With big data being a challenge for CIOs and CEOs in manufacturing, retail, healthcare, energy and finance industries, there is increasing demand for data-driven decision-making technologies that enable companies to deliver value to both their customers and for themselves. This demand creates both opportunities and challenges for big IT vendors such as IBM, Oracle, HP, EMC and Microsoft to create value to their customers and investors. The following presentation are the efforts to explain CIOs, CEOs of Big IT vendors and other strategic investors to leverage opportunity in big data market from an technology investment stand-point. This presentation should support big IT vendors not only to enable their customer transform from traditional business intelligence (BI) platforms to operational business intelligence (BI) platforms, but also help them retain existing market share (BI) and gain competitive advantage in the big data market through strategically investing in pure-play big data vendors with innovative solutions. Target Audience: CIOs and CEOs of Big IT Vendors like Oracle, IBM, HP, EMC etc. Additional audience include VC (Venture Capitalists) and other strategic investors in big data markets

1. Big Data: Strategic Investment Opportunities
for IT Heavyweights & Other Investors
Arjunvasan Ambigapathy
Case Topic Description:
With big data being a challenge for CIOs and CEOs in manufacturing, retail, healthcare, energy and finance industries, there is increasing demand for data-
driven decision-making technologies that enable companies to deliver value to both their customers and for themselves. This demand creates both
opportunities and challenges for big IT vendors such as IBM, Oracle, HP, EMC and Microsoft to create value to their customers and investors. The
following presentation are the efforts to explain CIOs, CEOs of Big IT vendors and other strategic investors to leverage opportunity in big data market from
an technology investment stand-point. This presentation should support big IT vendors not only to enable their customer transform from traditional business
intelligence (BI) platforms to operational business intelligence (BI) platforms, but also help them retain existing market share (BI) and gain competitive
advantage in the big data market through strategically investing in pure-play big data vendors with innovative solutions.
Target Audience: CIOs and CEOs of Big IT Vendors like Oracle, IBM, HP, EMC etc. Additional audience include VCs and other strategic investors in big
data markets

2. Technology for Big Data?: The Need of the Hour
fradulant claims
We have too much
Clinical datasets
claims data data, but too few There is lack of
laboratory reports resources
adverse drug reactions analytical skills to
online
create value from
extensive Healthcare interactions data
An Unmet Demand for CIOs
electronic imaging instrumented
Manufacturing production machinery
medical records
catalogs, stores Energy
sales
channels
Big Data demand forecast
Retail Explosion data
retail purchase
Financial labor
history
agencies We can’t get data
product Media & Services
information into right people
tax filing
Communication activities in the
computer-aided design Our organization organization
sales forecasts lack right skills to
blogs multimedia content
identified R&D and product effectively manage
inconsistencies design databases data
regulatory filings
Top Challenges for CIO’s
CIO Challenge Thermometer
Demand for technology that can Reconcile Disparate Data
process large volume of data Sources
(Terabytes, Records, Data Lack of Organizational View
Transactions, Tables/files) Quality/Accuracy of Data
Accessing Risk of Data
Volume Big Data Right Data Leaks
Technology in
Demand Data Timeliness of
Value Security Data
Variety Velocity High Data
Management Costs Storage
Demand for technology that Demand for technology that Capacity
can process various types of can quickly process data
data (batch neartime, (Structured, unstructured,
realtime, streams) semistructured, etc) 0

3. How Enterprises Create Value from Data through Analytics? The Big Data Era!
Departmental Analytics Enterprise Analytics Big Data Analytics
• Initial data warehouse model and • Standardized data models • Clear data management strategy
architecture • Database mining, high • Business analytics competency
• Limited use analytical data due to performance computing and centers are established with data
fewer business analysts analytical appliances scientists
• CIO (Chief Information Officer) • Tech savvy analytical modelers and • Solve complex problems through
level of engagement in data statisticians were used competency centers
management is limited • CIO involves in data management • CIO plays a transformative role in
• Few KPI (Key Performance strategies decision taken by the organization
Indicators) in Revenue Generation • Significant impact in revenues • Frame new business strategy and
were found were monitored and measured competitive differentiation based
regularly on analytics
In-memory Analytics
Mobile Business Comprehensive
Intelligence Applications: Analytics
Cloud Computing
Data Visualization Predictive Analytics across Industry Verticals
Map Reduce Text Mining Advanced Analytics
SQL-based Business Storage Solutions
Intelligence OLAP Framework
CIOs used Traditional Business CIOs focused Towards Data Storage CIO will focus towards Big Data Analytics
Intelligence tools
2006 2008 2010
CIO Technology Adoption Roadmap
Source: Arjunvasan, Cisco Systems

4. Global Big Data Investment Scenario (2009-12)
E
Total funding in Big Data
The total investment in
Analytics have improved
Big Data technologies
from $76.5 Million in
have improved from 6
2009 to $700 Million in
deals in 2009 to 25
2011
deals in 2011
Investor Inclinations Vs.
N Top Big Data Technology
S
Top Beneficiaries Segments
Hadoop Applications
Big Data Analytics Platforms
Big Data-as-a-service
Investors are actively
investing in technologies Non-Hadoop Platforms
developed by new market
players
Top Investors
W

5. Investment Opportunity Analysis for IT Solution Developers & other Investors
Opportunistic Big Data Technology Segments Analyst Insights
Opportunity Strategy Evaluation (OSE) Grid Big Data Analytics Platforms and Applications
10 With increasing demand among organizations to generate value from their
existing abundant data, investing in Big Data Analytics Platform Developers
is poised for success
Non-Hadoop Big
Hadoop Distributions
Data Platforms Big Data VC funding has increased phenomenally in this market sub-segment with
Level of Attarctivess
Analytic
Next Generation Platforms & 266% increase in funding from beginning of 2008 to 2011. Cloudera,
Data Big Data-as-a- Applications HortonWorks, MapR, Opera Solutions are few major beneficiaries in VC
Warehousing Service funding with few portfolios in Series D. Companies focus on certification &
Hadoop
5
Distributions
training programs in big data
Non-Hadoop Big Data Platforms
Non-Hadoop Big Data Platforms have long-term (2-3 years) success assured,
as far as the penetration of this technology is concerned. Companies in this
segment have been attracting VC funding and from other investor sources.
The recent IPO of Splunk has created huge waves in this market segment.
0 Next Generation Data Warehousing
0 5 10
The importance of next-generation data warehousing solutions is evident
Probability of Success from the recent acquisitions of vendors (Vertica by HP in 2011; Greenplum
Source: Arjunvasan by EMC in 2010 and AsterData by Teradata in 2011). This segment is more
matured, unless new innovations emerge in future
NOTE
Investment opportunity analysis was performed based on analysis of each
big data technology segments under the following factors: Big Data-as-a-Service
• Level of Attractiveness: Sunk Cost, Demand from Industry Verticals, This market segment is poised to grow tremendously in future, as its
Favorable Government/Regulatory Initiatives and Barriers to Market Entry implementation saves cost in the form of recruiting ‘data scientists’ and big
• Probability of Success: Research Efforts, Challenges to Tackle, Criticality of data infrastructure costs. R&D investment and solving implementation
barriers will increase the probability of success for investors
Challenges, Funding

6. Strategic Investment Options for IT Heavyweights & Venture Capitalists
Current Funding Status Vs. Financial Performance of Key Portfolios
Top Strategic Investors
Strategic Investment Options
Each Segment shows Big Data Revenues of pure-play vendors in big
IPO
With evaluated high probability of success, Hadoop Distributions
Series D
Funding Series (in Segments)
vendors (such as Cloudera) can make best strategic investment
partners in the near term (1-2 years).
Big Data-as-a-Service vendors have potential to make big
Series C
wave in the Enterprise Software market, but funding is
needed to improve few technical barriers
Big Data Analytics Platforms vendors are strategic partners within the big data
Series B
data segment $0 - $50 million
industry. They drive industry growth by partnering with vendors from other big data
technology segments Big Data Market Segments
Hadoop Distributions
Series A
Very few seed investments indicate that it is time to start investing Non-Hadoop Big Data Platforms
in these technologies
Big Data Analytic Platforms & Applications
0% 25% 50% 75% 100% Big Data-as-a-Service
Revenue from Big Data as a % of Total Revenue
Next Generation Data Warehousing
Source: Arjunvasan, Wikibon
NOTE:
• Suggestions for strategic investments quoted in the above chart is based on performance of innovative, pure-play big data solution developers,
level of funding and revenues. It is vital to ensure the suggested strategic investment fits well with your business model and customer demands
• Big data innovations have been primarily from pure-play companies, which have lured investment in the form of venture funding and through
IPO (Initial Public Offering). In addition to connecting with venture capitalists, it is also important to evaluate IP (Intellectual Portfolio) of each
segment to make an informed decision

7. Technology Transition from Business Intelligence to Big Data Intelligence
M&A Strategy for Big IT Vendors
2007 Big Data market
(shows
consolidation
rs Cranes 2008 trend similar to
sto
In ve Business Intelligence
p Infrastructure Data Integration Business
To Enterprise Resource Planning Intelligence
Database
Text Mining
2009 Content market (from
Management
Predictive Demands Transform Technologies! 2007 to 2008)
Analysis •Predictive Analysis to help companies differentiate, compete and
Data Qualty
es succeed
ogi
R&D Data nol
ech •BI solutions that address business specific and industry vertical issues
Management
To pT
Risk •Independent performance layer that fits enterprise infrastructure
Reporting
Leveraging the Big Data Opportunity
Data Mining Mergers & Acquisitions
Traditional Business Operational Business
2010 Intelligence Intelligence • There is a greater demand
Technology Transformation
Web for IT organizations to
Analytics integrate Hadoop into existing
Storage database to gain competitive
advantage in the industry.
Planning 2017
Analysis • With mature sales channels
and support services, Cloudera
and MapR Technologies could
Charts be prospective candidates for
Data Analysis strategic investment
2011
• Market consolidation is
expected by 2017 and will be
worth $50 billion
2012

8. For more details:
Arjunvasan Ambigapathy
a.arjunvasan@gmail.com
Tel: +91-9962361689