Home › Cases

Why does a data quality strategy start with a baseline?

Because without one you cannot tell improvement from opinion. Ruud Kuil, a data management specialist with over sixteen years in governance and master data management, advises thinking big and starting small: awareness at board level, but a first measurement narrow enough to finish.

Ruud Kuil has more than sixteen years of experience in data governance and master data management, and has trained many professionals in the DAMA DMBOK framework. The conversation covers his view on data quality, examples from practice, and concrete advice for managers who want to start improving.

1. Think big, start small

Awareness at executive level matters, because without it nothing gets the room it needs. But the first step should be narrow enough to complete: one domain, one set of definitions, one measurement.

2. Measure before you improve

A baseline tells you where you stand per source and per definition. Without it, every later claim of improvement is an opinion, and the discussion moves to whose opinion counts.

3. Assign ownership

An owner per data item, named. Not a department, because a department cannot be asked a question.

4. Keep it visible

A measurement that is repeated is worth more than a thorough one that happens once. Improvement shows up in the trend, not in the first report.

Based on an episode of the Databewuster podcast, recorded in Dutch. The episode, the summary and the full transcript are on the podcast page. This English article describes what was discussed; it does not quote the guest directly, because the conversation was in Dutch.