When analytics engineering is worth the effort

Most teams know this matters. Fewer have decided who owns it. Analytics engineering is not free, and pretending otherwise leads to bad decisions.

Most data problems are ownership problems that turned into technical ones. The version that survives contact with a real deadline is the simple one.

When it is worth it

Transformations belong in version control like any other code. This is the sort of thing that compounds, quietly, in both directions. It is the sort of thing that looks like polish right up until it costs you an enquiry.

When it is not

If nothing downstream depends on it and nobody is complaining, it can wait. The reasoning matters more than the rule, because the rule has exceptions.

How to decide

One definition of a metric, used everywhere. The reasoning matters more than the rule, because the rule has exceptions. It is worth deciding this deliberately rather than inheriting whatever the last person set up.

In practice

Data outlives the applications built on top of it, which is why the model deserves more thought than the screens. 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.