Big Data is a buzz term with global traction. But while interest and awareness is high, is that buzz being converted effectively into significant economic activity in Malaysia? What are the inhibitors to driving Big Data solutions? And where are the opportunities we should nurture? In this presentation, Big Data Malaysia shares insights from a new survey based on a range of stakeholders in this emerging industry.

1. Converting Buzz into Activity
in the Big Data Ecosystem
Sandra Hanchard & Tirath Ramdas
Big Data Malaysia
Big Data World Show
JW Marriott, Kuala Lumpur, November 2013

2. 10 events since May 2012
Plus 9 during
•
A networking group for people passionate about data.
•
We talk about applications in social media analytics, financial data,
consumer insight, telecommunications, etc.
•
Participants from end-users, vendors, academia – engineers, analysts,
managers, professors, entrepreneurs.
•
Wide technical breadth (Hadoop, R, Greenplum, Postgres, Cassandra, MongoDB,
0MQ, Prudsys, Storm, Acunu Analytics, Google BigQuery, Dremel, Oracle, Datasift,
Tableau, GPUs, NetApp, Hive, Hbase, AWS, MySQL, Teradata…).
www.BigDataMalaysia.org

3. Rationale
Opportunities and inhibitors to Big Data
activity in Malaysia?
 Who’s interested vs. involved?
 What is the current and future capacity for
big data skills?
 Where are the critical gaps in skills?
 What are the soft inhibitors, including data
access, regulation and perception?
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3
November 2013

4. About the survey..
Methodology
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Content
Collection over October 2013
Distribution via Big Data
Malaysia email & social
media channels and partners
108 respondents over 90
organizations
Not intended to be
representative. Illustrative of
Big Data Malaysia network
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About you
Enabling Your Organization
End-Uses
Skills
Capabilities
Data Sources
This deck contains preliminary findings: we are generating hypotheses
for further analysis.
4
November 2013

5. Lucky draw sponsors
5
November 2013

6. Technical assistance
6
November 2013

7. Half respondents in ICT, even spread
across other sectors
Information and communications technology
Marketing services
Professional, scientific, and technical services
Educational services
48%
Media and Classifieds
Finance and Insurance
Government
7%
n = 108
7
8%
Manufacturing
Not applicable e.g. I am a student
Other
Preliminary findings - November 2013

8. 48% management, 40% practitioner
CEO, or equivalent
C-level:
24%
CIO/CTO, or equivalent
24%
Senior/middle manager
C-level Other
Software engineer
Practitioner:
40%
Analyst
Emerging number of
Data scientists (6%)
Who are they?
Data scientist
Technical consultant (e.g. pre-sales)
Academic or Scientist
Full time student
Other
0
5
10
15
20
No. respondents
Practitioners include; Software engineers, Analysts, Data scientists, Technical consultants and Academic/Scientists.
8
Preliminary findings - November 2013
25
30

9. No. respondents
Top management concentrated in SMEs,
other roles spread Enterprise > Boutique
20
C-level
15
10
5
0
No. respondents
Employees exceeding Employees from 200 Employees from 75 to Employees from 5 to Employees of less than
1000
to not exceeding 1000 not exceeding 200
less than 75
5
12
10
8
6
4
2
0
Senior/middle manager
No. respondents
Employees exceeding Employees from 200 Employees from 75 to Employees from 5 to Employees of less than
1000
to not exceeding 1000 not exceeding 200
less than 75
5
9
Self-employed
Self-employed
15
Practitioner
10
5
0
Employees exceeding Employees from 200 Employees from 75 to Employees from 5 to Employees of less than
1000
to not exceeding 1000 not exceeding 200
less than 75
5
Preliminary findings - November 2013
Self-employed

10. Some complacency in how data
leveraged, bullish anticipated spend
My organization is effectively
deriving tangible benefits from
our organizational data assets
How do you expect your spend on
Big Data to change in 2014
compared to 2013?
100%
21%
Strongly agree
Increasing by more than 25%
% Respondents n=108
Increasing by between 10% and
25%
39%
Agree
50%
Increasing by between 5% and
10%
Increasing by less than 5%
29%
0%
Neutral
4%
7%
Disagree
Strongly disagree
No change
0
Don’t know/prefer not to say = 16
Managers (n=75) defined as respondents who selected ‘yes’ to having managerial responsibility in their role.
10
Preliminary findings - November 2013
5
10
15
20
No. ‘Managers’
25

11. ..but actual and planned headcount
remain low
Big Data Headcount (HC): Current vs. Next 12 months
Low HC,
High growth
HC Next 12 months
> 50
1
21-50
1
1
4-10
1-3
Don’t
know/Prefer
not to say
7
11
4
2
6
1
1
1
8
1
3
No. ‘Managers’
5
2
4
1-3
4-10
11-20
High HC,
Low growth
2
None yet
Analysis based on Managers; excluding those who selected ‘Don’t know/Prefer not to
say’ for Current headcount. n=68.
11
High HC,
High growth
1
3
11-20
None
Low HC,
Low growth
3
Current HC
Preliminary findings - November 2013
21-50
> 50

12. ICT high outsourcing intent
suggests technical fragmentation
How willing would you be to
outsource high-skill tasks in
your Big Data initiatives to
external consultants?
How do you expect your spend on Big
Data to change in 2014 compared to
2013?
Non-ICT
100%
Increasing by more than 25%
24%
% Respondents
42%
Increasing by between 10%
and 25%
Quite/Extremely
willing
24%
Moderately willing
Increasing by less than 10%
50%
27%
Slightly willing
24%
0%
Not at all willing
12%
28%
18%
0%
Non-ICT
ICT
n=29
12
ICT
n=33
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10% 20% 30% 40% 50%
Boutique opportunities amongst
ICT, but Non-ICT also priority
targets given matching expected
spend
Preliminary findings - November 2013

13. Whatever Big Data offers, we’re all
focused on the ‘customer’
End-uses ranked by priority of relevance
All
Non-ICT
Customer behavioural profiling
Customer service and/or experience
Competitive intelligence
Customer retention
Social trends monitoring
Customer acquisition
Customer cross-sell and/or up-sell
Forecasting supply and demand
Brand monitoring
Product and service innovation
Operational cost management
Risk management
Supply-chain monitoring
Infrastructure and assets monitoring
Compliance and regulatory issues
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ICT
103
101
105
Very relevant
104
108
117
104
115
115
135
132
158
111
Moderately relevant
Slightly relevant
Not all relevant
n=56
ICT greater
production focus
as well as
Forecasting
n=52
Where are your blind spots? Are you too internally or externally focused with
aspirations.
Big data providers: Specialise or provide holistic solutions?
Swing is an indexed number based on relevance score.
13
Non-ICT greater
external focus e.g.
social trends &
brand monitoring
110
109
n=108
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Industry swing by All
Preliminary findings - November 2013

14. Managers have higher aspirations
for End-uses vs. practitioners
End-uses ranked by priority of relevance
All*
Customer behavioural profiling
Customer service and/or experience
Customer retention
Customer cross-sell and/or up-sell
Customer acquisition
Competitive intelligence
Social trends monitoring
Forecasting supply and demand
Product and service innovation
Brand monitoring
Operational cost management
Risk management
Supply-chain monitoring
Infrastructure and assets monitoring
Compliance and regulatory issues
All* excludes Students and Others
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Very relevant
Moderately relevant
Slightly relevant
Not at all relevant
n=95
Role function swing by All
Practitioner
Manager
109
102
114
104
111
101
107
109
110
107
105
124
113
108
91
108
n=44
Practitioners have
stronger internal
org. focus
n=51
Managers need to align perception of Big Data’s ‘value’ throughout
organization with business objectives.
What internal end-uses are being overlooked by Managers?
Swing is an indexed number based on relevance score.
14
Managers’ key
priorities:
 Profiling
 Retention
 Acquisition
Preliminary findings - November 2013

15. Desired skills: distributed data
analysis, nod to fundamentals
Capabilities ranked by priority of need
Industry swing by All
All
Specialised data analysis, modeling, simulation (op.research, machine learning)
Distributed systems (e.g. Hadoop) deployment and/or administration
Fundamental computer science and/or software engineering
Industry-specific/domain knowledge
Applied math and/or statistics
Web/mobile development and/or visualization
Research experience from any quantitative discipline
Business (strategy, marketing, product development, etc.)
Hardware/sensor design
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101
109
123
High need
Little need
No need
Those with Intermediate/Advanced skills prioritize
distributed systems
Those with Basic tech skills prioritize domain
knowledge
Soft skills undervalued (Strategy, marketing etc.)
No love for Internet of Things (hardware/sensor
design skills).
Swing is an indexed number based on need score.
15
ICT
106
119
109
113
116
n=108

Non-ICT
Critical need
Preliminary findings - November 2013
111
n=56
n=52
Skill-level swing by All
Intermediate/
advanced
105
117
122
Entry
102
111
103
104
101
n=47
n=61

16. Specific skills: ICT demands
Hadoop, Non-ICT wants algorithms
1.6
Key priority areas:
Normalized Priority Score
1.4
1.2
1
0.8
1.
2.
3.
4.
Big and Distributed Data (Hadoop, MapReduce)
Algorithms (computational complexity, CS theory)
Machine Learning (decision trees, neural nets, SVM, clustering)
Back-End Programming (e.g. Java/C++/Python/Rails/Objective C)
0.6
0.4
0.2
0
ICT (n=35)
16
Combined (n=61)
Non-ICT (n=26)
Preliminary findings - November 2013

17. Desired capabilities: uncovering
and visualizing patterns in real-time
Capabilities ranked by priority of relevance
Industry swing vs. All
All
Real-time insights from real-time data streams
Uncovering patterns (e.g. segments, correlations) from multi-structured data sets
Visualizing/presenting insights
Data discovery and exploration across many data sources
Statistical analysis on big working data sets (>100GB)
Automated decision making
Machine-generated data (e.g. log files, periodic diagnostics)
Content and sentiment from online media (e.g. social media)
Efficiently and safely storing large data sets on infrastructure controlled by my org.
Image, video, and audio data
Physical sensor networks (e.g. "Internet of Things")
Non-ICT
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120
137
173
109
Slightly relevant
Not at all relevant
n=56
n=52
Clear desire to derive ‘meaning’ from Big Data (i.e. insights)
Those in a non-ICT role more likely to prioritize content; social and mediarich data (i.e. very unstructured data)
Swing is an indexed number based on relevance score.
17
110
125
107
119
119
117
136
Moderately relevant
n=108

ICT
Very relevant
Preliminary findings - November 2013

18. Strong willingness to outsource
bodes well for service providers
Willingness to Outsource swing
vs. All Managers
Capabilities ranked by priority of relevance
All
Managers
Visualizing/presenting insights
Uncovering patterns (e.g. segments, correlations) from multi-structured data sets
Real-time insights from real-time data streams
Data discovery and exploration across many data sources
Statistical analysis on big working data sets (>100GB)
Automated decision making
Efficiently and safely storing large data sets on infrastructure controlled by my org.
Machine-generated data (e.g. log files, periodic diagnostics)
Content and sentiment from online media (e.g. social media)
Image, video, and audio data
Physical sensor networks (e.g. "Internet of Things")
Unwilling

Moderately relevant
Slightly relevant
Not at all relevant
n=38
Top priority by Managers to communicate ‘meaning’ from Big Data
(visualization & insights)
Desire for ‘discovery’: leverage through better information management
Swing is an indexed number based on relevance score.
18
111
120
113
103
124
120
122
146
130
167
110
Very relevant
n=75

Willing
Preliminary findings - November 2013
n=37

19. High-commitment managers had
higher prioritization of capabilities
Forward-capacity swing vs. All
Managers
Capabilities ranked by priority of relevance
All
Managers
High-FC
Visualizing/presenting insights
Uncovering patterns (e.g. segments, correlations) from multi-structured data sets
Real-time insights from real-time data streams
Data discovery and exploration across many data sources
Statistical analysis on big working data sets (>100GB)
Automated decision making
Efficiently and safely storing large data sets on infrastructure controlled by my org.
Machine-generated data (e.g. log files, periodic diagnostics)
Content and sentiment from online media (e.g. social media)
Image, video, and audio data
Physical sensor networks (e.g. "Internet of Things")
Cautious-FC
Moderately relevant
102
102
109
109
114
124
129
107
113
Slightly relevant
Not at all relevant
117
Very relevant
n=46
120
n=31
n=15
Only exception was media-rich data
Forward capacity: Measures resource commitment
*Headcount *Expected headcount *Expected spend *Willingness to outsource
..which will increase likelihood of delivering desired Big Data outcomes
Swing is an indexed number based on relevance score.
19
Preliminary findings - November 2013

20. Organizations are sourcing data
both internally and externally
My organization uses sources for Big Data initiatives primarily from the following:
None yet
ICT (n=45)
7%
27%
20%
11%
36%
Internal data
Open-access third-party data (incl. government)
Non-ICT (n=47)
17%
28%
6% 6%
43%
Proprietary third-party data
Combination
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20
ICTs more open sources; Non-ICTs
should prioritize content opportunities
e.g. data journalism
Respondents focused on profiling
customers more dependent on thirdparty data e.g. social media
Respondents who selected "Very high"
for relevance of Customer behavioural
profiling
Combination
Internal data
Open-access third-party data
Proprietary third-party data
None yet
Sorted by highest %. Bars illustrate
sw ing against remaining sample
Preliminary findings - November 2013
104
59
146
313
87
n=45

21. Open data from Government
needed to support ecosystem
Having access to some government
data does/will create valuable Big
Data opportunities for me
100%
% of respondents n=108
21%
Strongly agree
19%
Agree
49%
Neutral
4%
6%
What kinds of government data will assist
you?
 Demographic; socioeconomic; behaviour
 Population (online & offline), migratory
 Crime (by ethnic group); Border Security
 Public &community services (utilities,
health, education)
 Location (by utility); GIS
 Financial; credit
 Weather
Disagree
Strongly disagree
50%
0%
21
Preliminary findings - November 2013

22. Government data needed for
benchmarking & consolidation
“In order for us to understand the needs of Malaysians, statistical data from the
population census is important to identify correlations with internal behavioural
data” – Head of Decision Science, MNC bank
“Government has many different sets of survey data, collected from various
sources. For instance, Ministry of Health data can be sourced from private and
general hospitals, clinics or consultancies. Big data offers a mechanism to speed
up consolidation of all this information, without any processing delays to
configure each and every source.” - Jin Chuan Tai
Director, ChrysaSys Consulting Sdn Bhd
22
Preliminary findings – November 2013

23. General wariness of “red-tape”,
PDPA identified as biggest concern
Do you believe your local legal/regulatory
environment a hindrance to your planned
Big Data initiatives?
100%
Not at all a hindrance
2%
% of respondents n=108
13%
47%
Not a hindrance
What hindrances in particular?





Personal Data Protection Act 2010 (PDPA)
Red-tape
Bureaucracy and organizational structure
Data compliance and data risk
Loss of data
 15% “Don’t know”: greater education
around PDPA needed?
Neutral
50%
17%
Moderate hindrance
6%
Severe hindrance
15%
Don’t know
“Regulation does prevent some of our
products or product features being deployed
in some markets.” – Head of Development,
global marketing analytics firm
0%
23
Preliminary findings - November 2013

24. Less than a third willing to upload
internal data to a Cloud service
I am willing to upload my internal
data to third-party infrastructure
(e.g. a public cloud)
100%
% of respondents n=108
Strongly agree
20%
50%
6%
Vendors need to identify concerns –
privacy, migration cost, perceptions
specific to Malaysian professionals
Agree
37%
Opinion is divided starkly
amongst High-forward
capacity respondents.
Neutral
High-forward Capacity
23%
14%
0%
24

Disagree
Strongly disagree
Strongly agree
Agree
Neutral
Disagree
Strongly disagree
Bars represent swing against
Bars illustrate sw ing against
Cautious-forw ard Capacity
Preliminary findings - November 2013
175
113
41
210
153
n=31

25. Some conclusions..
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25
Strong ‘Grass-roots’ and Mid-tier support for
Big Data in Malaysia. Unknown at local
Enterprise, C-level.
Aspirations high but Human Resource
commitment a concern.
Immediate skilling priorities include R and
Hadoop.
Opportunities for boutique firms in Malaysia
to meet specialist technical needs with global
punch.
Preliminary findings - November 2013

26. ..further research
How are Big Data budgets being split
between infrastructure / personnel / data?
 Who qualifies as a ‘Data scientist’ – what
skills do they have, and what value do they
add?
 How can Big Data activity contribute to
Malaysia’s push to become a high-income
nation by 2020?
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26
November 2013

27. 5 Questions to ask yourself today
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27
Where are your aspirational blind-spots?
Internal vs. External.
Are your aspirations unrealistic?
Are you committing resources aggressively
enough?
Are you prioritizing the right blend of skills?
Are you driving/participating in cultural
change for Big Data advocacy?
November 2013

28. Get in touch
Tirath Ramdas
tirath@bigdatamalaysia.org
Founder
Sandra Hanchard
sandra@bigdatamalaysia.org
Researcher
You can find us on..
 Feedback / questions / comments
 What do you need to know?
www.bigdatamalaysia.org
 How can Big Data Malaysia serve
your organization?
November 2013