14 Facts About Data lineage

Data lineage includes the data origin, what happens to it, and where it moves over time.

Data lineage gives visibility while greatly simplifying the ability to trace errors back to the root cause in a data analytics process.

Data lineage provenance refers to records of the inputs, entities, systems, and processes that influence data of interest, providing a historical record of the data and its origins.

Data lineage enables companies to trace sources of specific business data for the purposes of tracking errors, implementing changes in processes, and implementing system migrations to save significant amounts of time and resources, thereby tremendously improving BI efficiency.

Scope of the data lineage determines the volume of metadata required to represent its data lineage.

Usually, data governance, and data management determines the scope of the data lineage based on their regulations, enterprise data management strategy, data impact, reporting attributes, and critical data elements of the organization.

At a very high level data lineage provides what systems the data interacts before it reaches destination.

Data lineage governance plays a key role in metadata management for guidelines, strategies, policies, implementation.

Masking feature in the data lineage visualization allows the tools to incorporate all the enrichments that matter for the specific use case.

Data provenance or data lineage can be used to make the debugging of big data pipeline easier.

Data lineage provenance provides a historical record of the data and its origins.

Data lineage containment implies that the contained data is a subset of the containing data.

Concept of prescriptive data lineage combines both the logical model of how that data should flow with the actual lineage for that instance.

The captured lineage is combined and processed to obtain the data flow of the pipeline.