It is recommended to conduct DAM Ways of Working health checks once your DAM is up and running. The frequency of these health checks will depend on the pace of engagement from your DAM users. Good data is dependent on good processes, so validate that these are working to expectations.

• User feedback and root cause analysis: Are data imports accurate and relevant? Talk to the people who are managing the data in the DAM. Are they importing master data from another source? Is the data accurate? Work with them to identify the root cause and help them correct it.
• Is metadata being applied to the right assets in the right sequence? Is anyone quality-checking the metadata? Be sure that users are up to speed on DAM training and are informed of the requirements for uploading assets to the DAM. Governance and accountability: Make data quality assurance a must-have.
• Building a governance plan for data can take many forms, but it is essential in setting up parameters for quality assurance. Data governance might include a governance body, or it might take the form of data requirements that must be complied with before proceeding to the next workflow task. Data maturity relies on good governance and standards.