Data quality
Also called data integrity, data accuracy
Data quality is the degree to which a firm's data is accurate, complete, timely, consistent and fit for its purpose. In regulated firms it decides whether risk reports, regulatory returns and screening results can be trusted. Supervisors treat poor data quality as a governance and control failure.
How it works
Firms define data owners and measurable quality rules for critical data, such as completeness of counterparty identifiers or accuracy of trade dates. Automated checks run as data moves from source systems to reports. Breaks are logged, fixed at source and tracked in dashboards. Data lineage shows where each reported figure came from, so errors can be traced and corrected.
Data quality matters because nearly every compliance task depends on it. Inaccurate customer data weakens KYC and sanctions screening. Incomplete trade data leads to transaction reporting breaches. The ECB has named risk data aggregation as a supervisory priority, and regulators have fined firms for years of misreported data. Fixing quality at source costs less than repeated manual corrections.
The rules
Banks must aggregate risk data accurately, completely and in a timely way, with strong data governance and architecture.
Globally systemically important banks, extended by many supervisors to domestic banksSets supervisory expectations on board accountability, data lineage, data quality management and timely risk reporting.
Banks directly supervised by the ECBRequires reporting in ISO 20022 XML, more data fields and procedures to ensure reported data is accurate and to notify errors to the regulator.
Counterparties reporting derivatives to EU trade repositoriesCommon questions
What are the main dimensions of data quality?
Common dimensions are accuracy, completeness, timeliness, consistency, validity and uniqueness. BCBS 239 focuses on accuracy, completeness, timeliness and adaptability of risk data. Firms set measurable thresholds for each dimension on their most critical data.
How does data quality link to BCBS 239?
BCBS 239 sets 14 principles on risk data governance, aggregation and reporting. Several deal directly with data quality, requiring accurate and complete data that can be reconciled. Supervisors assess banks against the principles and many banks still fall short.
Who is responsible for data quality in a bank?
Data owners in the business are responsible for the data they create. A chief data officer usually sets standards and monitors quality. Under the ECB guide, the management body is accountable for risk data aggregation and reporting.
How is data quality measured?
Firms run automated rules against data, for example checking that mandatory fields are filled, values are in range and records reconcile between systems. Results are shown as pass rates and break counts, with trends reported to management.
Our coverage
News and analysis on regulatory reporting from RegTech Post.
Read the coverage →RegTech PostData Integrity: The Invisible Engine Powering RegTech ComplianceRead the article →RegTech PostWhen Data Stays in Silos, Risk Rises: Lessons from Real CasesRead the article →
