Data Lineage?

Data lineage is the process of understanding, recording and visualizing dataflows through the organization, which includes where the data originated, the transformations it underwent along the way and its current destination at the end of the data pipeline. The data lineage information is collected from all the operational systems and touchpoints through which the data has passed.

The detailed documentation of the data lineage uses metadata (that is the data describing other data sets), to help users contextualize and discover the business meaning behind organizational data. The process uncovers and tracks the history of data as it was processed and how it achieved its present form. The task of data verification becomes easier and more efficient only if the entire life cycle is correctly tracked and documented.

Why is Data Lineage Important?

Dealing with multiple varied data sources that range in complexity and sizes requires several transformations that make it ready for further consumption. Identifying only the source of the data is not enough for understanding the changes that have occurred owing to processing, migration and system updates. The benefits of data lineage lie in its ability to expand visibility into the data journey and allow users to discover and resolve any anomalies.

Here are the reasons why businesses use data lineage –

1. Higher accuracy in data analytics results – For any particular data catalog entry, the data lineage system creates a graph visualization, letting the user know from where the data was sourced from and how it was transformed. With data lineage, the ability to allocate the right data for analytics improves. Since the data is valid and relevant, the insights thus generated will deliver meaningful and actionable information for strategic decision-making.
2. Enhanced regulatory compliance – Keeping in line with the organizational standards and regulations can be accomplished with the aid of data lineage. The metadata from the system can be an asset for compliance auditing and creating reports on the level of compliance.
3. Improved data governance – Tracking the movement of data helps data governance managers ensure that its maintenance follows company rules and regulations. The validity, quality, integrity and consistency of data can be guaranteed through proper lineage settings.

How Data Lineage Works?

Data lineage allows instant visibility into the data journey. Users can utilize this information to guide decision making. To leverage the full potential of the tool, it must offer connectivity across the data stack and automation in generating the lineage metadata.

Data lineage creates a framework for data mapping where the metadata is managed and collected from each step of data processing. This information is updated for each process and is stored in a metadata repository. Metadata gives users exhaustive information about their data, including format, structure, type, author, data of modification, details of the modifications and size of the file.

For an organization to construct and implement a data analysis system, lineage tools and data catalogs are necessary for data discovery exercises. These tools follow the evolution of data over time and the data catalog will produce an accessible and organized inventory of the data resources available. This helps trace the flow of all information in the data pipeline and discover the root cause behind any errors.

Data Lineage and its Future with Kyvos

Kyvos’ GenAI powered, high-speed semantic layer specializes in simplifying analytics on massive datasets. Kyvos’ ability to create data models with clearly defined domains using a collection of data products, can be discovered within the data catalogs. Therefore, the significance of data lineage documentation becomes clear and essential for data discovery.