How do you define and measure data quality in your projects?

3 How to document data quality and metadata?

Different methods and tools are available for documenting data quality and metadata, depending on the scope, complexity, and nature of the data and the project. For example, data quality assessment is the process of evaluating the data quality dimensions and identifying the data quality issues and gaps. Techniques such as data profiling, data cleansing, data validation, and data auditing can be used to perform data quality assessment. Data quality reports, dashboards, or scorecards can be used to document the data quality assessment results, which show the data quality indicators, metrics, and targets, as well as the data quality issues, root causes, and impacts. Additionally, a data quality plan outlines the data quality objectives, standards, and criteria, as well as the data quality improvement actions, roles, and responsibilities. A data quality plan template, such as the one provided by the Data Governance Institute, can be used to create the plan. A data quality matrix, such as the one proposed by the DAMA International, can be used to define the data quality dimensions, measures, and thresholds for each data element or attribute. Furthermore, a data dictionary provides the metadata for the data, such as the data name, definition, type, format, length, domain, source, owner, and usage. A data dictionary template, such as the one provided by the Data Management Association of Australia, can be used to create the dictionary. Additionally, a data modeling tool, such as ERwin or PowerDesigner, can be used to create a data model that shows the metadata and the relationships of the data entities and attributes.

5 How to use data quality and metadata documentation?

Using data quality and metadata documentation is essential for enhancing the value and usability of the data and the insights derived from it. You can use the data quality assessment results and the data quality plan to identify and prioritize data quality improvement actions, such as data cleansing, data enrichment, data standardization, and data integration. Additionally, data quality indicators and metrics can be used to monitor and measure the data quality improvement progress and outcomes. Furthermore, the data dictionary and data model can be used to understand and interpret the data, as well as to select and manipulate the data for analysis and reporting purposes. Finally, data quality reports, dashboards, or scorecards can be used to communicate the data quality status and issues to stakeholders and users, and to support data-driven decision making.