[Originally posted on LinkedIn]

Topline:

Begin with  Data Science / Machine Learning / Ai UseCases. Many teams can get bogged down in “getting the foundations” right with their systems and data infrastructure and are slow in delivering tangible benefits to their business stakeholders (obviously the base necessities need to be set up – environmens, access, git, etc). Begin with solving problems with usecases. As you deliver more, you will begin to understand clearly the data infrastructure needed for your future state. A customer lifecycle is a tried and tested method for identifying usecases that can be delivered rapidly.

Long of it:

Getting a Data Science team set up inside organisations can seem like you are trying to eat an elephant! Pulled in many potential directions – Where do you even start?

Start small, bite by bite, use-case by use-case.

Too many organisations focus on cure-all solutions, silver bullets, or major transformational initiatives. In reality, truly innovative companies proceed one step at a time, demonstrating measurable outcomes each step along the way. Data solutions are compounded as with iterative development, more data is captured which can accelerate further development.

Start by identifying data use cases within your customer lifecycle, which often have valuable low hanging.