The real cost of ignoring analytics engineering
This is cheap to get right at the start and expensive to retrofit. Nobody bills you for neglecting analytics engineering. The cost shows up somewhere else.
Data outlives the applications built on top of it, which is why the model deserves more thought than the screens. The version that survives contact with a real deadline is the simple one.
Where the cost lands
- Time spent on work that should not have been necessary
- Enquiries that quietly never arrive
- Transformations belong in version control like any other code
- Rework, once the problem is finally visible
Tested, documented models stop every report disagreeing. That sounds obvious written down. It is still the thing most often skipped. Write the reasoning down alongside the decision, because the reasoning is what changes first.
Where to go from here
One definition of a metric, used everywhere. It is worth being explicit about, because assumptions differ quietly. If it only works because one person remembers to do something, it does not work yet.
How to tell if yours is fine
Most data problems are ownership problems that turned into technical ones. Three things worth confirming about analytics engineering before you move on:
- Someone can say what the current setup is without going to look
- Tested, documented models stop every report disagreeing — and you know whether that is true here
- There is a way to tell whether the last change to this helped
Pick the one that would hurt most if it failed, and start there.