[AG.DLM.8] Improve traceability with data provenance tracking
Category: RECOMMENDED
Data provenance tracking records the history of data throughout its lifecycle—its origins, how and when it was processed, and who was responsible for those processes. This practice forms a vital part of ensuring data integrity, reliability, and traceability, providing a clear record of the data's journey from its source to its final form.
The process involves capturing, logging, and storing metadata that provides valuable insights into the lineage of the data. Key aspects of metadata include the data's source, any transformations it underwent (such as aggregation, filtering, or enrichment), the flow of data across systems and services (movements), and actors (the systems or individuals interacting with the data).
Use automated tools and processes to manage data provenance by automatically capturing and logging metadata, and make it easily accessible and queryable for review and auditing purposes. For instance, data cataloging tools can manage data assets and their provenance information effectively, providing a systematic way to handle large volumes of data and their metadata across different stages of the development lifecycle.
In more complex use cases, machine learning (ML) algorithms can be used to uncover hidden patterns and dependencies among data entities and operations. This technique can reveal insights that might not be easily detectable with traditional methods.
Regularly review and update the data provenance tracking process to keep it aligned with evolving data practices, business requirements, and to maintain regulatory compliance. Provide training and resources to teams, helping them understand the importance and practical use of data provenance information.
Data provenance tracking is particularly recommended for datasets dealing with sensitive, regulated data or complex data processing workflows. It also adds significant value in environments where reproducibility and traceability of data operations are required, such as in data-driven decision-making, machine learning model development, and debugging data issues.
Data provenance tracking is particularly recommended for datasets dealing with sensitive or regulated data, machine learning workflows, and complex data processing which may require debugging.
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