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A baseline makes Step 1 measurable. Without it, progress becomes a matter of opinion, and analytics work turns into a debate about who is “doing enough” instead of a discussion about what is improving.
The baseline should be lightweight. You are not creating a comprehensive inventory. You are capturing enough facts to align stakeholders and make change visible.
A baseline is just a short description of “what’s true right now” so you can tell, in a few weeks, whether anything actually improved.
Keep it small. If it turns into an inventory project, it stops being useful.
Get a rough sense of the analytics surface area: how many dashboards and reports exist, which tools are in play, and which metrics leadership actually cares about. You do not need precision. You need a shared picture.
Write down where decisions actually happen. Name the recurring leadership forums, and note which dashboards or metrics show up there. This quickly exposes “busy output” that never makes it into decisions.
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List the recurring issues that make people stop believing the numbers. Keep it concrete: common data quality problems, repeated definition disputes, and the workarounds people use when they do not trust the system.
When possible, capture one example of “how we could prove it” for the top disputed metrics (for example, an evidence view or underlying record set that lets people validate the headline number).