Documentation in Data & Analytics

Documentation is something that gets misunderstood a lot IMO.
These are my macro-level conceptual thoughts on the topic (applies to Data and beyond)

Its Objective: Context & Clarity

In more details, you want to make sure that the future readers/users (yourself included!) of the models/transformations/query/code/etc will understand what has been done without the need to come back to you and ask you about the work your you did and why.

It’s flavours: More diverse that what we might think

A couple of ones I consciously consider:

Ultimately, one needs to I ask oneself:

will the next reader face friction with what I am creating ?’.

If the answer is yes, simplify further and document better!

Some final guiding thoughts

And one, last bonus is: when you become smooth and good at documenting important pieces of experience/knowledge, write blog posts around them becomes easy! This new post of mine is basically a repurposed internal documentation I wrote for the Data Team @ The Fabulous

--

--

Head of Data & Analytics @ Fabulous. On Data, Analytics, Tech, Business and Life…

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
Ahmed Omrane

Head of Data & Analytics @ Fabulous. On Data, Analytics, Tech, Business and Life…