Uploaded byMatt Turck
Big Data, Big Deal? (A Big Data 101 presentation)
The document is a slide deck prepared by Matt Turck for non-technical audiences to introduce the concept of big data, outlining its characteristics such as volume, variety, and velocity. It discusses the need for new technologies to process this data, highlights the emerging role of data scientists, and emphasizes the significance of data-driven decision-making in various industries. The presentation also touches on the distinction between big data and small data, advocating for a balanced approach to data utilization.
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Big Data, Big Deal? (A Big Data 101 presentation)
- 1. Big data, bigdeal? February 2013 Matt Turck Twitter: @mattturck Blog: http://mattturck.com
- 2. Background: I preparedthis slide deck for a couple of “Big Data 101” guest lectures I did in February 2012 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.
- 3. What does Targetknow 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 whatcomputers have always done?
- 6. What’s different thistime? Volume. Variety. Velocity.
- 8. Facebook warehouses 180petabytes of data a year
- 9. Twitter manages 1.2million deliveries per second
- 10. New sources ofdata
- 11. Twitter manages 1.2million deliveries per second
- 12.
- 13. Big data isdata that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it. Edd Dumbill, O’Reilly
- 14. A new breedof technologies
- 15. Big Data Landscape Infrastructure Analytics Applications NoSQL Databases Hadoop Related Analytics Solutions Data Visualization Ad Optimization Publisher Marketing NewSQL Databases Statistical Computing Tools Social Media MPP 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 Sources Crowdsourcing 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)
- 16. A new breedof people: Data scientists engineering math nerds nerds nerds nerds comp sci hacking awesome nerds Credit: Hilary Mason, Bitly
- 17. Sexy nerds? “Data Scientist: The Sexiest Job of the 21st Century” October 2012
- 18.
- 19. Terms worth remembering Structuredvs. unstructured data Hadoop Cloud computing Data visualization Machine learning Predictive analytics
- 20. So what doyou do with all that technology?
- 21.
- 22.
- 23.
- 24.
- 25.
- 26.
- 27.
- 28.
- 29. But what aboutsmall data?
- 30. Moneyball is (relatively)small data
- 31. Nate Silver is(relatively) small data
- 32. Most companies onlyhave small data
- 33. It’s not aboutbig data for the sake of big data
- 34. Data-driven management “In Godwe trust. Everyone else, bring data”
- 35.
- 36. Easier than everfor any business to be truly data-driven
- 37. Thanks! Learn more: NYC Data Business Meetup meetup.com/NYC-Data-Business-Meetup/
Editor's Notes
- #2 This is going to be a talk for people who love the internet.
- #4 The true story of bitly, engineering, data science, loveHow to do data science at scaleBuilding teams and keeping people happyClever tricks
- #5 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.
- #6 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.
- #7 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.
- #10 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.
- #11 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.
- #12 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.
- #13 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.
- #14 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.
- #15 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.
- #17 Asking questions.
- #18 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.
- #19 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.
- #20 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.
- #21 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.
- #22 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.
- #23 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.
- #24 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.
- #25 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.
- #26 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.
- #27 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.
- #28 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.
- #29 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.
- #30 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.
- #31 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.
- #32 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.
- #33 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.
- #34 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.
- #35 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.
- #36 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.
- #37 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.