Before you invest in analytics engineering

The advice here is unglamorous, which is probably why it gets skipped. Before you spend anything on analytics engineering, it is worth confirming a few things are already true.

Data outlives the applications built on top of it, which is why the model deserves more thought than the screens. The teams that stay on top of it are the ones who put it on a calendar rather than a wish list.

Prerequisites

• You can describe the outcome you want in one sentence
• Someone owns it after the work is done
• Transformations belong in version control like any other code
• You have a way to tell whether it worked

The mistakes we see most

Tested, documented models stop every report disagreeing. That sounds obvious written down. It is still the thing most often skipped. Assume whoever inherits this will have half your context and none of your patience.

One definition of a metric, used everywhere. Where this goes wrong is almost never a lack of knowledge. It is worth deciding this deliberately rather than inheriting whatever the last person set up.

In practice

Numbers get quoted in meetings long after anyone remembers how they were calculated. 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

None of this needs a rewrite. Most of it is a morning's work once someone decides to do it.