Deliver Your Analytics Projects on Time

Something I don't hear talked about enough in the data space is delivering work in a timely way. Software engineers spend a tremendous amount of time attempting to predict and control delivery timelines; why don't data professionals do the same? There seems to be consensus that predicting the time required of a data project is inherently impossible because it's fundamentally an act of discovery.

I think this is false. I've personally delivered hundreds of analytics sprints, and I've trained others to do the same. We deliver almost all of these sprints (95%+) on time. I think that setting clear timelines for the delivery of analytics work is critical in building the data team into a trusted advisor for business stakeholders, and in this post I share our thinking on how we do it. The core of our approach is:

  1. Use Agile
  2. Eliminate as much uncertainty as possible before writing stories.

This is not rocket science—you can easily apply it in your org.


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