A data warehouse: a practical guide

Teams tend to reach for this after something has already gone wrong. This guide covers what a data warehouse actually involves, where it usually goes wrong, and how to tell whether yours is in reasonable shape.

Numbers get quoted in meetings long after anyone remembers how they were calculated. Nothing below assumes a large team or a large budget — most of it is a decision somebody has to make and then write down.

Why it matters

Reporting queries and application queries want different shapes. Where this goes wrong is almost never a lack of knowledge. Write the reasoning down alongside the decision, because the reasoning is what changes first.

For most businesses the question is not whether this matters but how much of it is worth doing right now. That depends on what you are trying to achieve in the next few months, not on best practice in the abstract. There is a version of this that is over-engineered, and it is worth avoiding.

The practical version

Separating them stops reports taking the product down. The reasoning matters more than the rule, because the rule has exceptions. Doing this properly once is usually cheaper than doing it approximately three times.

Data outlives the applications built on top of it, which is why the model deserves more thought than the screens. The version that works in practice is usually less elaborate than the version described in the guides.

Start with the questions, not with the schema. 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.

A working checklist

If you want a quick read on where you stand, work through this. Anything you cannot answer confidently is where to start.

• Reporting queries and application queries want different shapes
• Separating them stops reports taking the product down
• Start with the questions, not with the schema
• Someone is named as the owner, not just assumed to be
• There is a date in the calendar to review it again
• The decision and the reasoning behind it are written down somewhere findable
• You could explain the current setup to a new hire in five minutes

What to watch for

The most common failure is not doing this badly. It is doing it once, during a launch, and never revisiting it. Circumstances move, the setup does not, and the gap widens quietly until something breaks or somebody notices the numbers.

• It was configured during a launch and has not been touched since
• Different people in the business believe different things are true about it
• There is no way to tell whether the last change helped or hurt
• The only person who understands it has left, or is about to

Most data problems are ownership problems that turned into technical ones. None of that requires a large budget, only a decision and someone to own it.

How we approach it

On our projects this gets handled during the build rather than added afterwards, because retrofitting it costs several times more than including it. We write down what was decided and why, so the next person to touch it is not guessing.

If you are working with someone else, the questions worth asking are simple: who owns this, how will we know it is working, and what happens when it needs to change?

Making it stick

Pick the single item from the checklist above that would cause the most trouble if it turned out to be wrong. Fix that one, confirm it worked, then move on. None of this needs a rewrite. Most of it is a morning's work once someone decides to do it.