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Demystifying Big Data

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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.

Published in: Business
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Demystifying Big Data

  1. 1. Demystifying Big Data Aaron Weber, Product Architect Spiral16
  2. 2. Since 2007, Spiral16′s group of developers, dataanalysts and researchers have been quietly buildingone of the most powerful data mining platforms onthe planet, backed up by one of the mostexperienced social data analysis teams in theindustry.Spiral16′s social media and web researchplatform and data analysis services are designed tohelp everyone from CEOs to CMOs, marketing andPR agencies, and researchers. Demystifying Big Data 11/8/2012 2
  3. 3. So what is Big Data? Demystifying Big Data11/8/2012 3
  4. 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 Demystifying Big Data11/8/2012 4
  5. 5. So what is Big Data? Exabytes Created By Year 3000 2500 2000 1500 1000 500 0 2006 2007 2008 2009 2010 2011 2012 Demystifying Big Data11/8/2012 5
  6. 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. Demystifying Big Data11/8/2012 6
  7. 7. So what is Big Data? Demystifying Big Data11/8/2012 7
  8. 8. So what is Big Data? Inevitable Demystifying Big Data11/8/2012 8
  9. 9. Big Data vs Little Data Little Data = Relational Databases Demystifying Big Data11/8/2012 9
  10. 10. Big Data vs Little Data Big Data = Non-Relational Databases Demystifying Big Data11/8/2012 10
  11. 11. Big Data vs Little Data Demystifying Big Data11/8/2012 11
  12. 12. Big Data vs Little Data • Structured Demystifying Big Data11/8/2012 12
  13. 13. Big Data vs Little Data • Structured • Organized Demystifying Big Data11/8/2012 13
  14. 14. Big Data vs Little Data • Structured • Organized • Hierarchical Demystifying Big Data11/8/2012 14
  15. 15. Big Data vs Little Data • Structured • Organized • Hierarchical • Rigid Demystifying Big Data11/8/2012 15
  16. 16. Big Data vs Little Data Demystifying Big Data11/8/2012 16
  17. 17. Big Data vs Little Data • Unstructured Demystifying Big Data11/8/2012 17
  18. 18. Big Data vs Little Data • Unstructured • Disparate Demystifying Big Data11/8/2012 18
  19. 19. Big Data vs Little Data • Unstructured • Disparate • Non-Hierarchical Demystifying Big Data11/8/2012 19
  20. 20. Big Data vs Little Data • Unstructured • Disparate • Non-Hierarchical • Reusable Demystifying Big Data11/8/2012 20
  21. 21. So why Big Data? • Find trends in existing data Demystifying Big Data11/8/2012 21
  22. 22. So why Big Data? • Find trends in existing data • Better consumer targeting Demystifying Big Data11/8/2012 22
  23. 23. So why Big Data? • Find trends in existing data • Better consumer targeting • Predictive analysis Demystifying Big Data11/8/2012 23
  24. 24. So why Big Data? • Find trends in existing data • Better consumer targeting • Predictive analysis • Making better use of what you already know Demystifying Big Data11/8/2012 24
  25. 25. Who is using Big Data? Demystifying Big Data11/8/2012 25
  26. 26. Who is using Big Data? o Financial Services o Healthcare o Retail o Marketing o Politics Demystifying Big Data11/8/2012 26
  27. 27. The Business of Big Data Demystifying Big Data11/8/2012 27
  28. 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 Demystifying Big Data11/8/2012 28
  29. 29. The Business of Big Data Demystifying Big Data11/8/2012 29
  30. 30. So what is Big Data? Inevitable Demystifying Big Data11/8/2012 30
  31. 31. So what is Big Data? Inevitable And that’s a good thing. Demystifying Big Data11/8/2012 31
  32. 32. Big Data’s Big Questionso 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? Demystifying Big Data11/8/2012 32
  33. 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

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