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Glossary AI technique

Data quality

What is data quality?

Data quality is the degree to which your business data is correct, complete, current and consistent, and therefore usable for decisions and for AI.

Also known as data qualitydata hygienereliable data

Duplicate customers in the CRM, outdated addresses, fields filled in differently: it looks small, but every report and every AI application running on it inherits those errors.

AI makes data quality more important, because AI acts directly on the data. At the same time AI helps: it can flag duplicates and deviations and fill in missing data from other sources.

The foundation is that systems are connected and use the same data, so there is one source of truth. How to put that in place for AI is covered under the data foundation for AI. Read more about system integration.

Last updated: 20 October 2026

Relatedterms

Read on in the glossary

All terms
AI techniqueSingle source of truthA single source of truth means each piece of data is authoritative in one place, so people and systems work with the same, current version of customers, figures and agreements.AI techniqueMaster data management (MDM)Master data management is managing your core data, such as customers, products, suppliers and employees, so that it is the same, complete and current in every system.AI techniqueSystem integrationSystem integration is connecting software so that systems such as your CRM, accounting and ERP exchange data automatically instead of someone retyping it.AI techniqueBusiness intelligence (BI)Business intelligence is collecting, combining and visualising company data in reports and dashboards, so you can steer on figures.

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