Data quality policy is a policy with regard to data quality.

DQP

The purpose of a data quality policy is giving direction to decisions and measures that affect data quality.

Characteristics and requirement of a data quality policy are:

Relationships with other elements are:

• Data quality policy is part of a data quality management system.
• Data quality policy provides a framework for setting data quality objectives (KPI’s).
• Data quality policy of a consumer can be input for a data suppliers.
• Stakeholder Analysis is considered while managing data quality policy.
• Data quality policy is input for the procedure to manage critical data elements.
• Data quality policy is guided by awareness of data quality.

A data quality policy contains the following subjects:

• Importance of data quality
• Purpose of the data quality policy
• Scope of the data quality policy
• Relationship with the strategic objectives of the organisation
• Applicable laws and regulations and company decisions
• Critical data and dimensions of data quality that have priority
• Policy statements about areas that contribute to data quality such as people, processes, IT, agreements, and documentation.
• Roles and responsibilities about data quality
• Procedure to monitor the policy
• Procedure to evaluate and adjust the policy

• Historical context
• Roadmap for implementation
• References
• Definitions of terms
• Document version
• Management that established the latest version of the policy

The next sections show fragments of some data quality policies.

It is vital that the systems used to collect, store, and calculate performance information:

• are accurate and
• any faults or errors are identified.

All systems need to be checked and tested regularly

It is acknowledged that on occasions, data quality issues such as errors or omissions will be identified. It is imperative that where this is the case, they are reported to the relevant Information Asset Owner, who will be responsible for identifying trends in data quality issues and taking steps to amend processes to reduce any margin for error.

All staff

• should be aware of the importance of good data quality and their own contribution to achieving it, and
• should receive appropriate training to data quality aspects of their work.

Teams should have comprehensive procedures in place for identifying and correcting data errors, such that information is accurate and reliable at time of use.

All data should be subject to internal and external audits if deemed necessary.

The CCG recognises that reliable information is fundamental in supporting the CCG to achieve its goals. The CCG recognises that all the decisions, whether clinical, managerial, or financial need to be based on information which is of the highest quality.

DAMA (2017). DAMA-DMBOK. Data Management Body of Knowledge. 2nd Edition. Technics PublicationsLlc. August 2017. Chapter 13.6.1 Data quality policy.

Data quality policy - Braintree District Council

Data quality policy - Imperial College London

Data quality policy - Isle of Wight NHS Trust

Data quality policy – Nothern Island Fire & Rescue Service

Data quality policy - Rackcdn.com

Data quality policy - WA HealthCCG Data quality policy 2017-18 - Cambridgeshire and …

How to create a data quality policy - Data quality pro. (2018, May 6). Data Quality Pro. https://www.dataqualitypro.com/blog/how-to-create-a-data-quality-policy

ISO 9000:2015.

ISO 9001:2015.