http://www.spiral16.com Spiral16 product architect Aaron Weber presented this keynote at DST Systems' 2012 Transfer Agency Executives' Forum in Dallas, TX on Nov. 7, 2012. Big data is a big buzzword right now, but what does it really mean? Big data allows companies to identify trends, target customers more efficiently, run predictive analysis (see Nate Silver for recent proof), and make better use of what you already know. No one industry has the market cornered on big data. Financial services, healthcare, retail, politics, and marketing can all benefit from aggregating and analyzing big data. What is big data? Inevitable. And that's a good thing.

1. Demystifying
Big Data
Aaron Weber, Product Architect
Spiral16

2. Since 2007, Spiral16′s group of developers, data
analysts and researchers have been quietly building
one of the most powerful data mining platforms on
the planet, backed up by one of the most
experienced social data analysis teams in the
industry.
Spiral16′s social media and web research
platform and data analysis services are designed to
help everyone from CEOs to CMOs, marketing and
PR agencies, and researchers.
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3. So what is Big Data?
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4. So what is Big Data?
“A collection of data sets so large and
complex that it becomes difficult to
process using on-hand database
management tools.”
- Wikipedia
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5. So what is Big Data?
Exabytes Created By Year
3000
2500
2000
1500
1000
500
0
2006 2007 2008 2009 2010 2011 2012
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6. So what is Big Data?
In 2008 we were generating
as much stored data from the
dawn of civilization to 2003
every two days.
And that rate is predicted to
double every two years.
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7. So what is Big Data?
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8. So what is Big Data?
Inevitable
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9. Big Data vs Little Data
Little Data =
Relational Databases
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10. Big Data vs Little Data
Big Data =
Non-Relational Databases
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11. Big Data vs Little Data
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12. Big Data vs Little Data
• Structured
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13. Big Data vs Little Data
• Structured
• Organized
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14. Big Data vs Little Data
• Structured
• Organized
• Hierarchical
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15. Big Data vs Little Data
• Structured
• Organized
• Hierarchical
• Rigid
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16. Big Data vs Little Data
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17. Big Data vs Little Data
• Unstructured
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18. Big Data vs Little Data
• Unstructured
• Disparate
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19. Big Data vs Little Data
• Unstructured
• Disparate
• Non-Hierarchical
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20. Big Data vs Little Data
• Unstructured
• Disparate
• Non-Hierarchical
• Reusable
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21. So why Big Data?
• Find trends in existing data
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22. So why Big Data?
• Find trends in existing data
• Better consumer targeting
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23. So why Big Data?
• Find trends in existing data
• Better consumer targeting
• Predictive analysis
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24. So why Big Data?
• Find trends in existing data
• Better consumer targeting
• Predictive analysis
• Making better use of what you
already know
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25. Who is using Big Data?
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26. Who is using Big Data?
o Financial Services
o Healthcare
o Retail
o Marketing
o Politics
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27. The Business of Big Data
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28. The Business of Big Data
Vendor (Founded) Founded Funding (in $US mil.) # of Institutional Rounds Investors
SAC Capital, The Founders Fund, Glynn
Capital, In-Q-Tel, Reed Elsevier
Palantir 2004 $301 7
Ventures, Ulu Ventures, Youniversity
Ventures and Jeremy Stoppelman
Mu Sigma 2004 $133 2 General Atlantic and Sequoia Capital
Silver Lake Sumeru, Accel-KKR, Invus
Opera Solutions 2004 $84 1 Financial Advisors, JGE Capital and
Tola Capital
Accel Partners, Greylock Partners and
Cloudera 2008 $81 4
Meritech Capital Partners
New Enterprise Associates, Sequoia
10gen 2008 $73.4 5 Capital, Flybridge Capital and Union
Square Ventures
Amazon, Menlo Ventures, Mohr
Davidow Ventures, Bay Partners,
ParAccel 2005 $73 5 Walden International, Tao Venture
Capital Partners and Silicon Valley
Bank
Andreesen Horowitz, General Catalyst,
O’Reilly AlphaTech Ventures, Windcrest
GoodData 2007 $53.5 3
Partners, Tenaya Capital and Next
World Capital
Ignition Partners, August Capital, JK&B
Splunk(1) 2003 $40 3
and Sevin Rosen Funds
Meritech Capital, Lightspeed Venture
DataStax 2010 $38.7 3 Partners, Sequoia Capital and Crosslink
Capital
1010data 2000 $35 1 Norwest Venture Partners
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29. The Business of Big Data
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30. So what is Big Data?
Inevitable
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31. So what is Big Data?
Inevitable
And that’s a good thing.
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32. Big Data’s Big Questions
o What about the data storage and
utilization we already have?
o How do we know if our data is Big Data?
o What are the primary costs of big data?
o What answers can I get from our data?
o Where do we begin?
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33. Better data. Better decisions.
7171 West 95th Street
Suite 310
Overland Park, KS 2208
913.944.4500
www.spiral16.com
Aaron Weber
aaron.weber@spiral16.com