This document discusses Intel's strategy to accelerate big data adoption in the Asia Pacific region over the next few years. It aims to deploy Apache Hadoop 2 years faster on Intel Xeon processors. Key opportunities mentioned include telecoms, financial services, government and healthcare. The strategy seeks to unlock value from data, support open platforms, and deliver software value from the edge to the cloud. Target sectors include OEMs, system integrators, independent software vendors and training partners.

1. APAC Big Data Strategy 2013
RK Hiremane
APAC Product Marketing Manager
Datacenter and Embedded Systems

2. Lighting Up Unused Data for Big Impact
Acceleration of adoption of Hadoop
Apache Hadoop deployed on Intel Xeon
2 years faster
Units
Intel® Xeon processor growth from big data use
2013 2014 2015 2016 2017

3. Where is the Opportunity?
• Telecommunications, financial services
• Government, healthcare
• Not just in the mature markets
1. Improve services to customers (& people)
2. New business opportunity to grow revenue

4. Democratize data analysis from edge to cloud
Unlock value of Data
Support Open Platforms
Deliver software value
Intel can deliver end-to-end from the edge intelligent systems to
the Datacenter/cloud

5. GTM Strategy
• OEMs
• System Integrators
• Independent Software Vendors
• Training partners

6. Flytxt Overview
Vision, Mission & Achievements Customers
 Our vision to create >10% economic value for Telcos from their
data using Flytxt’s Big Data Solutions
 Decisioning Logic Units, derived from raw data through patent
pending complex domain specific analytics technologies, drive
the applications
 Red Herring Asia top 100 winner; NASSCOM Emerge 50; IEEE Sample text
Cloud Computing Challenge Winner
Sample text
Company
 300+ employees. Management team with 150+ years in Telecom
technology & business
 Dutch Company with its competencies centre at Trivandrum, India and
offices at Delhi, Mumbai, Hong Kong, Dhaka, Lagos, Nairobi and Dubai
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Copyright © 2013 Flytxt B.V. All rights reserved 2/27/2013

7. Flytxt
• Provide telcos with big data
applications which increase demand, Correspondence
revenue, loyalty, and customer Analysis
satisfaction AND that reduce churn, Descriptive Variance
revenue leakage, fraud and direct Analysis Analysis
costs Big
Data
• Highly scalable platform: deployed Clustering Deterministic
across 400M+ subscribers Analysis Analysis
• Proven: 2% to 7% economic benefit Predictive
Analysis
to customers

8. The team that introduced virtualization, grid and HPC x86 cluster computing to Singapore
eLinux
Itanium Rocks!
APAC
Software Kit
1999 2001 2002 2003 2006 2009 2010 2012
• Next-generation HPC/Cloud/Analytics infrastructure provider. Customers include A*STAR, STEE, NEC, Singtel.
• More than a decade of experience architecting and implementing leading edge HPC and Grids, and now Cloud
systems.

9. Dr. Zhao Pei
VP Business Development

10. Revolution Confidential
Laurence Liew
General Manager, APAC
Revolution Analytics

11. Revolution Confidential
Revolution Analytics is the leading commercial provider of software
and support for the
open-source R statistical computing language
 Enterprise-ready
 Multi-platform
 Scalable from desktop to big data
 Delivers high performance analytics
 Easier to build and deploy analytic applications
Founded 2008 Number of customers 200+
Office Locations Palo Alto (HQ), Seattle (Eng) Investors Northbridge Venture Partners,
Singapore (APAC HQ) Intel Capital, Presidio Ventures
London (EMEA HQ)
CEO David Rich
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12. Revolution Confidential
200 Corporate Customers and Growing Revolution Confidential
Finance & Insurance Healthcare & Life Sciences
Academic & Gov’t Consumer & Info Svcs Manuf & Tech
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13. Revolution Confidential
Real-time
Big Data
Predictive
Analytics
Stack
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14. A Use Case for Retail Segment Revolution Confidential
 Analyze and optimize marketing mix
 customer segmentation
 Revenue/ action attribution among channels and marketing
programs
 Customized Next Best Action per individual prospect or customer;
data granularity feeds the big data challenge
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15. Telco- China Mobile Group Guangdong
Hadoop & Xeon optimized Big Data storage & analytics
• Challenge: Dealing with large volume of data and
delivering real time access to Call Data Records
(CDR) for billing self service
• Solution: Chose Hadoop + Xeon over RDMS to
remove data access bottlenecks, increase storage,
and scale system
• Benefits: Lower TCO, 30x performance increase,
stable operation, analytics on subscriber usage for
targeted promotions
• Data Characteristics:
– 30TB billing data/month
– Real-time retrieval of 30 days CDRs
– 300k records/second, 800k insert speed/sec
– 15 analytics queries
– 133 server nodes

16. Summary
• Accelerating the adoption of big data analytics
• Engaging with ecosystem and end-customers to unlock the value of data
• Delivering the full end-to-end capability of Intel from the edge intelligent
systems to servers in the datacenter or cloud and with the Intel
Distribution for Apache Hadoop

17. BioScience: Genomics for Translational Medicine
Hadoop for Data Correlation & Discovery Insights
• Challenge: Derive new value added patient
discovery services while bringing down genome
processing costs
Data Ingest
• Solution: Dynamically partition & scale
correlation of patient data to all public data
using Hadoop and Hbase
• Benefits: Contributes to 800x reduction in cost
to process 4 Million genome variants Billions of Pre-
computed Correlations
• Infrastructure and Data Characteristics:
• 10 Node Hbase Cluster
New Biomed Info-Products
• Billions of pre-computed correlations
1 Genome  10 Million rows
100 Genomes  1Billion rows
1M Genomes  10 Trillion rows
100M Genomes  1 Quadrillion 1,000,000,000,000,000 rows

19. Public Sector- Smart Traffic Intelligent Transport System
Hadoop for Predictive Analytics
• Challenge: Analyze city traffic to derive statistics Regional Data Collection
for crime prevention, info sharing, and predictive
traffic analysis
• Solution: Embed HBase client in camera for real-
time inserts of structured/unstructured data App Servers
• Benefits:
Distributed Processing Across District Nodes
• Automated queries for traffic violation
• Data mining of fake licenses <1 minute for all data
captured for a week
• Predictive traffic forecasting
• Data Characteristics:
Derived Analytics Services
• 30000 + camera data collection points
• Petabytes of traffic data & terabytes of images
• 2 billion HBase records
Crime Prevention Citizen Traffic Services
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