Five mistakes teams make with analytics engineering
The gap between knowing this and actually doing it is where most teams lose ground. These are the ones we run into repeatedly when we audit analytics engineering.
Numbers get quoted in meetings long after anyone remembers how they were calculated. Most teams find the first pass takes an afternoon and the maintenance takes minutes a month.
What to watch for
- Treating it as a launch task rather than an ongoing one
- Assuming someone else already owns it
- Transformations belong in version control like any other code
- Tested, documented models stop every report disagreeing
- Never checking whether the fix actually worked
One definition of a metric, used everywhere. This is the sort of thing that compounds, quietly, in both directions. Anything you cannot measure here, you are deciding by taste, which is fine as long as everyone knows it.
Making it stick
What this looks like day to day
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
If any of that sounds like a description of your current setup, it is fixable.