Big Data in Hong Kong -- Dr. Toa Charm

Copyright © by 2014 All rights reserved.
What's hindering Hong Kong
firms in push for big data?
Dr. Toa Charm
toacharm@gmail.com
May 15, 2014
Hong Kong

 Industry Experience in Business Intelligence and Big Data
– Associate Partner, Business Analytics, Greater China, IBM GBS
– Regional Head of BI Competency Centre (BICC), Asia Pacific, HSBC
– General Manager, Business Intelligence, Greater China, Oracle
– Managing Director, Greater China, Hyperion
– General Manager, Asia Pacific, Kingdee International Group
 Accomplishment in Business Intelligence and Big Data
– Established 1st Business Intelligence Competency Centre (BICC) in HSBC Asia Pacific
– Founder and Chairperson, BI Special Interest Group, Hong Kong Computer Society
– Won Hong Kong Computerworld Best BI Award, Hyperion’s Asia/Pacific Best Partnership Award, Hyperion’s
Best Marketing & Best Consulting Award
– Forum Chairs/Moderators: Big Data Business Forum (US), Hong Kong BI and Analytics Forum (2011-2013),
Retail Analytics Forum, Cloud Asia, Insurance Analytics, Finance Innovation, BankTech Forum, etc.
 Qualification and Publications
– Doctor of Business Administration, MBA, B.Sc.
– Doctoral Thesis: Impact of Organizational Capabilities on Business Intelligence Maturity and Customer
Relationship Management Performance
– Author of two books to be published in 2014 – “Strategic Success of BI and Big Data Journey in Greater
China” and “Strategy - Make or Break Our Company and Career Lives”.
– Adjunct Professorship: University of Hong Kong, Fudan University (Shanghai), University of Macau
– Completed senior executive programs from Harvard, UC-Berkeley, MIT, CEIBS
Dr. Toa Charm 湛家揚博士
Founder & Chairperson, BI and Big Data SIG
Hong Kong Computer Society
DBA , MBA, B.Sc. , CBIP (TDWI), Big Data Certification (MIT)
2
2

Big Data Success Vs CEO and CIO Resigned
3

Big Data Workshop
- Outline
4
1. Big Data in a Nutshell
2. Big Data Business Values and Innovations
3. Big Data Challenges in Hong Kong
4. Future Trends

1. Big Data
in a Nutshell
5

Big Picture of Big Data
6

Variety Brings in Complexity
7

Big Data is the Source of
Sustainable Competitive Advantages
“From labor-based productivity to
data-based productivity is the key
for the West to regain advantages ”
“Data will become valuable assets，
and will become a part of our
balance sheet”
~Kenneth Cukier, co-author of the best-selling big data
book “Big Data – A Revolution that will transform how
we live, work, and think
8

Financial Impact of Big Data
- Mckinsey
9

12 Disruptive Technologies (2013-2025)
- Big Data is a CSF of all 12 technologies
10

Worldwide Analytics and BI Survey
- Ranked as TOP priority by Global CIOs
3 times in 5 years
Source: Gartner Research, 2014 11

Source: Forbes Retrieved from http://www.forbes.com/sites/gilpress/2013/10/30/top-10-most-funded-big-data-startups-updated/ on Jan 18, 2014
Big Data
Innovators
and New
Challengers
12

http://www.bigdatalandscape.com/
Born of a New Set of Players
- Big Data Vendors and
Big Data-Driven Companies
13

Big Data Workshop
- Outline
14
4. Future Trends

Copyright © by 2014 All rights reserved. 15Source: TDWI, 4th Quarter 2013
1. Cost Savings
2. Fast and Better Decisions in Existing
Functions
3. Data-based Products and Services
4. New Business Models
Source: Big Data @work, Thomas Davenport, 2014
Some Key
Business
Drivers
for Big
Data

Business
Metamorphosis
Data
Monetization
Business
Optimization
Business
Insights
Business
Monitoring
Big Data Business Model
Maturation Index
Measures the degree to which your
organization has integrated big data and
advanced analytics into
your business model
Traditional BI
Big Data
16

GE predicts that they will contribute to the
world GDP by 1,500 billion dollars
simply by cost savings through big data
17

Crowdsourcing for Analytics
- Allstate Underwriting
19
Predictions of bodily injury claims based on automobile characteristics
Crowdsourced competitions have yielded stunning successes. In a competition sponsored by Allstate in
2011, top Kaggle contestants easily beat the performance of Allstate’s best baseline model for predicting
which autos covered by policies would be involved in bodily injury claims. The winner's model was 270%
more accurate than Allstate's baseline model, and the insurer has since incorporated key elements into
the models it now uses.
Source: Doug Henschen, Executive Editor, InformationWeek, Mar. 7, 2013
http://www.informationweek.com/software/business-intelligence/kaggle-winners-tapped-as-data-analytics/240150254

The Secret Weapon of Obama
for His Presidential Election
20

Google
- Flu Trends
Early detection of a disease outbreak can reduce the number of people affected. If a new strain of influenza virus
emerges under certain conditions, a pandemic could ensue with the potential to cause millions of deaths (as
happened, for example, in 1918). Our up-to-date influenza estimates may enable public health officials and
health professionals to better respond to seasonal epidemics and pandemics
Source: http://www.google.org/flutrends/about/how.html
21

The Movie “MoneyBall”
- Oakland’s A (Baseball Team)
A New Way of Thinking in a Traditional Industry
Source: http://www.youtube.com/watch?v=WNlCBy07z08
22

Final Jeopardy! and IBM Watson
- The Power of the IBM Big-Data Machine
“Watson”
Source: http://www.youtube.com/watch?v=lI-M7O_bRNg
Watson won the game!
23

Citibank is Making use of
Watson..
• Citi and IBM have agreed to explore the first consumer banking applications. Citi will
focus on how the machine’s deep content analysis and evidence-based learning
capabilities could improve customer interactions and simplify the banking experience.
• Financial advisers could capture a holistic view of a client’s situation in life, their
tolerance for risk, and the best outcomes, based on past performance, of people in the
same demographic and psychographic groups. From this information, banks could
anticipate the needs of their customers. They’ll be able to stage three-way conversations,
including Watson, to help their clients explore questions deeply and make sound
decisions…. More to come
Source: Manoj Saxena, General Manager,IBM Watson Solutions
http://asmarterplanet.com/blog/2012/03/taking-watson-to-the-bank.html
24

Big Data Application
- Insurance
25

Zest Finance
ZestCash, Inc., a financial services technology, startup committed to serving the needs of the
underbanked, today announced a new credit decisioning infrastructure (patent pending) that
can run multiple underwriting models in parallel, each with a different focus, to better analyze
credit risk. ZestCash also introduced Hollerith, a new set of underwriting models that allow
the company to extend credit to 25 percent more Americans and increase repayment from
customers by 20 percent. 26

Progressive Auto Insurance
- Disruptive Premium Pricing
Source:
http://www.cio.com/article/736686/How_Progressive_Uses_Telematics_and_Analytics_to_Price_Car_Insurance
27

Linkedin is Making Use of Big Data
- Data-based Products
28

The Richest in China, Forbes 2013
- Internet + Big Data Savvy Leaders
Net Worth
US$M Age
Robin Li, Baidu
Pony Ma, Tencent
Jack Ma, Alibaba
29

Platform Strategy Examples
- Alibaba and Tencent
30

Big Data
Value
Potential
by
Industry
31

Big Data Made
the Industry Border Blurred
• Small and innovative analytics firm gets into FSI
– ZestFinance makes loan to customers with bad or no credit histories basd on a lot
more variables instead of FICO credit scores
– Wonga offers loans for very short periods by looking at different data sources and
make credit decisions on the fly
– Cignifi digs deep into mobile data of callers to get clues about their propensity to
repay loans
• Smaller banks recognize the importance of data, keep their own credit
card business and leverage on Cloud-based big data analytics
• Tesco collects huge amounts of data on its customers’ shopping habits
that allow it to send precisely targeted coupons. The firm has banking
ambitions and has already launched credit cards and loans and plans to
introduce full bank accounts.
• Other firms help customers at the expense of banks
– Mint pulls togethers all banks information of a customer
– ReadyForZero, SaveUp, Zopa, Prosper and more, bypass banks entirely, letting savers lend
directly to borrowers.
32

Big Data Workshop
- Outline
33
4. Future Trends

Google Trend – “Big Data”
United States and Hong Kong
34

BI & Analytics Adoption in Hong Kong
is Far behind the Overall Asia
35
Asia sees big data
a low technology priority
HK is behind the rest of Asia
in taking advantage of Analytics
Source: Computerworld Hong Kong, 2013

Big Data Adoption
in Asia Pacific
- by industry
36
Source: Economist Intelligence Unit 2013

Obstacles Importance Type
Lack of understanding how to use
analytics to improve business
38% Organizational
Lack of management bandwidth due to
competing priorities
34% Organizational
Lack of skills internally in the line of
business
28% Organizational
Ability to get the data 24% Data
Culture does not encourage sharing
information
23% Organizational
Ownership of the data is unclear or
governance is ineffective
23% Data
Lack of executive sponsorship 22% Organizational
Concerns with the data 21% Data
Perceived costs outweigh the projected
benefits
21% Financials
No case for change 15% Financials
Figure 1-2: Primary Obstacles to Widespread Analytics Adoption (IBM and MIT, 2010)
Figure 1-1: 2013 Global CIO Top 10 Technologies (Gartner, 2013)
Research Problem # 1
Uprising Demand on BI
and
Organizational Challenges
• CIOs ranked BI as their annual top priority
3 times in the last 5 years including 2012
& 2013. It is expected to remain popular
with the maturity of Big Data.
• Organizational issue is the top challenge
for BI success rather than Data and
Financial issues. These CIOs need to
manage these organizational issues well
in order to meet the strong demand of BI
and deliver the expected values of BI.
• Little is known on organizational issues of
BI program success.

Research Problem # 2
• BI aims to enhance company performance
including CRM performance.
• As BI gets mature in a company, its CRM
Performance is expected to be higher.
Higher BI Maturity leads to a higher return
of investment as shown in TDWI BI
Maturity Model.
• However, different enterprises are getting
different results even they put same
amount of investment on BI projects.BI Maturity Model (Davenport and Harris, 2007)
TDWI Maturity Model (Eckerson, 2007a)
TDWI Maturity Model (Eckerson, 2007a)

The Concepts Map indicates that the three key organizational
capabilities identified in this study have a positive impact on BI
Maturity. this implies that enhancing these organizational
capabilities may help a company advance to a higher level of BI
Maturity, as discussed earlier in this Chapter.
My Doctoral Research
Concepts Map
Another indication seen from the Concepts Map is the
moderating effects of the three variables identified in
the research, indicating that Industry moderates the
relationship between Staff Capability and BI Maturity
but only slightly moderates the relationship between BI
Team Capability and BI Maturity. There is no indication
that Industry moderates the relationship between
Senior Management Capability and BI Maturity.
In addition, the Concepts Map indicates that Drivers for
Change moderates the relationship between Staff
Capability and BI Maturity but only slightly moderates
the relationship between BI Team Capability and BI
Maturity. There is no indication that Industry moderates
the relationship between Senior Management
Capability and BI Maturity.
Regarding the impact of Organizational Nature, it
moderates the relationship between BI Team
Capability and BI Maturity but only slightly moderates
the relationship between Staff Capability and BI
Maturity, as well as the relationship between Senior
Management Capability and BI Maturity.
Last but not least, the Concept Map indicates that BI
Maturity has a positive impact on CRM Performance.
As explained in Section 6.4, the higher the BI Maturity
is, the better the CRM Performance will be. However,
there are a few exceptions where BI Maturity is high
but the CRM Performance is relatively low. There
might be some moderating or mediating variables
between BI Maturity and CRM Performance that cause
this kind of variations, as discussed in Section 6.4.
Figure 6-11: Concepts Map

Data Analytics
- People, Roles and Skills
42

Data
Analytics
- Technologies
43

Strong Demand of Analytics and Data
Science Experts around the Globe
According to Mckinsey, Just US
alone faces a shortage of 140K to
190K people with deep analytical
skills as well as 1.5 million
managers and analysts to analyze
big data and make decisions based
on their findings.
Source: Mckinsey Global Institute
44
By 2015, big data demand will
reach 4.4 million jobs globally but
only one third of those jobs will be
filled.
Source: Gartner

20 Master’s Programs on
Big Data Analytics and adding up
e.g. UC-Berkeley
Source: Doug Henschen, Information Week, January 08, 2013 09:06 AM
http://www.informationweek.com/big-data/slideshows/big-data-analytics/big-data-analytics-masters-degrees-20/240145673
45

Data Privacy is the Biggest Enemy of
Big Data Long-Term Success
+
46

Big Data Workshop
- Outline
47
4. Future Trends

Gartner Predicts
Big Data Needs 5 years to Reach Maturity
- Hype Cycle for Emerging Technologies 2013
48

Some of the Key Elements of Big Data
Has Reached Maturity
- Hype Cycle for Big Data 2013

Future Trends
• More proven cases
• Influence or disrupts more industries and companies
• More data scientists and data-savvy managers
– More online programs, M. Sc. Data Science, Certification for Professional Qualifications, etc.
• Technology maturity
• Increase complexity in 3 Vs
– Wearables, IOT, sensors
• Regulation and Laws for Data Privacy
• More Open Data from governments
• Adoption
– Freemium
– Gamification
– Visualization
– Mobile
– Payment
• Still needs to resolve toughest organizational challenges on the top of
technologies, data and finance
• .. More
50

Big Data in Hong Kong -- Dr. Toa Charm

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