20
13

Disagree
4
5
1
3
0

Strongly disagree
0
0
0
0
3

41


9.

Do you think your organisation’s overall big data strategy has been successful?
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Overall very successful
3
4
9
9
7

Somewhat successful
39
32
48
56
33

Not very successful
29
37
24
24
30

Not successful at all
12
9
3
6
7

Don’t know
17
18
18
6
23

42



10a. A lack of data insights hinders quick decision-making and agility
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Strongly agree
21
27
44
12
26

Agree
58
50
46
61
65

15
19
6
19
3

Disagree
4
3
1
8
3

Strongly disagree
0
0
0
0
3

Don’t know
2
1
3
1
0


43


10b. The effective use of data resources will differentiate successful firms from failing firms
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Strongly agree
26
36
38
12
26

Agree
53
45
53
56
61

15
9
8
27
10

Disagree
4
3
1
8
0

Strongly disagree
0
0
1
0
3

Don’t know
1
1
1
1
0

44



10c. Data driven market insights would allow us to better allocate RD resources and go to market faster with new products
and services
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Strongly agree
19
31
26
14
16

Agree
52
51
56
52
77

21
14
11
29
3

Disagree
4
3
4
5
0

Strongly disagree
1
0
0
0
3

Don’t know
2
1
3
1
0


45


10d. Better data access and sharing would improve productivity and efficiencies in our organisation
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Strongly agree
22
31
38
16
23

Agree
57
54
48
60
65

17
13
13
20
10

Disagree
3
1
1
3
0

Strongly disagree
0
0
0
1
3

Don’t know
2
1
1
0
0

46



10e. Information silos are the single largest inhibitor for innovation in the ideas economy
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Strongly agree
22
28
22
10
13

Agree
35
44
46
45
32

27
18
18
38
39

Disagree
11
8
11
5
7

Strongly disagree
1
0
3
2
3

Don’t know
4
3
1
1
7


47


10f. Senior managers who make investment decisions are more concerned about the quantity of data we gather than the
quality of insights we get from it
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Strongly agree
11
18
18
10
7

Agree
30
36
26
36
23

28
24
38
37
32

Disagree
22
12
16
16
29

Strongly disagree
7
4
1
2
7

Don’t know
2
6
1
0
3

48



10g. “Big data” is a confusing phrase and I don’t really understand what impact it has on me or my organisation
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Strongly agree
8
6
8
6
10

Agree
24
29
15
33
13

28
23
24
33
48

Disagree
25
29
29
22
19

Strongly disagree
12
10
23
5
10

Don’t know
2
1
3
1
0


49


10h. Consumers’/clients’ concerns about data privacy mean the risks of collecting data outweigh the benefits
(% respondents)

AUST/NZ

ASEAN

India

Hong Kong

China

Strongly agree
7
4
13
11
0

Agree
23
29
25
37
23

20
33
33
29
50

Disagree
38
26
20
19
20

Strongly disagree
9
4
8
4
3

Don’t know
3
4
3
1
3

50


While every effort has been taken to verify the accuracy
of this information, The Economist Intelligence Unit
Ltd. cannot accept any responsibility or liability
for reliance by any person on this report or any of
the information, opinions or conclusions set out
in this report.

Cover image - Dave Simonds

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