The document outlines essential considerations for successful big data product management, emphasizing the importance of vision, data, tools, and execution. It highlights the need to understand the business problem at hand and assess data requirements, while also providing insights into various data management tools including SQL, NoSQL, and Hadoop. Additionally, it suggests engaging with local data science communities to find and recruit talent.

1. You don’t need to be a data scientist but it helps!
J. Travis Turney, MBA
Co-founder @DataScienceATL

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. Vision
What is the business problem you need to solve?
Revenue growth?
Cost control?
What valuable answers are you seeking in the data?

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?)

6. Figure provided courtesy of Brad Anderson, Solution Architect,

7. Tools – Structured data
Structured Query Language (SQL)

8. Tools – Unstructured (NoSQL)
What if your data isn’t structured?

9. Tools – Unstructured (NoSQL)
NoSQL vendors

10. Tools – Streaming

11. Tools – Batch processing
Hadoop – “Horizontally scalable” distributed
platform

12. 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

13. 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/