This document discusses the big data analytics market opportunity. It notes that the volume of data from various sources is growing exponentially. It then outlines the life cycle of big data, reference architectures, and characteristics of big data. It discusses drivers of big data, pain points for enterprises, and the market opportunity for big data analytics. It predicts strong growth in spending on big data analytics and outlines types of analytics initiatives and trends in big data technology.

1. BIG DATA ANALYTIC MARKET
OPPORTUNITY
STANLEY WANG
SOLUTION ARCHITECT, TECH LEAD
@SWANG68
http://www.linkedin.com/in/stanley-wang-a2b143b

2. BIG DATA CHALLENGE

3. 12+ TBs
of tweet data
every day
25+ TBs of
log data
every day
?TBsof
dataeveryday
2+
billion
people on
the Web
by end
2011
30 billion RFID
tags today
(1.3B in 2005)
4.6
billion
camera
phones
world wide
100s of
millions
of GPS
enabled
devices
sold
annually
76 million smart
meters in 2009…
200M by 2014

4. The Life Cycle of Big Data

6. Reference Architecture of Big Data

7. Hot Topic – Big Data Analytics
• Cost effectively manage and analyze
all available data in its native form
unstructured, structured, streaming
ERP
CRM RFID
Website
Network Switches
Social Media
Billing
• New technology makes it
possible to analyze ALL
available data

8. Big Data Characteristics

9. Big Data Changes in Game

10. What’s Driving Big Data?
- Ad-hoc querying and reporting
- Data mining techniques
- Structured data, typical sources
- Small to mid-size datasets
- Optimizations and predictive analytics
- Complex statistical analysis
- All types of data, and many sources
- Very large datasets
- More of a real-time

11. Flow of Data Integration

13. Analytic Shift in Paradigm

14. Big Data Pain Point to Enterprise
• Lack of Skill Sets: Pressure of time to market, consider using
codeless analytic environment to increase productivity;
• Comprehensive Infrastructure: Need to integrate with
existing systems;
• Heterogeneous Data Integration: Reduce the cost of upfront
data preparation and engineering;
• Data Governance: Ensure fair usage of data and its related
inference;
• Predictable Cost Analysis: Properly allocate funding and
align resources ;

15. MARKET OPPORTUNITY

17. Big Data Revenue By Segment

18. Big Data Analytics Spending Trend

19. Big Data Analytics Market Prediction
• The Advanced and Predictive Analytics (APA) software
market is projected from grow from $2.2B in 2013 to
$3.4B in 2018, attaining a 9.9% CAGR in the forecast
period;
• Cloud-based Business Intelligence (BI) is projected to
grow from $.75B in 2013 to $2.94B in 2018, attaining a
CAGR of 31%;
• A.T. Kearney forecasts global spending on Big Data
hardware, software and services will grow at a CAGR of
30% through 2018, reaching a total market size of
$114B. The average business expects to spend $8M on
big data-related initiatives this year;

20. Big Data Analytics Market Outlook

22. Types of Big Data Analytics Initiatives

23. Vision on Big Data Technology Trend
• With optimizing hardware and software to work together for
Big Data workloads, Enterprise Big Data applications will
expand rapidly and be deployed both on-premises and in the
cloud environment;
• Vendors dominating IT markets such as Oracle, IBM, and
Microsoft, will continue to extend their reach to include Big
Data initiatives. There are rooms for some new competitors
to emerge, but as is often the case, these will tend to cater
to niche cases and will be acquired eventually by one of the
large IT vendors;

24. Unified Big Data Access Platforms
• Unified information access platforms will emerge to knit
together information silos across the enterprise, no matter
the format or the content;
• Address the complex nature of the disparate security,
archiving, source repository, multiple schemas and
terminology;
• Combining elements of database, business intelligence, and
search technologies;
• Capable of indexing and integrating large volumes of
unstructured, Semi structured, and structured data into a
unified environment for information gathering, analysis, and
decision support;

25. Advanced Analytics Solutions
• One of the inhibitors of growth for standalone advanced
analytics tools is the insufficient number of analysts with
appropriate skills;
• Vendors addressing the challenge and the opportunity to
focus efforts on prebuilt advanced analytic applications will
be emerging as key players in the Big Data market;
• The shortage of trained Big Data technology experts will
inhibit adoption and use of Big Data technologies, and it will
also encourage vendors to deliver these technologies as
cloud-based solutions;

26. Market Opportunity Statement
• Big Data Analytics are at the heart of most opportunities in
the enterprise market. Articulate our value proposition by
connecting technology capabilities to business problems
with analytical solutions;
• Open source tools such as Hadoop can effectively expedite
for retrieving and moving through complex data, but still
difficult to take the next step and pose intelligent questions;
• Sophisticated tools for data integration and analysis on big
scale are largely lacking today. Vendors that create tools and
applications can take opportunity as a lever to seize market
leadership positions;

27. Big Data Analytic as Service
• The growth in cloud computing will likely mean that end
users will choose new applications and services, based less
on the technology itself and more on the business value they
deliver;
• Big Data as Service emerges as opportunities to support
small business with operational workloads and information
access;
• Vendors that can develop easy-to-use tools for non experts
will have a ready market;