Data quality management

What do we mean by data quality management? 

Data quality management is the process of ensuring data quality. 

It enacts data governance policy to ensure that data meets defined quality standards and puts in place a systematic approach to identify, assess, monitor, and address data quality issues through the data lifecycle.

The aim of data quality management is to ensure that priority data is maintained to be accurate, complete, and reliable, so that it is trusted by all stakeholders.

Data quality management is a pre-requisite for data led transformation.

Knowledge 

• How to create and implement a data quality management framework. 
• The tools, technologies and techniques used to support effective data quality management. 
• Understanding how to engage stakeholders in data quality management.

Skills 

Able to:  

Create and implement a data quality management framework: Developing and implementing a structured framework that outlines the processes, roles, and responsibilities for managing data, linked to the organisation’s overall data governance approach. 

Define data quality metrics: Setting standards for the level of quality required for specific data objects and / or attributes. 

Collaborate with stakeholders: Ensuring stakeholder involvement in the definition and enactment of quality standards. 

Profile data: Analysing and assessing the quality of data to identify issues such as duplicates. 

Cleanse data: Correcting errors and inaccuracies, including duplication and standardisation to meet data quality thresholds. 

Standardise data: Establishing and enforcing standards for the format, structure, and representation of data to ensure consistency across different data sets. 

Enable data validation: Ensuring that data is validated after cleansing to assure its completeness and accuracy. 

Embed data quality monitoring on an ongoing basis: Setting up mechanisms to continually track data quality to enable trends to be identified and data quality to be maintained ongoing. 

Conduct root cause analysis: To understand the underlying causes of poor data quality or anomalies so that they can be corrected. 

Use data quality tools and technology: To automate and streamline the process of profiling, cleansing and standardising data.  

Train, coach, and support: Data owners and data stewards to understand their roles in managing data quality.   

Communicate effectively: With stakeholders at all levels, including technical and non-technical teams. 

Manage risk: Understanding the risks associated with data governance and quality, ensuring these are reflected in any project/programme and corporate risk registers and managed within risk appetite.