How can you measure metadata quality?

1 Define metadata quality dimensions

Before you can measure metadata quality, you need to define what it means for your specific context and goals. Metadata quality dimensions are the criteria used to evaluate the metadata, such as completeness, accuracy, consistency, relevance, and timeliness. Additionally, you can add other dimensions that are relevant for your domain, like security, accessibility, or usability. All of these dimensions are important for understanding how much metadata is available and how correct and reliable it is. Furthermore, they can help assess how coherent the metadata is across different sources and systems and how useful it is for the intended users and purposes. Lastly, they can help determine how current the metadata is and if it reflects the data changes.

2 Establish metadata quality metrics

Once you have defined the metadata quality dimensions, you need to establish how to measure them. Metadata quality metrics are the indicators or measures that you use to quantify the metadata quality. For example, you can look at completeness, which is the percentage of data elements and attributes that have metadata, accuracy which is the percentage of metadata that is correct and verified, consistency which is the number of metadata conflicts or discrepancies across sources, systems, and standards, relevance which is the frequency or rate of metadata usage or queries, and timeliness which is the average or median time lag between data changes and metadata updates. Additionally, you can use other metrics such as scores, ratings, or rankings that are suitable for your data architecture.

3 Collect and analyze metadata quality data

After you have established the metadata quality metrics, you need to collect and analyze the metadata quality data. This data is used to calculate the metrics and can come from various sources, such as metadata repositories or catalogs, data dictionaries or glossaries, data lineage or traceability, data quality tools or platforms, and data users or stakeholders. Different methods and techniques can be employed to collect and analyze the metadata quality data, including surveys, interviews, audits, reviews, dashboards, and reports.

4 Implement metadata quality improvement actions

Once you have collected and analyzed the metadata quality data, it's time to implement metadata quality improvement actions. These are the steps or tasks taken to enhance the metadata quality, such as adding or updating missing or outdated metadata, correcting or validating inaccurate or unreliable metadata, resolving or harmonizing inconsistent or conflicting metadata, removing or archiving irrelevant or redundant metadata, and automating or streamlining metadata creation and maintenance. Additionally, standards, policies, and guidelines can also be used to improve your data architecture.

6 Repeat metadata quality measurement cycle

Finally, you need to repeat the metadata quality measurement cycle. This process involves continuously measuring, improving, and evaluating the metadata quality, and is not a one-time activity, but an ongoing practice. By repeating the cycle, you can monitor and track the metadata quality trends and changes, identify and address any issues or gaps, adapt and optimize the dimensions, metrics, data, actions, outcomes and feedback, align and integrate the metadata quality with data architecture goals and strategies, and foster a metadata quality culture. Additionally, other benefits such as learning, innovation or collaboration may be derived from repeating the cycle.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?