ETL is the process of moving and transforming data to get it into a more useful format.
Every company has this idealized vision of a data science and analytics team, with full visibility into how the business is doing, how the product gets used, how experiments are performing, super good looking and funny people, etc. The problem with getting there (and this is part of why data teams don’t get hired until later in the company lifecycle) is that the actual, cold hard data that you need to answer important questions typically lies all over the place. And it needs cleaning.
I’ve decided to illustrate this principle with a sadly very real set of examples from my experience: