Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.



Published on

A MapR presentation.

Published in: Business
  • Be the first to comment

  • Be the first to like this


  1. 1. Hi, how are you?
  2. 2. The Augmented Reality Sandbox
  3. 3. “Present on anything. Make it look nice.”
  4. 4. Some of the things I considered talking about.
  5. 5. A talk that went poorly
  6. 6. Not Tom Selleck
  7. 7. Beer
  8. 8. (I realize this isn’t a topic you often talk about with a prospective employer) But I love beer.
  9. 9. Beer, and beer brewing, is complex.
  10. 10. …and it’s Big Money Beer Sold in 2013: 196,241,321 barrels (or over six billion gallons) of that 15,302,838 barrels was craft beer (or nearly 500 million gallons) all data according to Brewers Association at
  11. 11. $100 Billion Made on Beer Last Year of that $14.3 Billion Dollars on Craft Beer all data according to Brewers Association at
  12. 12. There was a 17.2% increase in craft beer sales from 2012-2013. Hundreds of new breweries are opening each year. Big breweries trying to catch up with own “craft” brands. “Craft” is a relatively new market. What to make? What to sell? How do we figure it all out?
  13. 13. (though, in this case, the dataset isn’t *that* big)
  14. 14. 2000-2012 1.5 million entries 400,000+ users
  15. 15. With craft beer on the rise, so many choices. How to make them?
  16. 16. “Notes on this brew: Brewed with dirty gym socks, and pigfeed corn. May be paired with industrial cleaner to strengthen its effect" –MaltLickyWithTheCandy
  17. 17. “Look, I don't know what everyone is bashing...this shit costs like $10 for a 30 pack what do you expect? Golden ambrosia served in a chalice while angels whistle on your dong?” –aheedratron
  18. 18. Overall, Trusted Source
  19. 19. Thesis: We can infer what beers we should brew based on Beer Advocate data.
  20. 20. A Quick Note: IPAs are terrible.
  21. 21. Some data analysis from the beer advocate stats.
  22. 22. When are we drinking?
  23. 23. Does the data work?
  24. 24. September
  25. 25. October
  26. 26. So, we’re pretty sure this works. What’s next?
  27. 27. Next Steps
  28. 28. Ted Dunning Anomalous data Look for the “norm” Aggregate ratings with current data, and see if we can find beers that stand out as having potential.
  29. 29. Next Steps • Build additional Dashboards that help answer questions: • What beers should I brew this month? • What flavor profiles should I be matching for this time period? • What beers have the potential to sell well, but aren’t being pushed by competitors?
  30. 30. A Beginning Toolset
  31. 31. Thanks.