A typical workflow will show data often lead to analytics, which in turn lead into decision-making.
While that might make sense when data are what we have on-hand, and we ask ourselves… What can we do with the data?
Perhaps it is more instructive to look at the workflow in reverse.
The more critical, first question to ask is… What decisions are we trying to make?
That question leads to… What analysis or analytics output do we need to support the decision-make?
Which then leads to… What data do we have on-hand that can support the analytics? If not, where can we go to find the data we need for the analytics?
Data science experts encourage us to ask questions first before doing the analytics.
The important question is always what is the decision this for?