The web-conference hosted by CRISIL Global Research & Analytics on “Big Data’s Big Impact on Businesses” on January 29, 2013, saw participation from senior officials of global multinationals from 9 countries. The presentation described how data analytics is helping businesses make “evidence-based” decisions, thereby creating a positive impact. It also spoke about the opportunities opening up in the Big Data space in India and across the globe. Hosted by: Sanjeev Sinha, President, CRISIL Global Research & Analytics Gaurav Dua, Director & Practice Leader (Technology, Media & Telecom), CRISIL Global Research & Analytics

1. Big Data’s Big Impact on Businesses
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2. Big Data’s Big Impact
on Businesses
Webconference : Jan 29, 2013

3. Key Takeaways
Slide 3
Introduction to Big Data
Slide 5
Global Landscape and Trends
Slide 12
The Big Data Opportunity
Slide 20
Big Data’s Big
Impact on
Businesses

4. Key Takeaways
 Big Data market opportunity is expected to witness strong growth in the next 5 years
– Expected to touch US$25 billion globally; the ‘BIG’ opportunity for India lies in the IT & IT-enabled
Services space, which is likely to be ~US$ 10-11 billion market globally in 2015
– India is likely to garner a ~10% share of the ~US$ 10-11 billion global Big Data IT Services Market by
2015
– Data-related regulations like Dodd-Frank and Basel III to impact Big Data implementations
 Initially, North America & Europe are likely to drive the Big Data opportunity since
over 85% of the world’s data is today residing in these 2 regions
 New database architectures and innovative analytics tools & techniques to facilitate
Big Data implementations
 By end of 2012, around 90% of Fortune 500 companies had some initiatives underway
related to Big Data
 Key verticals driving demand for Big Data analytics: Financial services, Retail,
Telecom, Healthcare and Manufacturing
 Key risk – potential shortfall of 1.5 million Data-Savvy Managers and 140,000-190,000
Data Scientists in the US by 2018
Source: CRISIL GR&A analysis
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5. Key Takeaways
Slide 3
An Introduction to Big Data
Slide 5
Global Landscape and Trends
Slide 16
The Big Data Opportunity
Slide 23
 Definition of Big Data
 Big Data ecosystem
 Benefits of Big Data to enterprises
 Key applications for end
consumers

6. Big Data is Defined by Volume, Variety and Velocity
What is Big Data ?
Big Data relates to rapidly growing, Structured and Unstructured datasets with sizes beyond the ability of
conventional database tools to store, manage, and analyze them. In addition to its size and complexity, it refers to
its ability to help in “Evidence-Based” Decision-making, having a high impact on business operations
Speed, Accuracy and Complexity of Intelligence
3Vs 1 Large quantity of data
Small Data Sets Big Data which may be enterprise-
Volume specific or general and
public or private
Advanced Big Data
analytics analytics
2
Diverse set of data
being created, such
Variety as social networking
Small Data Sets Big Data feeds, video and
audio files, email,
sensor data and
Traditional Traditional other raw data
analytics analytics Velocity
3
Speed of data inflow as
Gigabytes Terabytes Petabytes Zetabytes well as rate at which this
fast-moving data needs to
Size of Data be stored
Source: CRISIL GR&A analysis
Source: CRISIL GR&A analysis
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7. The Global Data likely to Grow at a CAGR of 41%
Growth of global data, 2009-2020
Zetabytes CAGR
35.0
(2009-2020)
41.0%
7.9
1.9
0.8
2009 2011 2015 2020
Implication for an organization
 Need for large storage capacity and quick retrieval of data
 Enable informed decision-making effectively, leveraging large data sets
– Turn 12 TB of Tweets created each day into improved product sentiment analysis
– Convert 350 billion annual meter readings to better predict power consumption
Note: ZB stands for Zetabytes;
Source: IDC; CRISIL GR&A analysis
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8. Volume
Today 80% of Data Existing in any Variety
Enterprise is Unstructured Data Velocity
Introduction  Resides in formal data stores – RDBMS and Data
Structured Data Warehouse; grouped in the form of rows or columns
 Variety of sources from where data is being
 Accounts for ~10% of the total data existing currently
generated has also undergone a shift
RDBMS (e.g., Data Microsoft Project
 The types of data being created has changed from ERP and CRM Warehousing Plan File
structured to semi-structured to unstructured data
 A form of structured data that does not conform with the
Implication for organization Semi-
formal structure of data models
Structured Data
 Need to manage broad range of data types  Accounts for ~10% of the total data existing currently
 Process analytic queries across numerous data
types
 Comprises data formats which cannot be stored in row/
Unstructured column format like audio files, video, clickstream data,
Data
Solutions required  Accounts for ~80% of the total data existing currently
Weather Location
 Need to extract meaningful analysis from this data Video Audio Text message Blogs patterns co-ordinates
has led to several technologies to gain traction
 Examples include NoSQL databases to store
unstructured data as well as innovative processing
Web logs & Sensor data/ Geospatial
methods like Hadoop and massive parallel clickstreams M2M Email Social media data
processing (MPP)
Source: Industry reporting; CRISIL GR&A analysis
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9. Volume
Big Data will Enable Real Time Analytics Variety
Velocity
Big Data velocity enabling real
600+ time use of data
videos on
YouTube 200  Big Data is also characterized by
1,500+
blog posts million+ velocity or speed i.e. frequency of
emails sent data generation or the frequency of
data delivery
2  New age communication channels
7,000+ million+
photos on Google such as mobile phones, emails, social
flickr search networking has increased the rate of
Data queries information flows
velocity
per Examples:
minute
700,000+ 400,000+  Telcos adopting location based
minutes of
Facebook
Skype marketing based on user location
updates sensed by mobile towers
calling
 Satellite images can help monitor and
US$ 3500+ analyze troop movements, a flood
300,000+ ticks per
are spent minute in plane, cloud patterns, or forest fires
on online 400,000+ securities
shopping trading  Video analysis systems could monitor
tweets on
Twitter a sensitive or valuable facility,
watching for possible intruders and
alert authorities in real time
Source: Industry reporting; CRISIL GR&A analysis
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10. Big Data Analytics is Application of Advanced Techniques on Big
Datasets; Answers Questions Previously Considered Beyond Reach
Evolution of analytics Big Data analytics
Behavioral analytics Advanced
 Big Data analytics is
analytics
where advanced Stochastic Analytic
analytic techniques Complex optimization database  Why did it
Prescriptive are applied on Big event happen?
functions
analytics Data sets processing Constraint  When will it
Optimization
 The term came into Extreme SQL
based BI happen
play late 2011 – early Visualization again?
Level of Complexity
2012 Predictive
Social network analytics  What
modeling
caused it to
Predictive Semantic analytics
Forecast happen?
analytics
- ing  What can be
Time series analysis
Statistical done to
analysis Natural Language Processing avoid it?
Multivariate statistical analysis
Alerts Online analytical processing (OLAP)
Descriptive Query Data mining
analytics drill
down
Adhoc Basic analytics
reports  What happened?
Standard  When did it happen?
reports  What was the its impact ?
Late 1990s 2000 onwards
Time
Analytics as a separate value chain function In-database analytics
Source: CRISIL GR&A analysis
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11. Big Data Management, Analytics, IT Services & Applications
are the Key Constituents of Big Data Ecosystem
Four key elements: What does the Big Data Ecosystem Constitute ?
1. Big Data Components of Big Data Ecosystem
Management &
storage: End users
 Data storage Big Data Analytics
application and use
Data analytics & its
infrastructure Applications
and technologies Developer Environments Analytics (mobile, search, web)
(Languages (Java), products
(SI,customization, consulting, system design)
2. Big Data Analytics Environments (Eclipse &
NetBeans), programming (Avro, Apache BI
 Includes the Thrift)
interfaces (MapReduce)) &visualization
technologies and
tools
tools to analyze the
data and generate Input data
insight from it
Business analysts
3. Big Data’s Data
IT services
Application & Use Sources Big Data
Operational Data
 Involves enabling
Data management &
Unstructured Data Architecture NoSQL
the Big Data data Hadoop/ Big Data NoSQL MPP
insights to work in (Text, web tech’y framework Hadoop RDBMS
storage
BI and end-user pages, social (MapReduce etc.) based DW
applications media content,
4. IT services including video etc.)
 System Integration
Structured Data administration tools
 Consulting
data
ETL & Data Workflow/
 Project System
(stored in integration scheduler
management and tools
MPP, RDBMS products products
customization
and DW*)
*MPP – Massively parallel processing; RDBMS - Relational Data Base Management Systems; DW – Data warehouse
Source: CRISIL GR&A analysis
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12. Key takeaways
Slide 3
An Introduction to Big Data
Slide 5
Global Landscape and Trends
Slide 16
The Big Data Opportunity
Slide 23
 Big Data – Geographic Analysis
 Market Trends & Developments

13. North America & Europe Drives the Big Data
Opportunity with over 85% of the World’s Data
As North America and Europe account for the lion’s share of the world’s data the initial opportunity of both Big
Data implementations and analytics lies in the these geographies i.e. developed economies
 Key verticals: Healthcare,  Key verticals: Technology, Financial services,
Manufacturing, Retail, Digital Oil & Gas, Utilities, Manufacturing
Marketing  Demand trend: European MNC’s are still in
 Demand trend: High demand the early stages of the adoption cycle
North of Big Data analytics
America Europe
>2,000 Japan
>400
>3,500 China
Middle East
>250
>200 India
>50  Key verticals: Manufacturing,
Telecom, Health & Life Sciences
 Demand trend: Demand for BI
 Demand trend: Current demand to derive operational efficiency
appears to be limited, however,
South America lack of skills may drive  Key verticals: Telecom, Bioinformatics,
>40 outsourcing of Big Data analytics Retail
 Demand trend: Industry is in nascent stage
with demand catching up, particularly in retail
 Low awareness levels
 Key verticals: Telecom, Retail, Banking
 Demand trend: Still embryonic; most
organizations have wait and watch approach
Data generated: High to low
Amount of new Big Data stored (Petabytes), 2010
Source: McKinsey Global Institute; CRISIL GR&A analysis
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14. Emergence of Niche Startups and Large IT Players Enhancing
their Big Data Capabilities are key enablers for the Industry
Market Trends and Developments
1 Emergence of niche Big Data startups driving technological innovation
2 Large IT players leveraging M&As to add Big Data capabilities to their service portfolios
3 Financial Services, Retail and Telecom are likely to be the early adopters in the Big Data space
4 Talent shortage is one of the biggest challenges of the Big Data space
Source: CRISIL GR&A analysis
14

15. Emergence of niche Big Data start-ups to boost 1
technological innovation
A new class of companies, specializing in Big Data technologies have emerged, to capitalize on the
opportunities in the Big Data domain
Technology Area Players*
Big Data start-ups – Key characteristics
Specialized in niche Big Data technologies like Hadoop distributions
Hadoop, NoSQL systems, in-memory analytics,
1
multiple parallel processing, and analytical
platforms
Non Hadoop Big Data
Majority of start-ups generate revenue less than Platforms
2 USD 50 million and exhibit double digit revenue
growth annually
Most start-ups raising funding by private ventures Analytic Platforms
3 and Applications
or being acquired by large IT players
Cloud-based Big
Data Applications
*Indicative list of players
Source: Industry reporting; CRISIL GR&A analysis
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16. Large IT Players Leveraging M&As to add Big Data 2
Capabilities to their Service Portfolios
Target
Area Acquirer Date Deal value Rationale
Company
Data Oct. '11 USD 1.1 billion  Develop a comprehensive platform to analyze Big Data
Management
USD 263  Strengthen position in data warehousing market through
Mar. '11
million expertise in SQL and MapReduce-based analysis
 Extend Smarter Commerce suite with qualitative analytics
Jun. '12 N.A.
software
 Leverage data navigation technologies for Big Data by
May. '12 N.A. automating discovery of through innovative index and search
capabilities
May. '12 N.A.  Addition of sales performance analytics
Advanced
analytics
May. '12 N.A.  Enhance Big Data marketing analytics
Apr. '12 N.A.  Acquisition of spend and procurement analytics
Mar. '12 N.A.  Accelerate development of Big Data analytic applications
Mar. '11 N.A.  Enhance real time business analytics for Big Data
Key highlights
 M&As in the Big Data space had tripled in  M&As with bigger deal value are happening in data
the first half of 2012 management
 Acquisition targets are mainly innovative Big Data
start-ups
N.A. is not available. Source: Industry reporting; CRISIL GR&A analysis
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17. 1. Retail: Sears is leveraging Big Data analytics internally and 3
is also keen on offering analytics services externally
Sears Holding is a leading integrated retailer with ~4,000 full-line and specialty retail stores in the US
and Canada. It operates through its subsidiaries including Sears, Roebuck and Co. and Kmart Corp.
Challenge/Business
Solution Benefits
Need
IT need • Leverages its global In-house center in Across IT environment
• Manage Increasing volumes of data Pune, India for Big Data Analytics
• Utilization of 100% of collected data
like customer personal information, • Implemented a Big Data architecture against 10% utilization earlier
PoS data, online purchases, etc., using Hadoop
• Ability to run price elasticity
posing a challenge • Used MapReduce algorithms to analyze algorithms in one week, as opposed
• Capacity run-out on its mainframe, and data and feed results back into the to eight weeks previously
adding more capacity proving to be mainframe, on individual customer
expensive activity, across all 4,000 locations • Cost-savings of USD 600,000 per year
Business need Across business
• The need to set prices quickly and in • More relevant and personalized
real time customer communications and offers to
• The need to drive customer loyalty an active customer base (~80 million)
• Increased shopping and higher spend
per transaction by active members
Looking at the current and potential benefits of Big Data analytics, Sears aims to expand into newer areas and sell its data
management and analytics services technology to other companies, through its subsidiary MetaScale
*Massively Parallel Processing
Source: Industry reporting; CRISIL GR&A analysis

18. 2. Financial Services: Witnessing increased adoption of Big Data 3
analytics, to reduce risk and uncover new market opportunities
• The need to meet growing regulatory compliances, detect fraud and create new market opportunities is driving the growth for Big Data
analytics in the financial services sector
• Customer & transaction data from multiple channels like branch, kiosks, mobile and web; social media; emails; credit cards data;
insurance claims data; stock market data; statistical data, PDF & excel files, videos, government filings, etc. are key Big Data sources
Big Data application across Financial Services sub-sector
Capital
Banking Markets/ Insurance
Trading
Predict client longevity,
Credit line optimization Trading surveillance along with analyzing
perspective clients
Credit reward program medical status
Intraday analysis
analysis Using weather and
calamity information for
Trading pattern analysis managing exposures
and losses
Pre-trade decision support analytics
Risk management/assessment
Fraud detection
Portfolio analytics
Compliance & regulatory reporting
CRM,, Entering new markets
Source: Industry reporting; CRISIL GR&A analysis

19. Potential Shortfall of 1.5 million Data-Savvy Managers and 4
~150,000 Data Scientists in the US in 2018
Demand-supply gap for data scientists* Requisite educational
Role in Ecosystem Other expertise
in US, 2018 qualifications
440K-490K
 Big Data analytics  Advanced degree like  Expertise in data
Data  Business intelligence
M.S. or Ph.D., in analytics skills to extract
mathematics, statistics, data, use of modeling &
300K Scientists
 Visualization economics, computer simulations
science or any decision
140K – 190K  Multi-disciplinary
sciences
knowledge of business to
50%-60% find insights
gap relative
to supply  Knowledge of statistics
 Project management  Advanced business
and/or machine learning
Data-savvy across the Big Data degree such as MBA,
M.S. or managerial to frame key questions
Managers ecosystem and analyze answers
diplomas
– Consulting
2018E Supply 2018E Demand services  Conceptual knowledge of
– Implementation business to interpret and
– Infrastructure challenge the insights
Demand-supply gap for data-savvy
management
managers* in US, 2018 – Analytics  Ability to make decisions
using Big Data insights
4.0 million
 Technical support in  Having a degree in  Possessing data
Technical hardware & software computer management knowledge
2.5 million across the Big Data science, information
Engineers
ecosystem for: technology, systems  IT skills to
1.5 million – Data architecture engineering. or related develop, implement, and
60% gap disciplines maintain hardware and
– Data
relative to administration software
supply – Developer
environment
– Applications
2018E Supply 2018E Demand
*Analysts with deep analytical training; **Managers to analyze Big Data and make decisions based on their findings; Source: McKinsey Global Institute; CRISIL GR&A analysis
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20. Key Takeaways
Slide 3
An Introduction to Big Data
Slide 5
Global Landscape and Trends
Slide 16
The Big Data Opportunity
Slide 23
 Forecasted market size
 Future outlook

21. Global Big Data market to reach ~USD 25 billion by
2015,with a 45% share of IT & IT-enabled services
 The global Big Data market is expected to grow by about a CAGR of 46% over 2012-2015
 IT & ITES, including analytics, is expected to grow the fastest, at a rate of more than 60%
– Its share in the total Big Data market is expected to increase to ~45% in 2015 from ~31% in 2011
 The USD 25 billion opportunity represents the initial wave of the opportunity. This opportunity is set to expand
even more rapidly after 2015 given the pace at which data is being generated.
Global Big Data Market Size, 2011 – 2015E Global Big Data Market Size, 2015F
US$ billion
~US$25 billion
25.0-26.0 Opportunity for India
lies in capturing the
Big Data analytics & slice of IT services that
US$ 10-11
IT & IT-enabled includes Big Data
billion
services analytics and IT & IT-
enabled services
US$ 7-7.5 Lion’s share of the Big
8.0-8.5 Software billion Data hardware and
software market is
5.3-5.6 expected to be
occupied by IT giants
US$ 6-6.5 like
Hardware billion IBM, HP, Microsoft, SA
P, SAS, Oracle, etc.
2011E 2012E 2015F
2015
Source: Industry reporting; CRISIL GR&A analysis
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22. Conclusion
 Big Data market opportunity is expected to witness strong growth in the next 5 years
– Expected to touch US$25 billion globally; the ‘BIG’ opportunity for India lies in the IT & IT-enabled
Services space, which is likely to be ~US$ 10-11 billion market globally in 2015
– Data-related regulations like Dodd-Frank and Basel III to impact Big Data implementations
 New database architectures and innovative analytics tools & techniques to facilitate
Big Data implementations
 Key verticals driving demand for Big Data analytics: Financial services, Retail,
Telecom, Healthcare and Manufacturing
 Key risk – potential shortfall of 1.5 million Data-Savvy Managers and 140,000-190,000
Data Scientists in the US by 2018
Source: CRISIL GR&A analysis
22

23. www.crisil.com/gra

24. Appendix

25. India’s ‘BIG’ opportunity is in IT and
IT-enabled services
India Big Data outsourcing opportunity, 2011 – 2015E India Big Data outsourcing opportunity, by
US$ billions category, 2015F, Percent
100%= ~US$1.1 billion
1.1-1.2
24%-27% Pure-play Analytics
firms
Integrated IT/ BPO
players
~0.2
~0.1
73%-76%
2011E 2012E 2015F
Source: CRISIL GR&A analysis Source: CRISIL GR&A analysis
 India’s Big Data market is expected to grow at a 83% CAGR over 2011-2015 to reach ~US$ 1.1-1.2 billion
 India’s share in the ~USD 10-11 billion global Big data IT and IT-enabled services market is expected to
be ~10% in 2015 , where:
– In 2015, integrated IT and BPO players will dominate the US$1.1 billion opportunity with close to 73-76%
Source: Industry reporting; CRISIL GR&A analysis
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26. Key Players Across the Traditional and Big Data
Technology Stack
Key players in BI/Traditional Analytics vs. Big Data Analytics technology stack
Big Data Analytics
BI/Traditional Analytics
Big Data Use
E-commerce, Search , Social gaming
Traditional BI suites and OLAP
End-user
applications
IT Services – Data Management
Big Data Analytics Basic visualization apps. Advanced visualization apps. MapReduce Programs
Visualization
tools
Traditional Analytics Advanced Analytics
Analytical tools
Parallel Relational Hadoop
Big Data RDBMS NoSQL Databases Database
SAP HANA
Data
management
systems
Conventional
file systems HDFS
Infrastructure &
storage systems Monolithic Hardware Distributed Hardware
Note: This is a representative list of players
Source: Industry reporting; CRISIL GR&A analysis
26

27. Financial Services and Telecom to be the early
adopters of the Big Data
 Indian service providers like Infosys, Fractal are enabling Big Data analytics in the area of fraud detection, CRM
Financial and customer loyalty program, trading pattern analysis, risk calculation on large portfolio of loans
Services  Key Adopters: JPMorgan Chase, Merrill Lynch, HSBC, American Express, Goldman
Sachs, Barclays, Bank of America, Citigroup, and Wells Fargo
 Telecom players are increasingly focusing on Big Data to limit churn rates, build loyalty and provide multi-
Telecom channel and multi-service applications by proactively analyzing the subscriber and network data
 Key Adopters: Airtel , Vodafone
 Both brick and mortar as well as online retailers are increasing their adoption of Big Data analytics for real time
Retail analysis of purchase behavior and buying patterns, enhanced customer segmentation and customer loyalty
 Key Adopters: Walmart & Sears
 Indian service providers are enabling manufacturing companies through Big Data analytics in the areas of
Manufacturing accurate demand forecasting, optimization of operations, inventory management, open innovation and better
analysis of post sales feedback in real time
 Key benefits of big data in public sector include: Intelligence to counter national threats, Forecast economic
Public Sector events, Traffic management, Environment monitoring, energy/ waste management, etc.
 Healthcare players use Big Data Next-generation sequencing and mapping for genomics, analysis of correlation
Healthcare between treatments & outcomes and real time data from medical devices for better patient care
Source: Industry reporting; CRISIL GR&A analysis
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