Elephant Camo: Getting Big Data to Blend In [SXSW Proposal Teaser]

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Figuring out what to do with Big Data would be much easier if it were just a math problem. Math problems are definable, scaleable and solveable - but it takes more than this to transport an organization from data flooded to data fueled. Analysts and Scientists need to study their own culture - the business of doing business - to understand the needs, motivations, and fears of the world around them before they can make an impact. In this presentation, we will take that journey of insight, to understand how organizations react to the overtures of Big Data solutions, and define practical steps for getting them past the hurdles. We'll talk about some of the problems Big Data can solve, and how we can explain those solutions in ways that tap into our natural biology. Most importantly, we'll give you quick-win ideas for getting started when you get back - connecting to the problems of your business, and the people that have them.

VOTE FOR THIS PROPOSAL HERE: http://panelpicker.sxsw.com/vote/42029

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Elephant Camo: Getting Big Data to Blend In [SXSW Proposal Teaser]

  1. 1. Elephant Camo: Getting Big Data to Blend In
  2. 2. Elephant Camo: Getting Big Data to Blend In -—. ._—. ... . . _.. :.. ... ... ... -.‘. ... _ We begin lllerally - with a bang, mma. .n. ... ... . ». ... ... ..u. buslna6aWIIrvIrvRIvDr1nuueJar. he mmmmqmmm
  3. 3. We begin - literally — with a In 1802, Eleuthére Irénée du Pont founded a business in Wilmington, Delaware, for the purposes of making what he knew: Fast-forward 100 years and DuPont is wildly successful, manufacturing cellulose, lacquers, and practically every kind _ of industrial chemical in use. Which brings in a lot of money. .. and a
  4. 4. It's practically a problem of too T : i'r business. By 1912, Pierre S. duPont decided it was a good idea to diversify, so he added some growth stocks to his company's portfolio. His particular focus was a plucky new company called General Motors in a brand new industry: the automobile. Business went well, but knowing just N’ : w ~+r= .W became a ’: ‘I: ': V '—. :r'
  5. 5. Call it a turn-of-the century level lfi tr rlur -7 I-«JL0; l.4. "-ta; i _ '4.‘ 4‘ ; i i_ 9 i- y l~ ; um. ft. » 9 o». ul. . . r‘. DuPont had plenty of numbers on their businesses, but when you're involved in everything from cars to _y cellulose to combustables, how can you compare ' them all together? It took Donaldson Brown, a Virginia Polytech grad and former explosives salesman to bring it all together, in an efficiency report he would submit later that same year DuPont started investing in GM. It included a formula he created called "Return on lnvestment": 7 i.
  6. 6. The same problems exist today, at a 21st-century level. - Businesses need not just better / faster/ smarter answers, but correct questions to find them. - Data scientists often have to venture far afield to find these answers - far beyond what their organizations may know or even yet trust. - Decision makers exist in a world of "automagical" things, and expect nothing less from Big Data It's not just about conquering the elephant, it's about making it fit in.
  7. 7. Want to learn more about Elephant Camo? If you're interested in learning about how to not just make Big Data work, but actually fit it in your organization's culture, I hope you'll vote for my SXSW proposal on this very topic. Vote now! http: [[bitlyzelephantgamopitgh
  8. 8. Elephant Camo: Getting Big Data to Blend In .4.. ... m.. .,. ... . Thanks for your vo: e!

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