Transcript Big Data: The next frontier for emerging market

BIG DATA
The next frontier for emerging market
USC CSSE Annual Research Review
March 14, 2013
Rachchabhorn Wongsaroj
Bank of Thailand, Visiting Scholar @ USC
Outline
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Current situation
What is big data?
Why big data is important?
Big data cases
Research challenges
Big data in Thailand
Future research
Current Situation
Global data
Data Quality
Data Quantity
Problems
Data Timeliness
Lots of data is being
created & collected
Data Variety
What is big data?
Big Data = Volume, Variety and Velocity
Volume
Variety
People to
People
People to
Machine
Machine to
Machine
Velocity
8 Billion
messages/day
845M active users
20 Hours of
video uploaded
every minute
340Million
Tweets/day
140M active users
Source: Gartner & IBM
Why big data is important?
Emerging Technologies Hype Cycle 2011 (Gartner)
Why big data is important?
Emerging Technologies Hype Cycle 2012 (Gartner)
Why big data is important?
Source: McKinsey Global Institute Analysis
Why big data is important?
Big data can generate significant financial value across sectors
US Health Care
$300 billion value/year
̴ 0.7 % annual
productivity growth
Europe Public Sector
Administration
Global Personal
Location Data
£250 billion value/year
̴0.5 % annual productivity
growth
$100 billion +revenue for
service provider
Up to $700 billion value
to end users
US Retail
Manufacturing
60+% increase in net
margin possible
0.5-1.0 % annual
productivity growth
Up to 50% decrease in
product development
Up to 7% reduction in
working capital
Source: McKinsey Global Institute Analysis
Why big data is important?
Health Care sector has potential to invest $300B
14% $47B
Accounts advanced fraud
detection: performance
based drug pricing
49% $165B
Clinical transparency
in clinical data and
clinical decision support
R&D
$47B
Account
32% $108B
$108B
R&D
R&D personalized medicine,
clinical trial design
2% $5B
Business Model aggregation
of patient records, online
platform and communities
$165B
Clinical
3% $9B
Public health surveillance
and response systems
Business Model
Public
Clininal
Account
Source: US Department of Labor
Big data cases
Cases
Data sources / Techniques
Output
Google patient search data,
Predictive Model, etc.
Hospitalization pattern,
Customized insurance
Advanced analytic solutions
Process time reduction
Customer transactions
Customer defection
prediction
Trading transactions & IP address
Possible Frauds, Financial
Bubble, Money Laundering
Real time people & location data
Crime and terrorist
prevention
Product search pattern,
social media
Website outage/peak time
support, Travel trend and
pattern
Research Challenges
Function
Big data retail lever
Marketing
 Cross-selling
 Location based marketing
 In-store behavior analysis
Customermicro-segmentation
micro-segmentation
 Customer
Sentimentanalysis
analysis
 Sentiment
 Enhancing the multichannel consumer experience
Merchandising
 Assortment optimization
 Pricing optimization
 Placement and design optimization
Operations
 Performance
transparency
Performance
transparency
 Labor
Laborinputs
inputsoptimization
optimization
Supply Chain
 Inventory management
 Distribution and logistic optimization
 Informing supplier negotiations
New Business Model
 Price
services
Pricecomparison
comparison
services
 Web-based markets
Source: McKinsey Global Institute Analysis
Big data in Thailand
Challenges
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Language
Cost of implementation
Magnitude of data
Demographic data generator
Data type
Big data in Thailand
Language (natural language processing)
 no space between words
 Combination between Thai –Foreign languages
Lack of Thai text analytic components
Example
Big data in Thailand
Cost of implementation
13 Big data vendors in 2013
Hadoop :
Requires:
~$1 million between 125 and 250 nodes
Distribution:
Annual costs: ~$4,000 per node
-> A small fraction of an enterprise data
warehouse $10-$100s of millions.
Big data in Thailand
Magnitude of data
As of September 2012
60% use
Local Bandwidth
44%
31%
14%
9%
Local Bandwidth
(.th, or.th, etc)
1,006,140 Mbps
Overseas Bandwidth
405,860 Mbps
25% use smart phone
8% use tablet
Big data in Thailand
Demographic data generator
Most data are from young generations
Population
65M
Internet users
25M
39% of population use Internet
85.9% of data is created by Internet
users age 6-24
Big data in Thailand
Types of data – limited Big data technique application
Only 2.12% focus on Education
Source: http://www.prd.go.th/ewt_news.php?nid=23168
Bank of Thailand (BOT)
Website – As is
Manual
Checking
Financial institution
BOT data
(Internet/
Extranet)
DB 1
DB 2
Problems
 Too many steps
 Once due - act first, fix later
 Too many stakeholders
 Bureaucracy management style
DB3
Template
Input
Manual Submit
BTWS
Working
Auto Submit
BOT
Website
Source: Bank of Thailand
BOT data website – As is
Volume
Revision
Policy
Timeliness
Manual Checking
Variety
Input
Data
Complex
Validation
Cross
Validation
Manual
Check
Query
Data (BO)
Input
Template
Manual
Submit
Website
VelocityApprove
Accuracy
& Reliability
Source: Bank of Thailand
Future research
 Data quality management
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Tools
Template
Checklist
Process
Reference
 Big Data: The next frontier for innovation, competition, and
productivity, McKinsey Global Institute Analysis
 Understanding Big Data: Analytic for Enterprise Class Haddop
and Streaming Data, IBM
 Gartner Report
 Thailand National Statistic Office
 Thailand Digital Statistic Source
 Bank of Thailand (www.bot.or.th)
BIG DATA
The next frontier for emerging market
Thank you
Q&A
Rachchabhorn Wongsaroj
Bank of Thailand
Visiting Scholar @ USC