Data quality: complete definition in AI for SMEs

Data quality is the degree of reliability, completeness, accuracy and freshness of a company's data. It is the factor that determines AI output quality: a model trained or fed with bad data will produce bad results, whatever its sophistication. Quality is measured on several axes: completeness (missing fields), accuracy (wrong values), uniqueness (duplicates), consistency (formats), freshness (recency).

What it changes for an SME

Data quality is the breaking point between a successful AI project and a failing one:

• a RAG fed with outdated procedures produces false answers — employees abandon the tool;
• a prospecting agent relying on a CRM full of duplicates sends duplicate emails;
• a forecasting model trained on poorly entered sales predicts nonexistent trends.

Best practice

Data quality is not a one-off project — it is a continuous discipline: automatic checks at every ingestion, quality indicators tracked monthly, accountable owners for each dataset. In fractional AI leadership, we start with a data quality diagnosis before any AI project.