Bi and The Cloud: What does it all mean?

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Slide deck I used to discuss some ideas about cloud computing, business intelligence, and big data at TDWI's Solution Summit on Cloud BI. Feel free to quote but please attribute to me (Pano Xinos, 2013) or the original sources if sources are provided. All proprietary information is their respective owners and/or publishers. This presentation is Copyright

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Bi and The Cloud: What does it all mean?

  1. 1. BI  and  The  Cloud:   What  does  it  all  mean?   Pano  M.  Xinos  
  2. 2. For  the  sake  of  clarity…  
  3. 3. Necessary  evil   On-­‐demand,  self-­‐service   Broad  network  access   Resource  pooling   Rapid  elasLcity   Measured  service     hMp://csrc.nist.gov/publicaLons/nistpubs/800-­‐145/SP800-­‐145.pdf   Private  cloud   Public  cloud   Hybrid  cloud   IaaS   PaaS   SaaS  
  4. 4. PredicLve  AnalyLcs  =  Big  Data   Business  Intelligence  ≠  Big  Data  
  5. 5. THREE  IDEAS    
  6. 6. One   CHEAP,  ACTIONABLE  INFORMATION   Two   AS  “BIG  DATA”  à  MAINSTREAM   BUSINESS  INTELLIGENCE  =  BIG  DATA   Three   GARBAGE  IN,  GARBAGE  OUT   (“IT’S  NOT  INFORMATION  OVERLOAD;  IT’S   FILTER  FAILURE.”)  
  7. 7. One   CHEAP,  ACTIONABLE  INFORMATION  
  8. 8. hMp://aws.amazon.com/ec2/pricing/  (Oct.  2013)   hMp://www.google.com/trends/  (Oct.  2013)  
  9. 9. hMp://aws.amazon.com/ec2/pricing/  (Oct.  2013)  
  10. 10. $3B+  of  revenue  in  2013   can’t  be  wrong  
  11. 11. Big  Data  <3’s  Cloud     Large  Workloads   +  Cloud  CompuLng   XOXOXOX  
  12. 12. CommodiLzed  compute  combined   with  predicLve  analyLc  tools  yields   acLonable  informaLon  
  13. 13. Cloud   +  PredicLve  Data  AnalyLcs   Cheap,  AcLonable  InformaLon  
  14. 14. “Man  Spends  266  Hours  Awake  For   World  Record,  Now  Feels  Stupid”   hMp://www.wired.com/table_of_malcontents/2007/05/man-­‐spends-­‐266-­‐hours-­‐awake-­‐for-­‐world-­‐record-­‐now-­‐feels-­‐stupid/  
  15. 15. Two   AS  “BIG  DATA”  à  MAINSTREAM   BIG  DATA  =  BUSINESS  INTELLIGENCE  
  16. 16. hMp://www.infoincog.com/the-­‐big-­‐data-­‐hype-­‐cycle/  
  17. 17. It’s  not  all  doom  and  gloom  
  18. 18. hMp://bytesizebio.net/2013/04/13/terrible-­‐advice-­‐from-­‐a-­‐great-­‐scienLst/  
  19. 19. Future  success  is  measured  in   terms  of  opportunity  cost   Image:  hMp://thecareerblog.wordpress.com/  
  20. 20. Companies  that  already  have  a  BI   pracLce  will  more  easily   integrate  predicLve  analyLcs  
  21. 21. Open  source  is  a  low(er)  cost   soluLon  that  will  help  drive   adopLon  
  22. 22. As  predicLve  analyLcs  is  more   widely  adopted,  it  will  merge   with  BI  as  a  new  pracLce  
  23. 23. hMp://www.woldfitness.com  
  24. 24. Three   GARBAGE  IN,  GARBAGE  OUT   (“IT’S  NOT  INFORMATION  OVERLOAD;  IT’S   FILTER  FAILURE.”1)  
  25. 25. “It’s  not  informaLon  overload;   it’s  filter  failure.”   -­‐-­‐Clay  Shirky  (2008)   1  hMp://www.youtube.com/watch?v=LabqeJEOQyI  
  26. 26. hMp://blog.poMerzot.com/2007/09/25/garbage-­‐in-­‐garbage-­‐out-­‐and-­‐the-­‐desire-­‐to-­‐cover-­‐our-­‐own-­‐ass-­‐is-­‐ruining-­‐the-­‐world/  
  27. 27. Flickr,  Copyright  ShieldkiMen  
  28. 28. PoliLcs   hMp://www.threehundredeight.com/  
  29. 29. So,  what  does  it  all  mean?  
  30. 30. One   CLOUD  IS  CHEAP.  INFORMATION  IS   PRICELESS.   Two   GET  ON  BOARD  WHILE  THE  GETTING  IS   GOOD.   Three   EXPERIENCE  AND  INDUSTRY   EXPERTISE  SOLVES  PROBLEMS.  
  31. 31. Thank  you!   @pmxinos   PanoXinos.TDWI@gmail.com  

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