4 steps to successful big data product management
DESCRIPTION
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.TRANSCRIPT
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You don’t need to be a data scientist but it helps!
J. Travis Turney, MBA
Co-founder @DataScienceATL
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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?
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Vision
What is the business problem you need to solve?
Revenue growth?
Cost control?
What valuable answers are you seeking in the data?
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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?)
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Figure provided courtesy of Brad Anderson, Solution Architect,
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Tools – Structured dataStructured Query Language (SQL)
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Tools – Unstructured (NoSQL)What if your data isn’t structured?
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Tools – Unstructured (NoSQL)NoSQL vendors
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Tools – Streaming
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Tools – Batch processingHadoop – “Horizontally scalable” distributed
platform
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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
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So Where Can I Find Talent?
@DataScienceATL meetup
Monthly events with local data science thought leaders
Great opportunities to sponsor, network, & recruit!
www.meetup.com/Data-Science-ATL/