This document is a presentation on big data given by Martyn Crew, founder and CEO of Catch the Big Data Wave. The presentation defines big data, discusses why it is important to customers, outlines the big data ecosystem and options available, and who is making money from big data currently. The agenda includes defining the 3Vs of big data, examining big data's importance to customers, reviewing the big data ecosystem and options, identifying sectors making money from big data, and addressing that big data solutions can vary in scale and need.

1. Catch the Big Data Wave
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2. Presented by
Catch the Big Data Wave
Martyn Crew, Founder and CEO

3. Snap Poll
 Do you have any big data initiatives
underway?
 Yes
 No
 Planned
 Not sure

4. Agenda
 What is Big Data?
 Why is it Important to Your Customers?
 The Big Data Ecosphere
 Who’s Making Money from Big Data?
 Big Data isn’t always BIG
 Q&A

5. What is big data? The 3 Vs

6. Volume
 New sources e.g.
web logs, social
media
 Saving more data
at raw level for
better analysis

7. Velocity

8. Variety

9. Presented by
Why is big data important
to your customers?

10. Consultants vs. real world
 The McKinsey View
 Save everything
 Bring in the data
scientists (or McKinsey
consultants)
 Look for correlations –
find transformation
nuggets of data

11. Consultants vs. real world
 The Real World View
 Save what makes sense
 Save more of it because more
data = better trend analysis
 Analyze it faster
 Cross-reference existing data
e.g. CRM data with new data
sources such as social media
to gain more insight

12. Customer view

13. Big data - more and better BI
 Have all your information at your fingertips
 Unify all your analytical time horizons
 Enable more powerful business applications e.g.
customer experience optimization
 Enable faster answers using interactive analysis,
modeling or unstructured data

14. Presented by
Big Data …
… Big (Marketing) Problem

15. Poll Question
 Who do you thinks owns big data for most
of your customers?
 CEO
 CIO
 CFO
 CMO
 Don’t know

16. Who owns big data?
16
•Social media analytics
•Social CRM
•Brand monitoring
•Sentiment analysis
•Marketing campaign optimization
•Customer experience management
•CRM next best action
•Customer churn mitigation
•Loyalty & promotions analysis
•Behavioral analytics
•Influencer analysis
•Ad placement optimization
•Financial risk analysis
•Supply chain optimization
•Defect tracking
•Device monitoring
•Root cause analysis
•IT log analysis
•Event analytics
•Network analytics
•BI
•DW
•ETL
•Advanced analytics
•Data science
•Content optimization
•Compliance monitoring

17. Presented by
Big Data Ecosphere
and Customer’s Options

18. Big data ecosphere - Wikibon
Source: Wikibon 2012

19. Big Data today - Ventana
October 24, 2012

20. Emerging big data options
 Scalable,
easy to get going
 Highly functional
 Becomes prohibitively
expensive
 Public vs.
Private
 Distributed low cost
storage solution
 Big data = Hadoop for
many companies
 A collection of open
source technologies
 Many flavors
Cloud

21. Presented by
Who’s Making Money
from Big Data?

22. Breakdown by sector - Wikibon
Wikibon , 2012

23. Breakdown by category - Gartner

24. Presented by
One More Thought…

25. Big isn’t always BIG
 “Big to me” or “medium data”
 Big is relative
 Big data may only be about
one V e.g. variety
 Big is whatever customers
don’t have systems, skills,
money or time to
handle

26. Presented by
How do you make money
from big data?

27. 7 Steps…
1. Talk to your customers
2. Develop value proposition and messaging
3. Test with customers e.g. survey, interviews
4. Refine message
5. Develop roadmap
6. Align product to messaging
7. Talk to your customers

28. About Bootstrap
 100% high-tech focused
 Content, content, content
 Deep technical and product marketing
experience in all things data related
 Customers include Actian, AsterData,
Informatica, Jaspersoft, Kickfire, Lenovo,
Metric Insights and Treasure Data

29. Questions?
martyn@bootstrap-mktg.com
800 595-4965
@bootstrapmktg
Q&AQ&A

30. Public Cloud
Public
Scalable, easy to get going
Highly functional
Becomes prohibitively expensive
Dominated by Amazon (AWS)
Rackspace a strong contender
Emerging open source plays e.g. OpenStack

31. Private Cloud
Private
VPN meets Cloud
Addresses security, privacy
and cost concerns of public cloud
Leading players Amazon, Cisco, IBM,
Microsoft, Rackspace, VMware
Challengers – Eucalyptus, Nimbula

32. NoSQL Options
NOSQL
NoSQL movement is
emerging from open source
world
NoSQL = Not Only SQL
Good solution for web
apps not so good for big
data analytics