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4 Steps to Successful Big Data Product Management


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This deck was the basis for a talk about big data product management I gave at Big Data Mornings (@BigDataAM) in Atlanta at @Hypepotamus on Wed August 28, 2013.

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4 Steps to Successful Big Data Product Management

  1. 1. You don’t need to be a data scientist but it helps! J. Travis Turney, MBA Co-founder @DataScienceATL
  2. 2. Big Data Product Management Vision What does success look like? Data What data do you have/need? Tools What do you need to get there? Execution Who’s going to make it happen?
  3. 3. Vision What is the business problem you need to solve? Revenue growth? Cost control? What valuable answers are you seeking in the data?
  4. 4. Know your data! How large is the data to be stored? How large is the data to be queried? What time frame is appropriate for the response? How fast is it arriving (bursts or continuously?)
  5. 5. Figure provided courtesy of Brad Anderson, Solution Architect,
  6. 6. Tools – Structured data Structured Query Language (SQL)
  7. 7. Tools – Unstructured (NoSQL) What if your data isn’t structured?
  8. 8. Tools – Unstructured (NoSQL) NoSQL vendors
  9. 9. Tools – Streaming
  10. 10. Tools – Batch processing Hadoop – “Horizontally scalable” distributed platform
  11. 11. Execution – How to get started? SQL skills are everywhere. Lots of talent. Easy to hire. Hadoop skill set growing but talent can be expensive NoSQL talent is rarer than Hadoop Streaming skills may be the most rare
  12. 12. So Where Can I Find Talent? @DataScienceATL meetup Monthly events with local data science thought leaders Great opportunities to sponsor, network, & recruit! 