You are viewing a single comment's thread from:

RE: Managing Data Science projects

in #datascience4 years ago

Oh yes, a familiar story in that if you don't know what data you want at the end of a project or process, you will often struggle and get lost in a giant soup of numbers and data!

Knowing what you need for the final result will make it a lot easier to work back to the beginning 🙂

Posted using Dapplr

Sort:  

True. I've developed a methodology to deal with that. When me or someone else from EAI (my company) start developing a data strategy with a client, we list all the different types of data, data sources (sometimes the same data has the same sources), and relevance for the different use cases in the organization. We do this even with data that has not yet been collected (or purchased).

This is part of our data strategy program, this way companies that work with us know from the start what data they need, what they should be collecting, what are the dependencies, and their priorities! :)

Thanks for the comment, it means a lot!