Data Quality Policy Writers

What are Data Quality Policies?

Data quality policies outline how organisations ensure that information is accurate, complete, consistent, timely and reliable.

High-quality data is essential for effective decision-making, regulatory compliance and operational efficiency. A clear policy helps employees understand their responsibilities in maintaining data integrity and provides standards for managing information throughout its lifecycle.

What Do Data Quality Policies Cover?

A data quality policy typically includes:

• Definitions of key data quality principles such as accuracy, completeness, consistency and timeliness

• Roles and responsibilities of staff, managers and data owners

• Standards for collecting, recording and verifying data

• Procedures for regular monitoring, validation and cleansing of information

• Controls to prevent duplication, errors or unauthorised changes

• Requirements for documenting data sources and processes

• Processes for reporting and correcting data quality issues

• Links to information governance, data protection, records management and IT policies

A clear policy helps ensure that data is managed in a consistent and structured way, reducing the risk of errors and improving confidence in reporting and analysis.

It also supports compliance with UK GDPR, the Data Protection Act 2018 and industry standards that require accurate and reliable data handling.

By embedding data quality principles across the organisation, businesses can improve efficiency, strengthen compliance and make better-informed decisions. High-quality data builds trust with customers, regulators and stakeholders, while supporting long-term success.

Legal Basis and Standards

Data quality is a statutory data protection principle under UK GDPR Article 5(1)(d) (accuracy).

It is also a core requirement of ISO 8000 (Data Quality), DAMA-DMBOK governance frameworks, and sector-specific regimes including the FCA's data-quality expectations under SUP 16, the ONS Code of Practice for Statistics for public-sector data, and the NHS Data Quality Maturity Index.

Common Compliance Pitfalls

• No defined accountability for the quality of specific data domains (no "data owners").
• Subject access requests revealing inaccurate or out-of-date personal data, triggering Article 16 rectification obligations.
• Migration projects that do not include data-quality acceptance criteria.
• Master data conflicts between systems that nobody is mandated to resolve.
• No documented quality measurement (completeness, accuracy, timeliness, consistency).

What Policy Pros Delivers

Our Data Quality Policy package includes the main policy aligned to UK GDPR Article 5(1)(d), ISO 8000 and DAMA-DMBOK, a data domain ownership matrix, quality dimensions and KPI definitions, a master data conflict resolution procedure, a migration acceptance-criteria template, and integration with the data protection, information governance and master data management policies.