What are the key data lineage tracking practices for metadata management?

1 Define your data lineage scope and granularity

One of the first steps in data lineage tracking is to define your scope and granularity. This means deciding what data sources, processes, and outputs you want to track and how detailed you want your lineage information to be. For example, you may want to track the lineage of a specific data set, a business function, or a regulatory requirement. You may also want to track the lineage at the column, table, or database level, depending on your needs and resources. Defining your scope and granularity will help you focus your data lineage tracking efforts and avoid unnecessary complexity and overhead.

2 Use automated tools and standards

Another key practice for data lineage tracking is to use automated tools and standards that can capture, document, and visualize your data lineage. Manual data lineage tracking can be time-consuming, error-prone, and inconsistent, especially in large and heterogeneous data environments. Automated tools and standards can help you streamline and standardize your data lineage tracking process and ensure accuracy and completeness. For example, you can use data cataloging tools that can scan and extract metadata from various data sources and generate data lineage diagrams. You can also use metadata standards, such as ISO 11179 or Dublin Core, that can define common attributes and formats for your metadata.

3 Integrate data lineage with other metadata elements

A third key practice for data lineage tracking is to integrate data lineage with other metadata elements, such as data definitions, data quality rules, data owners, and data policies. Data lineage is not an isolated piece of information, but rather a part of a larger metadata context that can provide more value and insight for your data governance. By integrating data lineage with other metadata elements, you can enrich your data understanding and enable better data discovery, analysis, and usage. For example, you can link your data lineage with your data quality rules to monitor and improve the quality of your data throughout its lifecycle. You can also link your data lineage with your data owners and policies to ensure data accountability and compliance.

4 Update and validate your data lineage regularly

A fourth key practice for data lineage tracking is to update and validate your data lineage regularly. Data lineage is not a static snapshot, but rather a dynamic and evolving representation of your data assets. As your data sources, processes, and outputs change over time, so should your data lineage. Updating and validating your data lineage regularly will help you maintain its relevance and accuracy and avoid data inconsistencies and issues. For example, you can use automated tools that can detect and notify you of any changes in your data sources or processes and update your data lineage accordingly. You can also use validation techniques, such as data profiling or testing, to verify that your data lineage matches your actual data flows and outcomes.

5 Communicate and share your data lineage

A fifth key practice for data lineage tracking is to communicate and share your data lineage with your data stakeholders. Data lineage is not only a technical artifact, but also a business asset that can inform and support your data-driven decisions and actions. Communicating and sharing your data lineage with your data stakeholders will help you foster data trust, collaboration, and value creation. For example, you can use data lineage to explain the origin and logic of your data reports and dashboards to your data consumers. You can also use data lineage to share the impact and dependencies of your data projects and initiatives with your data producers and managers.

6 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?