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Winning with analytics - Data Innovation Summit 2015 - Made in Belgium

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Winning with (predictive) analytics is not much different from winning with beer.
15 minute key presentation by @pythongeert on Data Innovation Summit 2015 - Made in Belgium by datasciencebe.com

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Winning with analytics - Data Innovation Summit 2015 - Made in Belgium

  1. 1. winning with analytics made in belgium @pythongeert
  2. 2. analytics and beer data innovation summit 2015 - made in belgium @pythongeert this is not the first link between analytics and beer: in 1908, student’s t-distribution was developed by a scientist hired by guinness to optimize brewing quality
  3. 3. trappist data innovation summit 2015 - made in belgium @pythongeert (often belgian) trappist monks produce beer that is considered the best beer in the world we’ve identified five key success factors that are common to winning with beer and analytics
  4. 4. organization “We are no brewers. We are monks. We brew beer to be able to afford being monks.” data innovation summit 2015 - made in belgium @pythongeert analytics is still not considered strategic priority for many organisations, but the tide is changing
  5. 5. data data innovation summit 2015 - made in belgium @pythongeert this tide is changing mainly because of the hype around big data. data (of any size) provide the ingredients of analytics. no more, no less, they are one component of the success this picture contains hops, one of the key ingredients for beer
  6. 6. technology data innovation summit 2015 - made in belgium @pythongeert the big data hype has resulted in a big data technology rush but let’s not forget the role of sound analytical methodology
  7. 7. talent data innovation summit 2015 - made in belgium @pythongeert talent used to be scarce but the number of participants at this summit proves otherwise despite the challeging job requirements
  8. 8. usage talent data technologyorganization data innovation summit 2015 - made in belgium @pythongeert the most important component all models that are not used, are useless it makes a lot of sense to value analysts on the output used instead of the output produced
  9. 9. Additional details: pythonpredictions.com datasciencebe.com

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