Data quality
Data quality, in the data protection sense, refers to the set of requirements in Art. 5(1) GDPR that govern the substance of personal data rather than the legitimacy of processing them: data must be accurate and kept up to date, adequate, relevant and limited to what is necessary (data minimisation), and kept no longer than necessary (storage limitation). The term originates in Directive 95/46/EC and the OECD Guidelines and is mirrored in Art. 4 of Regulation (EU) 2018/1725 for EU institutions.
In life sciences, data protection quality requirements coexist with the data integrity expectations of GCP and of the EMA and FDA, summarised by the ALCOA+ acronym: attributable, legible, contemporaneous, original, accurate, complete, consistent, enduring and available. Both frameworks converge on audit trails, controlled corrections, validated systems and clear ownership of data.
Practical data quality controls in a trial include edit checks in the eCRF, source data verification, query management, reconciliation between safety and clinical databases, and database lock procedures. From the data subject's side, quality is enforced through the right of rectification, which in a research context is exercised through documented corrections rather than by overwriting original records.
