Data retention: a practical guide

This is one of those topics that looks small until it costs you something. This guide covers what data retention actually involves, where it usually goes wrong, and how to tell whether yours is in reasonable shape.

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

Keeping everything forever is a liability, not an asset. The cost of getting this wrong is rarely visible on the day it happens. The teams that stay on top of it are the ones who put it on a calendar rather than a wish list.

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. In practice this is a scheduling problem more than a technical one.

The practical version

Retention rules should be written down and enforced automatically. Getting it slightly wrong is survivable. Ignoring it entirely is not. It rarely shows up as a line item, which is exactly why it slips.

Numbers get quoted in meetings long after anyone remembers how they were calculated. The version that works in practice is usually less elaborate than the version described in the guides.

Deletion has to work across backups too. Small and consistent beats large and occasional here. It is the sort of thing that looks like polish right up until it costs you an enquiry.

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.

• Keeping everything forever is a liability, not an asset
• Retention rules should be written down and enforced automatically
• Deletion has to work across backups too
• 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

Warning signs

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. The cost of getting this wrong is rarely visible on the day it happens.

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. Pick the one that would hurt most if it failed, and start there.