Big Data market opporunity is expected to witness strong growth in the next 5 years touching $25bn globally. The big opporunity lies in Indian IT/ITES space which is likely to be $10-11billion market globally in 2015. Key risks include shortfall of data-savvy managers and data scientists in the US.

1. Sanjeev Sinha
President, CRISIL Global Research & Analytics
© 2013 CRISIL Ltd. All rights reserved..
Is Big Data Driving Product/ Technology
Innovation?
Oct 23, 2013
1

2. 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

Driving product innovation through Big Data analytics is amongst the Top 10
business priorities

Organisations are leveraging Big Data analytics to embed customer sentiment in
product innovation

Integrated approach to Big Data analytics is driving next-generation innovations in
technology

New database architectures and innovative analytics tools & techniques to facilitate
Big Data implementations

Emergence of niche Big Data start-ups to boost technological innovation

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
2

3. 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
Speed, Accuracy and Complexity of Intelligence
its ability to help in “Evidence-Based” Decision-making, having a high impact on business operations
3Vs
Small Data Sets
Big Data
Advanced
analytics
1
Big Data
analytics
Volume
2
Variety
Small Data Sets
Big Data
Traditional
analytics
Traditional
analytics
Large quantity of data
which may be enterprisespecific or general and
public or private
Diverse set of data
being created, such
as social networking
feeds, video and
audio files, email,
sensor data and
other raw data
Velocity
3
Gigabytes
Terabytes
Petabytes
Size of Data
Zetabytes
Speed of data inflow as
well as rate at which this
fast-moving data needs to
be stored
Source: CRISIL GR&A analysis
Source: CRISIL GR&A analysis
3

4. 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
Level of Complexity
Prescriptive
analytics
 Big Data analytics is
where advanced
analytic techniques
are applied on Big
Data sets
Extreme SQL
Visualization
Predictive
modeling
Predictive
analytics
Forecast
- ing
Statistical
analysis
Adhoc
reports
Standard
reports
Late 1990s
Social network analytics
Semantic analytics
Time series analysis
Natural Language Processing
Multivariate statistical analysis
Online analytical processing (OLAP)
Alerts
Query
drill
down
Analytic
database
functions
Constraint
based BI
Optimization
 The term came into
play late 2011 – early
2012
Descriptive
analytics
Stochastic
optimization
Complex
event
processing
Advanced
analytics
 Why did it
happen?
 When will it
happen
again?
 What
caused it to
happen?
 What can be
done to
avoid it?
Data mining
Basic analytics
 What happened?
 When did it happen?
 What was the its impact ?
2000 onwards
Time
Analytics as a separate value chain function
In-database analytics
Source: CRISIL GR&A analysis
4

5. 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
US$ billion
Global Big Data Market Size, 2015F
~US$25 billion
25.0-26.0
Big Data analytics &
US$ 10-11
IT & IT-enabled
billion
services
Software
Hardware
8.0-8.5
US$ 7-7.5
billion
US$ 6-6.5
billion
5.3-5.6
2011E
2012E
Opportunity for India
lies in capturing the
slice of IT services that
includes Big Data
analytics and IT & ITenabled services
Lion’s share of the Big
Data hardware and
software market is
expected to be
occupied by IT giants
like IBM, HP, Microsoft,
SAP, SAS, Oracle, etc.
2015F
2015
Source: Industry reporting; CRISIL GR&A analysis
5

6. India’s ‘BIG’ opportunity is in IT and
IT-enabled services
India Big Data outsourcing opportunity, 2011 – 2015E
US$ billions
India Big Data outsourcing opportunity, by
category, 2015F, Percent
100%= ~US$1.1 billion
1.1-1.2
24%-27%
Pure-play Analytics
firms
Integrated IT/ BPO
players
~0.2
~0.1
2011E
2012E
Source: CRISIL GR&A analysis
2015F
73%-76%
Source: CRISIL GR&A analysis
 India’s Big Data market is expected to grow at a 83% CAGR over 2011-2015 to reach ~US$ 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
6

7. Driving Product Innovation through Big Data
Analytics is Amongst the Top 10 Business Priorities
Why Big Data Analytics in Product Innovation?
WHY BIG DATA ANALYTICS IN PRODUCT INNOVATION?
Product innovation is a risky
business: Majority of new
products that enter the market fail
Big Data Analytics shortens time to
market, improves product adoption,
and reduces costs
Drivers of Big Data Analytics in
Product Innovation
Need for real time analysis of data
Explosion of unstructured and semi-structured data
Research required to adapt products,
improve sales, and drive value is
costly and time consuming
Companies are turning to big data
platforms like Hadoop to help
provide faster insights
Barries in Adoption of Big Data Analytics in
Product Innovation
Organisational and Cultural issues
Paucity of budgetary allowances
Demand for intelligence on product defects,
improvements and usage
Shortage of data scientists and
analytics professionals
Proactive assessment of customer behaviour
Inadequacy of in-house technology infrastructure
Source: Industry reporting; CRISIL GR&A analysis
7

8. Leveraging Big Data Analytics to Measure, Manage and
Increase the value of Product Innovation
Organisations are recognizing the value of ‘Big Data Analytics’ in mining customer needs and desires as well in devising
a data management strategy that integrates big data into the front end of the innovation pipeline
Strategic Portfolio Planning
Unstuctured Data
3
Stuctured Data
2
Go-to-Market
1
 Product Analytics
New Product Development
Predictive Analytics
Behavioral
Analytics
Sentiment
Analytics
Use of Big Data
Analytics
Customer
Analytics
CRM
Analytics
Customer
Lifetime Value
Customer Sentiment
Accelerate Innovation
 R&D Analytics
 Product Launch Analytics
 Product Life Cycle Analytics  Customer Segmentation
 Product Analytics
 Sales/Demand Forecasting
 Predictive Analytics
 Price/ Promotion
 Innovation Analytics
 Assortment Planning
 Product Analytics
 Regulatory Analytics
 Marketing Mix Modeling
 Predictive Analytics
 Portfolio Analytics
 Competitor Analysis
 Extreme Event Modeling
Optimization
 Acquisition Modeling
Usage of Big Data Analytics to Embed Customer Sentiment in Product Innovation
Innovation Data Management
Source: Industry reporting; CRISIL GR&A analysis
8

9. Integrated approach to Big Data analytics is driving nextgeneration innovations in technology
MARKET TRENDS AND DEVELOPMENTS
Converging technology trends in data storage, processing,
and analytics are driving adoption
INTEGRATED APPROACH TO ADVANCED BIG DATA
ANALYTICS PLATFORM
Traditional Approach
Structured, analytical, logical, and historical
Transaction Data
Increasing convergence between cloud and big data are
becoming huge springboards to innovations in technology
Emergence of niche Big Data start ups driving technological
innovation
Internal App Data
ERP Data
Mainframe Data OLTP System Data
Data
Warehouse
Structured
Repeatable
Linear
Traditional
Sources
Enterprise Integration
Large IT players leveraging M&A’s to add Big Data
capabilities to their service portfolios
Unstructured
New
Exploratory
Sources
Iterative
Text Data: Emails
Social Data
RFID
Web Logs
Hadoop
Streams
Open Source Big Data tools (Talend, Pentaho, etc.) and models
are driving next-generation innovations in technology
Talent shortage is one of the biggest challenges of the Big
Data space
Driver
Inhibitor
Neutral
Sensor Data
New Approach
Creative, holistic thought, intuition, sense and respond
Source: Industry reporting; CRISIL GR&A analysis
9

10. New database architectures and innovative analytics
tools & techniques to facilitate Big Data implementations
Store large
quantities of
unstructured
data
Area of advancement
Data storage
and
management
(Architectures)
Examples
Application areas*
Database architectures:
Need
• Website click streams
• Hadoop (MapReduce
& HDFS)
• Tweets and Facebook
likes
• NoSQL databases
• MPP architecture like
EMC’s Greenplum
Faster data
access,
storage and
analysis
In-memory databases:
Data storage
and analytics
• SAP HANA
• Terracota BigMemory
• Sensor Data
• Emails
• Real-time embedded
systems
• Algorithmic trading
• E-commerce
• Social networking
Real time
analysis of
high volumes
of data
Advanced
analytics and
data
processing
In-memory analytics
platforms like:
• Kognitio analytics
platform
• SAP HANA analytics
appliance
• Risk management
• Customer intelligence
• Revenue optimization
• Assortment
• Merchandise planning
Gain
actionable
insights from
analytics and
respond to
issues
instantly
Source: Industry reporting; CRISIL GR&A analysis
*Are indicative examples
• Tag clouds
Advanced
Visualization
• Energy management
• Real time dashboards
• SEO optimization
• Heat maps
• Real-time traffic
congestion detection
using GPS data
• Spatial information
flow

11. Emergence of niche Big Data start-ups to boost
technological innovation
A new class of companies, specializing in Big Data technologies have emerged, to capitalize on the
opportunities in the Big Data domain
Big Data start-ups – Key characteristics
Specialized in niche Big Data technologies like Hadoop,
1 NoSQL systems, in-memory analytics, multiple parallel
processing, and analytical platforms
2
Focus on two segments in big data—building pure
technology infrastructure for managing the information,
and analytical software that help enterprises in specific
industries
Technology Area
Hadoop distributions
Non Hadoop Big Data
Platforms
3 Most start-ups raising funding by private ventures or
being acquired by large IT players
Majority of start-ups generate revenue less than USD
4 50 million and exhibit double digit revenue growth
annually
Analytic Platforms
and Applications
5 Have created demand for data scientists, data savvy
managers and large number of technical engineers
Cloud-based Big
Data Applications
Source: Industry reporting; CRISIL GR&A analysis
*Indicative list of players
Players*