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Is Big Data Driving Product/ Technology

Innovation?

Sanjeev Sinha

President, CRISIL Global Research & Analytics

Oct 23, 2013

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

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Source: CRISIL GR&A analysis

Big Data is Defined by Volume, Variety and Velocity

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Size of Data

Sp

eed

, A

ccu

racy a

nd

Co

mp

lexit

y o

f In

tellig

en

ce

Big Data

analytics

Big Data

Traditional

analytics

Advanced

analytics

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

What is Big Data ?

Volume

Variety

Velocity

Large quantity of data

which may be enterprise-

specific or general and

public or private

1

Diverse set of data

being created, such

as social networking

feeds, video and

audio files, email,

sensor data and

other raw data

2

Speed of data inflow as

well as rate at which this

fast-moving data needs to

be stored

3

Gigabytes Terabytes Petabytes Zetabytes

Small Data Sets

Small Data Sets

Traditional

analytics

Big Data

Source: CRISIL GR&A analysis

3Vs

Source: CRISIL GR&A analysis

Descriptive

analytics

Big Data Analytics is Application of Advanced Techniques on Big

Datasets; Answers Questions Previously Considered Beyond Reach

4

Evolution of analytics

Leve

l o

f C

om

ple

xit

y

In-database analytics Analytics as a separate value chain function

Time

Standard

reports

Adhoc

reports

Alerts

Statistical

analysis

Forecast

- ing

Predictive

modeling

Optimization

Stochastic

optimization

Natural Language Processing

Big Data analytics

Complex

event

processing

Predictive

analytics

Prescriptive

analytics

Basic analytics What happened?

When did it happen?

What was the its impact ?

Advanced

analytics

Why did it

happen?

When will it

happen

again?

What

caused it to

happen?

What can be

done to

avoid it? Multivariate statistical analysis

Time series analysis

Behavioral analytics

Data mining

Constraint

based BI

Social network analytics

Semantic analytics

Online analytical processing (OLAP)

Extreme SQL Visualization

Analytic

database

functions

Big Data analytics is

where advanced

analytic techniques

are applied on Big

Data sets

The term came into

play late 2011 – early

2012

Late 1990s 2000 onwards

Source: CRISIL GR&A analysis

Query

drill

down

Global Big Data market to reach ~USD 25 billion by

2015,with a 45% share of IT & IT-enabled services

5

2011E 2012E 2015F

Global Big Data Market Size, 2011 – 2015E

US$ billion

5.3-5.6

8.0-8.5

25.0-26.0

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.

Source: Industry reporting; CRISIL GR&A analysis

2015

US$ 6-6.5

billion

US$ 7-7.5

billion

US$ 10-11

billion

Global Big Data Market Size, 2015F

~US$25 billion

Big Data analytics &

IT & IT-enabled

services

Software

Hardware

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.

Opportunity for India

lies in capturing the

slice of IT services that

includes Big Data

analytics and IT & IT-

enabled services

India’s ‘BIG’ opportunity is in IT and

IT-enabled services

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~0.1

~0.2

1.1-1.2

2011E 2012E 2015F

India Big Data outsourcing opportunity, 2011 – 2015E

US$ billions

India Big Data outsourcing opportunity, by

category, 2015F, Percent

24%-27%

73%-76%

Pure-play Analyticsfirms

Integrated IT/ BPOplayers

Source: CRISIL GR&A analysis Source: CRISIL GR&A analysis

100%= ~US$1.1 billion

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

Driving Product Innovation through Big Data

Analytics is Amongst the Top 10 Business Priorities

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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

Companies are turning to big data

platforms like Hadoop to help

provide faster insights

Research required to adapt products,

improve sales, and drive value is

costly and time consuming

WHY BIG DATA ANALYTICS IN PRODUCT INNOVATION?

Source: Industry reporting; CRISIL GR&A analysis

Need for real time analysis of data

Explosion of unstructured and semi-structured data

Demand for intelligence on product defects,

improvements and usage

Proactive assessment of customer behaviour

Organisational and Cultural issues

Paucity of budgetary allowances

Shortage of data scientists and

analytics professionals

Inadequacy of in-house technology infrastructure

Drivers of Big Data Analytics in

Product Innovation Barries in Adoption of Big Data Analytics in

Product Innovation

Leveraging Big Data Analytics to Measure, Manage and

Increase the value of Product Innovation

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Source: Industry reporting; CRISIL GR&A analysis

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

1

2

3

Accelerate Innovation

New Product Development

Go-to-Market

Usage of Big Data Analytics to Embed Customer Sentiment in Product Innovation

R&D Analytics

Innovation Analytics

Product Analytics

Predictive Analytics

Extreme Event Modeling

Product Life Cycle Analytics

Product Analytics

Predictive Analytics

Assortment Planning

Regulatory Analytics

Portfolio Analytics

Product Analytics

Product Launch Analytics

Customer Segmentation

Sales/Demand Forecasting

Price/ Promotion

Optimization

Marketing Mix Modeling

Competitor Analysis

Acquisition Modeling

Unstuctured Data

Stuctured Data

Customer Sentiment

Use of Big Data

Analytics

Behavioral

Analytics

Sentiment

Analytics

Predictive Analytics

Customer

Lifetime Value

Customer

Analytics

CRM

Analytics

Strategic Portfolio Planning

Innovation Data Management

Integrated approach to Big Data analytics is driving next-

generation innovations in technology

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Source: Industry reporting; CRISIL GR&A analysis

MARKET TRENDS AND DEVELOPMENTS

Driver Inhibitor Neutral

Converging technology trends in data storage, processing,

and analytics are driving adoption

Increasing convergence between cloud and big data are

becoming huge springboards to innovations in technology

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

Large IT players leveraging M&A’s to add Big Data

capabilities to their service portfolios

Emergence of niche Big Data start ups driving technological

innovation

Enterprise Integration

Unstructured

Exploratory

Iterative

Structured

Repeatable

Linear

Data

Warehouse Traditional

Sources

Transaction Data

ERP Data Internal App Data

Mainframe Data OLTP System Data

Web Logs

Text Data: Emails Social Data

Sensor Data

RFID

Hadoop

Streams

New

Sources

INTEGRATED APPROACH TO ADVANCED BIG DATA

ANALYTICS PLATFORM

New Approach

Creative, holistic thought, intuition, sense and respond

Traditional Approach

Structured, analytical, logical, and historical

Source: Industry reporting; CRISIL GR&A analysis

New database architectures and innovative analytics

tools & techniques to facilitate Big Data implementations

• Energy management

• SEO optimization

• Real-time traffic

congestion detection

using GPS data

Data storage and

management

(Architectures)

Store large

quantities of

unstructured

data

• Website click streams

• Tweets and Facebook

likes

• Sensor Data

• Emails

• Real-time embedded

systems

• Algorithmic trading

• E-commerce

• Social networking

Faster data

access,

storage and

analysis

Real time

analysis of

high volumes

of data

• Risk management

• Customer intelligence

• Revenue optimization

• Assortment

• Merchandise planning

Gain

actionable

insights from

analytics and

respond to

issues

instantly

*Are indicative examples

Data storage and analytics

Advanced analytics and

data processing

Advanced Visualization

Need Area of advancement Application areas* Examples

Database architectures:

• Hadoop (MapReduce & HDFS)

• NoSQL databases

• MPP architecture like EMC’s Greenplum

In-memory databases:

• SAP HANA

• Terracota BigMemory

In-memory analytics platforms like:

• Kognitio analytics platform

• SAP HANA analytics appliance

• Tag clouds

• Real time dashboards

• Heat maps

• Spatial information

flow

Source: Industry reporting; CRISIL GR&A analysis

Technology Area Players*

Hadoop distributions

Non Hadoop Big Data

Platforms

Analytic Platforms

and Applications

Cloud-based Big

Data Applications

Emergence of niche Big Data start-ups to boost

technological innovation

*Indicative list of players

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,

NoSQL systems, in-memory analytics, multiple parallel

processing, and analytical platforms

Majority of start-ups generate revenue less than USD

50 million and exhibit double digit revenue growth

annually

Have created demand for data scientists, data savvy

managers and large number of technical engineers

Focus on two segments in big data—building pure

technology infrastructure for managing the information,

and analytical software that help enterprises in specific

industries

Most start-ups raising funding by private ventures or

being acquired by large IT players

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