Data Lineage: What is it and what is it used for?
Data lineage tracks the origin and transformations of each field throughout the pipeline. Why it’s critical for governance, debugging, and compliance.
Have you ever heard of data lineage?
Data lineage is the process of tracking the flow of data over time, enabling a clear understanding of where data originated, how it changed, and its final destination within the data pipeline.
Trusted data is essential to drive better decision-making and process improvement across all business areas. However, this information is only valuable if stakeholders are confident in its accuracy, considering that insights are only as good as data quality.
Data lineage tools provide a detailed history of everything that happened to information throughout its lifecycle, including transformations during ETL and ELT processes, data migrations, system updates, errors, and much more.
Data tracking is critical to ensure quality control of the information consumed and used in the decision-making process, enabling consistency and accuracy validations. In addition, data lineage is a major step toward achieving data observability and agility in error resolution, since it is possible to review execution history to find the root cause of the problem.
These tools go hand in hand with data governance goals, with visibility serving as a source of confirmation of data efficiency and quality.
How does it work?
As we saw earlier, data lineage tools allow users to fully understand how data flows through the data pipeline. This happens through metadata.
Metadata is "data about data," which includes a variety of information about data assets, such as type, format, structure, author, creation date, modification date, and file size. Data lineage tools provide a complete view of metadata to guide users in determining which data is relevant for each objective.
In recent years, the way we store and use data has evolved with the rise of big data. Companies are investing more in data science to improve assertive decision-making and business outcomes. However, to build robust analytics, it is necessary to use data lineage tools and data catalogs to perform data mapping.
While data lineage tools show the evolution of data over time through metadata, a data catalog uses the same information to create a history that enables searches across all data assets in an organization. Together, they allow data professionals to understand the importance of different datasets for specific outcomes.