Data quality

Poor data quality is one of the most underestimated enterprise risks. Inaccurate customer records lead to bad personalization. Incomplete consent data leads to compliance violations. Duplicate records inflate audience size and corrupt analysis. Stale data leads AI systems to make decisions based on information that no longer reflects reality.

Data quality is typically assessed across five dimensions:

• Accuracy: does the data reflect reality?
• Completeness: are all required fields populated?
• Consistency: does the same data point agree across systems?
• Timeliness: is the data current enough for the use case?
• Uniqueness: are there duplicate records that misrepresent the data population?

For AI specifically, data quality is the primary determinant of model reliability. Models trained on low-quality data amplify errors at scale, producing outputs that appear confident but are systematically wrong. For compliance, inaccurate personal data creates exposure under the GDPR's accuracy principle and gives individuals valid grounds to exercise correction rights.