What does a data lineage diagram include and why is it valuable?

What does a data lineage diagram include and why is it valuable?

• A. It shows only the data dictionary contents.
• B. It shows sources, transformations, and targets; valuable for impact analysis, audits, compliance, and understanding data flows.
• C. It shows data models but not data flow.
• D. It shows only end data consumers.

Answer: (B)

Data lineage diagrams capture where data comes from (sources), what happens to it (transformations), and where it goes (targets). This full map of data flow reveals how data is produced and evolves as it moves through systems and processes, showing dependencies and provenance from origin to consumption. This makes it valuable for several reasons. It supports impact analysis: when a change is made to a source or a transformation, you can see which downstream reports, models, or dashboards will be affected. It aids audits and compliance by providing a clear record of data origins and the steps data undergoes, which helps demonstrate controls and traceability. It also helps with debugging and data quality: if a result looks wrong, you can trace it back through the lineage to identify where the issue started, understand data quality at each stage, and improve governance. Why the other ideas don’t fit as well: simply listing data dictionary contents covers definitions but not flow or provenance. Focusing only on data models shows structure, not how data moves and transforms. Showing only end data consumers omits where the data originated and what happened to it along the way, losing essential context about lineage.

Data lineage diagrams capture where data comes from (sources), what happens to it (transformations), and where it goes (targets). This full map of data flow reveals how data is produced and evolves as it moves through systems and processes, showing dependencies and provenance from origin to consumption.

This makes it valuable for several reasons. It supports impact analysis: when a change is made to a source or a transformation, you can see which downstream reports, models, or dashboards will be affected. It aids audits and compliance by providing a clear record of data origins and the steps data undergoes, which helps demonstrate controls and traceability. It also helps with debugging and data quality: if a result looks wrong, you can trace it back through the lineage to identify where the issue started, understand data quality at each stage, and improve governance.

Why the other ideas don’t fit as well: simply listing data dictionary contents covers definitions but not flow or provenance. Focusing only on data models shows structure, not how data moves and transforms. Showing only end data consumers omits where the data originated and what happened to it along the way, losing essential context about lineage.