This content is part of the comprehensive Governance in Tableau documentation.
Data quality is a measure of data's fitness to serve its purpose in a given context—in this case, for making business decisions. The quality of data is determined by factors such as accuracy, completeness, reliability, relevance, and freshness. You likely already have processes in place to ensure data quality as it is ingested from source systems, and the more that is fixed in upstream processes, the less correction will be needed at the time of analysis. You should ensure data quality is consistent all the way through to consumption.
As you are planning, it is a good time to review existing upstream data quality checks because data will be available to a larger group of users under a self-service model. In addition, Tableau Prep Builder and Tableau Desktop are great tools for detecting data quality issues. By establishing a process to report data quality issues to the IT team or data steward, the data quality will become an integral part of building trust and confidence in the data.
With the Tableau Data Management Add-on and Tableau Catalog, you should communicate data quality issues to your users to increase visibility and trust in the data. When a problem exists, you can set a warning message on a data asset so that users of that data asset are aware of particular issues. For example, you might want to let users know that the data hasn't been refreshed in two weeks or that a data source has been deprecated. You can set one data quality warning per data asset, such as a data source, database, flow, or table. For more information, see Set a Data Quality Warning, including the following types: Warning, Deprecated, Stale Data, and Under Maintenance.
Note that you can set a data quality warning using REST API. For more information, see Add Data Quality Warning in the Tableau REST API Help.