big data’s big impact on businesses
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Big Data’s Big Impact on Businesses
1
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Big Data’s Big Impact
on Businesses
Webconference : Jan 29, 2013
Key Takeaways
Introduction to Big Data
Global Landscape and Trends
The Big Data Opportunity
Slide 3
Slide 5
Slide 12
Big Data’s Big
Impact on
Businesses
Slide 20
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
4
Source: CRISIL GR&A analysis
Key Takeaways
An Introduction to Big Data
Slide 3
Slide 5
Definition of Big Data
Big Data ecosystem
Benefits of Big Data to enterprises
Key applications for end
consumers
Global Landscape and Trends
The Big Data Opportunity
Slide 16
Slide 23
Big Data is Defined by Volume, Variety and Velocity
6
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
The Global Data likely to Grow at a CAGR of 41%
7
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
Implication for an organization
2009 2011 2015 2020
0.8
1.9
7.9
35.0CAGR
(2009-2020)
41.0%
Zetabytes
Growth of global data, 2009-2020
Note: ZB stands for Zetabytes;
Source: IDC; CRISIL GR&A analysis
Today 80% of Data Existing in any
Enterprise is Unstructured Data
8
Variety of sources from where data is being
generated has also undergone a shift
The types of data being created has changed from
structured to semi-structured to unstructured data
Structured Data
Resides in formal data stores – RDBMS and Data
Warehouse; grouped in the form of rows or columns
Accounts for ~10% of the total data existing currently
AudioVideoWeather
patternsBlogs
Location
co-ordinatesText message
Web logs &
clickstreams
RDBMS (e.g.,
ERP and CRM
Data
Warehousing
Microsoft Project
Plan File
Semi-
Structured Data
A form of structured data that does not conform with the
formal structure of data models
Accounts for ~10% of the total data existing currently
Unstructured
Data
Comprises data formats which cannot be stored in row/
column format like audio files, video, clickstream data,
Accounts for ~80% of the total data existing currently
Sensor data/
M2M Email Social mediaGeospatial
data
Introduction
Need to manage broad range of data types
Process analytic queries across numerous data
types
Need to extract meaningful analysis from this data
has led to several technologies to gain traction
Examples include NoSQL databases to store
unstructured data as well as innovative processing
methods like Hadoop and massive parallel
processing (MPP)
Implication for organization
Solutions required
Source: Industry reporting; CRISIL GR&A analysis
Volume
Variety
Velocity
Big Data will Enable Real Time Analytics
9
Big Data is also characterized by
velocity or speed i.e. frequency of
data generation or the frequency of
data delivery
New age communication channels
such as mobile phones, emails, social
networking has increased the rate of
information flows
Examples:
Telcos adopting location based
marketing based on user location
sensed by mobile towers
Satellite images can help monitor and
analyze troop movements, a flood
plane, cloud patterns, or forest fires
Video analysis systems could monitor
a sensitive or valuable facility,
watching for possible intruders and
alert authorities in real time
Big Data velocity enabling real
time use of data
Data velocity
per minute
600+videos on YouTube 200
million+ emails sent
2 million+
Google search queries
400,000+minutes of
Skype calling
400,000+tweets on
US$ 300,000+ are spent on online shopping
700,000+ Facebook updates
7,000+ photos on
flickr
1,500+blog posts
3500+ticks per minute in securities
trading
Source: Industry reporting; CRISIL GR&A analysis
Volume
Variety
Velocity
Descriptive
analytics
Big Data Analytics is Application of Advanced Techniques on Big
Datasets; Answers Questions Previously Considered Beyond Reach
10
Evolution of analytics
Leve
l o
f C
om
ple
xit
y
In-database analyticsAnalytics 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 SQLVisualization
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
Big Data Management, Analytics, IT Services & Applications
are the Key Constituents of Big Data Ecosystem
11
Data
Sources
Big Data Analytics
Components of Big Data Ecosystem
Developer Environments(Languages (Java),
Environments (Eclipse &
NetBeans), programming
interfaces (MapReduce))
Analytics
products
(Avro, Apache
Thrift)BI
&visualization
tools
Applications(mobile, search, web)
End users
Business analysts
Big Data
Data ArchitectureHadoop/ Big Data
tech’y framework
(MapReduce etc.)
Unstructured
data
(Text, web
pages, social
media content,
video etc.)
Structured
data
(stored in
MPP, RDBMS
and DW*)
Data administration tools
NoSQL
MPP
RDBMS
DW
NoSQL
Hadoop
based
Operational Data
Da
ta m
an
ag
em
en
t &
sto
rag
e
Da
ta a
na
lyti
cs &
its
ap
pli
cati
on
an
d u
se
IT s
erv
ices
(SI,
cu
sto
miz
atio
n, co
nsu
ltin
g, syste
m d
esig
n)
ETL & Data
integration
products
System
tools
Workflow/
scheduler
products
Input data
Four key elements:
1. Big Data
Management &
storage:
Data storage
infrastructure
and technologies
2. Big Data Analytics
Includes the
technologies and
tools to analyze the
data and generate
insight from it
3. Big Data’s
Application & Use
Involves enabling
the Big Data
insights to work in
BI and end-user
applications
4. IT services including
System Integration
Consulting
Project
management and
customization
What does the Big Data Ecosystem Constitute ?
*MPP – Massively parallel processing; RDBMS - Relational Data Base Management Systems; DW – Data warehouse
Source: CRISIL GR&A analysis
Key takeaways
An Introduction to Big Data
Global Landscape and Trends
The Big Data Opportunity
Slide 3
Slide 5
Slide 16
Slide 23
Big Data – Geographic Analysis
Market Trends & Developments
North America & Europe Drives the Big Data
Opportunity with over 85% of the World’s Data
13
>3,500
>40
>2,000
>200
>400
Data generated: High to low
Amount of new Big Data stored (Petabytes), 2010
Key verticals: Healthcare,
Manufacturing, Retail, Digital
Marketing
Demand trend: High demand
of Big Data analytics
>250
Key verticals: Telecom, Retail, Banking
Demand trend: Still embryonic; most
organizations have wait and watch approach
Demand trend: Current demand
appears to be limited, however,
lack of skills may drive
outsourcing of Big Data analytics
Low awareness levels
Key verticals: Technology, Financial services,
Oil & Gas, Utilities, Manufacturing
Demand trend: European MNC’s are still in
the early stages of the adoption cycle
North
America
South America
Europe
Middle East
India
China
Japan
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
Source: McKinsey Global Institute; CRISIL GR&A analysis
Key verticals: Manufacturing,
Telecom, Health & Life Sciences
Demand trend: Demand for BI
to derive operational efficiency
Key verticals: Telecom, Bioinformatics,
Retail
Demand trend: Industry is in nascent stage
with demand catching up, particularly in retail
>50
Emergence of Niche Startups and Large IT Players Enhancing
their Big Data Capabilities are key enablers for the Industry
14
Market Trends and Developments
Emergence of niche Big Data startups driving technological innovation
Large IT players leveraging M&As to add Big Data capabilities to their service portfolios
Financial Services, Retail and Telecom are likely to be the early adopters in the Big Data space
Talent shortage is one of the biggest challenges of the Big Data space
1
2
3
Source: CRISIL GR&A analysis
4
Emergence of niche Big Data start-ups to boost
technological innovation
15
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
1
2
3
Majority of start-ups generate revenue less than
USD 50 million and exhibit double digit revenue
growth annually
Most start-ups raising funding by private ventures
or being acquired by large IT players
Technology Area Players*
Hadoop distributions
Non Hadoop Big Data
Platforms
Analytic Platforms
and Applications
Cloud-based Big
Data Applications
*Indicative list of players
Source: Industry reporting; CRISIL GR&A analysis
1
Large IT Players Leveraging M&As to add Big Data
Capabilities to their Service Portfolios
16
Key highlights
Acquisition targets are mainly innovative Big Data
start-ups
M&As with bigger deal value are happening in data
management
M&As in the Big Data space had tripled in
the first half of 2012
Area AcquirerTarget
CompanyDate Deal value Rationale
Data
Management
Oct. '11 USD 1.1 billion Develop a comprehensive platform to analyze Big Data
Mar. '11USD 263
million
Strengthen position in data warehousing market through
expertise in SQL and MapReduce-based analysis
Advanced
analytics
Jun. '12 N.A. Extend Smarter Commerce suite with qualitative analytics
software
May. '12 N.A.
Leverage data navigation technologies for Big Data by
automating discovery of through innovative index and search
capabilities
May. '12 N.A. Addition of sales performance 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
N.A. is not available. Source: Industry reporting; CRISIL GR&A analysis
2
1. Retail: Sears is leveraging Big Data analytics internally and
is also keen on offering analytics services externally
Benefits
IT need
• Manage Increasing volumes of data
like customer personal information,
PoS data, online purchases, etc.,
posing a challenge
• Capacity run-out on its mainframe, and
adding more capacity proving to be
expensive
Business need
• The need to set prices quickly and in
real time
• The need to drive customer loyalty
• Leverages its global In-house center in
Pune, India for Big Data Analytics
• Implemented a Big Data architecture
using Hadoop
• Used MapReduce algorithms to analyze
data and feed results back into the
mainframe, on individual customer
activity, across all 4,000 locations
Across IT environment
• Utilization of 100% of collected data
against 10% utilization earlier
• Ability to run price elasticity
algorithms in one week, as opposed
to eight weeks previously
• Cost-savings of USD 600,000 per year
Across business
• More relevant and personalized
customer communications and offers to
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
Challenge/Business
NeedSolution
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.
3
Source: Industry reporting; CRISIL GR&A analysis
• 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
2. Financial Services: Witnessing increased adoption of Big Data
analytics, to reduce risk and uncover new market opportunities
Big Data application across Financial Services sub-sector
Banking
Capital
Markets/
TradingInsurance
Trading surveillance
Intraday analysis
Trading pattern analysis
Credit line optimization
Credit reward program analysis
Pre-trade decision support analytics
Fraud detection
Portfolio analytics
Compliance & regulatory reporting
CRM,, Entering new markets
Predict client longevity, along with analyzing perspective clients
medical status
Using weather and calamity information for managing exposures
and losses
Risk management/assessment
3
Potential Shortfall of 1.5 million Data-Savvy Managers and
~150,000 Data Scientists in the US in 2018
19
2018E Supply 2018E Demand
Demand-supply gap for data scientists*
in US, 2018
Data
Scientists
Data-savvy
Managers
Technical
Engineers
Expertise in data
analytics skills to extract
data, use of modeling &
simulations
Multi-disciplinary
knowledge of business to
find insights
Advanced business degree such as MBA, M.S. or managerial diplomas
Advanced degree like
M.S. or Ph.D., in
mathematics, statistics,
economics, computer
science or any decision
sciences
Knowledge of statistics
and/or machine learning
to frame key questions
and analyze answers
Conceptual knowledge of
business to interpret and
challenge the insights
Ability to make decisions
using Big Data insights
Having a degree in
computer
science, information
technology, systems
engineering. or related
disciplines
Possessing data
management knowledge
IT skills to
develop, implement, and
maintain hardware and
software
Project management
across the Big Data
ecosystem
– Consulting
services
– Implementation
– Infrastructure
management
– Analytics
Big Data analytics
Business intelligence
Visualization
Technical support in
hardware & software
across the Big Data
ecosystem for:
– Data architecture
– Data
administration
– Developer
environment
– Applications
50%-60%
gap relative
to supply
300K
Role in Ecosystem Requisite educational
qualificationsOther expertise
140K – 190K
440K-490K
Demand-supply gap for data-savvy
managers* in US, 2018
2018E Supply 2018E Demand
60% gap
relative to
supply
2.5 million
1.5 million
4.0 million
*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
4
Forecasted market size
Future outlook
Key Takeaways
An Introduction to Big Data
Global Landscape and Trends
The Big Data Opportunity
Slide 3
Slide 5
Slide 16
Slide 23
Global Big Data market to reach ~USD 25 billion by
2015,with a 45% share of IT & IT-enabled services
21
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, SA
P, SAS, Oracle, etc.
Opportunity for India
lies in capturing the
slice of IT services that
includes Big Data
analytics and IT & IT-
enabled services
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
22
Source: CRISIL GR&A analysis
www.crisil.com/gra
Appendix
India’s ‘BIG’ opportunity is in IT and
IT-enabled services
25
~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 Analytics firms
Integrated IT/ BPO players
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
Key Players Across the Traditional and Big Data
Technology Stack
26
IT S
erv
ice
s –
Da
ta M
an
ag
em
en
t
Infrastructure &
storage systems
Data
management
systems
End-user
applications
Analytical tools
Visualization
tools
Parallel Relational
Database
Distributed HardwareMonolithic Hardware
RDBMS
Basic visualization apps. Advanced visualization apps.
HDFS Conventional
file systems
Traditional Analytics Advanced Analytics
Hadoop
Traditional BI suites and OLAPE-commerce, Search , Social gaming
Big Data
Big Data Analytics
Big Data Use
MapReduce Programs
Key players in BI/Traditional Analytics vs. Big Data Analytics technology stack
NoSQL Databases
SAP HANA
Big Data Analytics
BI/Traditional Analytics
Note: This is a representative list of players
Source: Industry reporting; CRISIL GR&A analysis
Financial Services and Telecom to be the early
adopters of the Big Data
27
Healthcare
Financial Services
Manufacturing
Indian service providers like Infosys, Fractal are enabling Big Data analytics in the area of fraud detection, CRM
and customer loyalty program, trading pattern analysis, risk calculation on large portfolio of loans
Key Adopters: JPMorgan Chase, Merrill Lynch, HSBC, American Express, Goldman
Sachs, Barclays, Bank of America, Citigroup, and Wells Fargo
Retail
Both brick and mortar as well as online retailers are increasing their adoption of Big Data analytics for real time
analysis of purchase behavior and buying patterns, enhanced customer segmentation and customer loyalty
Key Adopters: Walmart & Sears
Telecom
Telecom players are increasingly focusing on Big Data to limit churn rates, build loyalty and provide multi-
channel and multi-service applications by proactively analyzing the subscriber and network data
Key Adopters: Airtel , Vodafone
Key benefits of big data in public sector include: Intelligence to counter national threats, Forecast economic
events, Traffic management, Environment monitoring, energy/ waste management, etc.
Indian service providers are enabling manufacturing companies through Big Data analytics in the areas of
accurate demand forecasting, optimization of operations, inventory management, open innovation and better
analysis of post sales feedback in real time
Healthcare players use Big Data Next-generation sequencing and mapping for genomics, analysis of correlation
between treatments & outcomes and real time data from medical devices for better patient care
Public Sector
Source: Industry reporting; CRISIL GR&A analysis
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