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Big Data, Big Deal? (A Big Data 101 presentation)

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Background: I prepared this slide deck for a couple of “Big Data 101” guest lectures I did in February 2013 at New York University’s Stern School of Business and at The New School. They’re ...

Background: I prepared this slide deck for a couple of “Big Data 101” guest lectures I did in February 2013 at New York University’s Stern School of Business and at The New School. They’re intended for a college level, non technical audience, as a first exposure to Big Data and related concepts. I have re-used a number of stats, graphics, cartoons and other materials freely available on the internet. Thanks to the authors of those materials.

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  • This is going to be a talk for people who love the internet.
  • The true story of bitly, engineering, data science, loveHow to do data science at scaleBuilding teams and keeping people happyClever tricks
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Asking questions.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.
  • Very different perspective, we have constrained resources, short time, and an expectation that what we do is relevant to the real world in some way.We build the system on this data, and then scale it for production use.

Big Data, Big Deal? (A Big Data 101 presentation) Presentation Transcript

  • 1. Big data, big deal? February 2013 Matt Turck Twitter: @mattturck Blog: http://mattturck.com
  • 2. Background: I prepared this slide deck for a couple of“Big Data 101” guest lectures I did in February 2012 atNew York University’s Stern School of Business and atThe New School. They’re intended for a collegelevel, non technical audience, as a first exposure to BigData and related concepts. I have re-used a number ofstats, graphics, cartoons and other materials freelyavailable on the internet. Thanks to the authors of thosematerials.
  • 3. What does Target know about pregnant women?
  • 4. Hype Data is… "the new gold” “the new black” “the new plastic” "the new oil” “the new frontier”
  • 5. Isn’t it what computers have always done?
  • 6. What’s different this time? Volume. Variety. Velocity.
  • 7. Facebook warehouses 180 petabytes of data a year
  • 8. Twitter manages 1.2 million deliveries per second
  • 9. New sources of data
  • 10. Twitter manages 1.2 million deliveries per second
  • 11. Open Government Data
  • 12. Big data is data that exceeds theprocessing capacity of conventionaldatabase systems. The data is toobig, moves too fast, or doesn’t fit thestrictures of your databasearchitectures. To gain value from thisdata, you must choose an alternativeway to process it. Edd Dumbill, O’Reilly
  • 13. A new breed of technologies
  • 14. Big Data Landscape Infrastructure Analytics Applications NoSQL Databases Hadoop Related Analytics Solutions Data Visualization Ad Optimization Publisher Marketing NewSQL Databases Statistical Computing Tools Social MediaMPP Databases Management / Cluster Services Industry Applications Monitoring Sentiment Analysis Analytics Services Security Application Service Providers Location / People / Big Data Search Events Storage IT Analytics Data SourcesCrowdsourcing Data Data Sources Collection / Real- Crowdsourced SMB Analytics Marketplaces Transport Time Analytics Cross Infrastructure / Analytics Personal Data Open Source Projects Framework Query / Data Data Access Coordination / Real - Statistical Machine Cloud Flow Workflow Time Tools Learning Deployment Matt Turck (@mattturck) and Shivon Zilis (@shivonz)
  • 15. A new breed of people: Data scientists engineering math nerds nerds nerds nerdscomp sci hacking awesome nerds Credit: Hilary Mason, Bitly
  • 16. Sexy nerds? “Data Scientist:The Sexiest Job of the 21st Century” October 2012
  • 17. Nerd talent shortage
  • 18. Terms worth rememberingStructured vs. unstructured data Hadoop Cloud computing Data visualization Machine learning Predictive analytics
  • 19. So what do you do with all that technology?
  • 20. Lending
  • 21. Trading
  • 22. Insurance
  • 23. Agriculture
  • 24. Healthcare
  • 25. Energy
  • 26. Music
  • 27. Education
  • 28. But what about small data?
  • 29. Moneyball is (relatively) small data
  • 30. Nate Silver is (relatively) small data
  • 31. Most companies only have small data
  • 32. It’s not about big datafor the sake of big data
  • 33. Data-driven management“In God we trust. Everyone else, bring data”
  • 34. Data-driven culture
  • 35. Easier than ever for any business to be truly data-driven
  • 36. Thanks! Learn more: NYC Data Business Meetupmeetup.com/NYC-Data-Business-Meetup/