Analytics engineering for small teams

It is rarely the thing that gets a project approved, and often the thing that decides how it goes. Most advice about analytics engineering assumes a team that does not exist at your size. Here is the version that does not.

Data outlives the applications built on top of it, which is why the model deserves more thought than the screens. It rarely shows up as a line item, which is exactly why it slips.

What to keep

Transformations belong in version control like any other code. There is a version of this that is over-engineered, and it is worth avoiding. Most teams find the first pass takes an afternoon and the maintenance takes minutes a month.

What to drop

Process that exists to coordinate ten people is overhead when there are two of you. The cost of getting this wrong is rarely visible on the day it happens.

What good looks like

One definition of a metric, used everywhere. In practice this is a scheduling problem more than a technical one. Most teams find the first pass takes an afternoon and the maintenance takes minutes a month.

In practice

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 you are not sure where your systems currently stand on this, it takes us about an hour to find out.