Agile and Data Science — A Match Made in Heaven?

Many of the challenges that Data Science teams face are complex adaptive problems and hence fall into the complex domain. This means that at the outset the end solution is unknown while through experimentation we may discover that our initial hypothesis is wrong and needs to change. This may lead to a new hypothesis and more experimentation. In the complex domain there are no best practices to follow but only emergent and adaptive solutions.

Agile promotes empiricism to help solve complex adaptive problems, like the ones that data science teams are facing. As such, Agile is a perfect fit for managing data science projects compared to traditional project management approaches which are best fit to problems with a known scope and solution.