The Analytic Value Chain

I am currently hiring for a Director for my statistics team and it is a hard position to hire for (if you think you might be qualified, please send me a resume.) A perfect candidate has to have skills all across the data analysis value chain. I imagine you are thinking "consultant speak"; maybe so, but I think of analyses as a product that needs to run through an analytic "factory." You can manufacture impactful analyses, just like GM manufactures cars. And I think of the value chain as having 9 steps.

1. Define the problem
2. Determine data requirements
3. Locate relevant data
4. Extract, Transform, and Load the data
5. QA data
6. Understand relationships in data
7. Do the analysis
8. Create presentation materials
9. Share results with business

It is hard to find someone who has the business skills to understand the problem faced by my internal clients, has the technical skill to manage the analysis, has the process re-engineering skills to improve the process (it is a factory, remember?), and the communication skills to present the results back to the business.

In the next couple of weeks, I'll discuss each of the steps in the value chain. Hopefully this set of posts will help folks as they set up their own analytic shops.