Data audit: the phase everyone overlooks

Everyone wants to start building. The audit, by contrast, is anything but spectacular: no demo, no visible deliverable, just the patient inventory of what actually exists. Yet it is the phase that determines everything else.

What an audit should really produce

A good audit does more than list tables. It answers one simple question: what is genuinely migratable, and at what risk?

In practice, that means:

1. extracting and analyzing the real data, not a convenient sample;
2. measuring actual volumes, not the figures that were announced;
3. identifying inconsistencies, duplicates and edge cases;
4. understanding the functional dependencies between objects.

Volume estimates almost always lie

A volume estimate provided at the start of a project is, in the vast majority of cases, underestimated. History, intermediate revisions, orphaned objects and accumulated exceptions inflate the real scope far beyond what was anticipated.

Measuring early means avoiding a nasty surprise at the worst possible moment.

Auditing already reduces risk

Every anomaly spotted during the audit is an anomaly that will not derail production. The audit doesn’t solve the problems, but it makes them visible early enough to be dealt with.

That is exactly the spirit of the approach: work from real data, not assumptions. The rest of the migration depends on it.