Data Lineage: Documenting the Data Life Cycle
Modern data is dynamic. Whether used for analytics, AI or other business use cases, it evolves and transforms as it travels through an organization’s data ecosystem. Data lineage tracks these changes, documenting the origin and movement of data throughout its life cycle. In this article, we’ll explore what data lineage is and what sets it apart from other related processes. We’ll also delve into how data lineage helps organizations document their data flows, comply with industry and government regulations, and make their data more useful for analytics and reporting.
What is data lineage?
Data lineage is the process of documenting the data life cycle. It’s a set of practices that provides organizations with clear visibility into the origins of their data and how that data is transformed, aggregated and/or otherwise manipulated as it transits between systems and processes.
Data lineage enables businesses to ensure the data they rely on remains high-quality, accurate and consistent. Organizations track their data lineage using tools specifically designed for that purpose. For example, Snowflake customers can use Horizon, Snowflake’s built-in governance solution.
How data lineage differs from data governance and data provenance
Data lineage, data governance and data provenance each play an important role in ensuring business data remains usable for analytics, machine learning and other applications. Although closely related, each of these concepts occupies a distinct place within an organization’s data management and governance strategy.
Data governance is the broad umbrella under which data lineage and data provenance fall. Data governance encompasses a set of practices for ensuring the security, accuracy and availability of data.
Data provenance captures important details such as who created the data, when the data was created and what changes the data has undergone. The primary focus of data provenance is documenting the historical record of the data.
Data lineage focuses on recording the origins, evolution and movement of data with the data pipeline. It helps organizations understand data flows and dependencies.
Why is data lineage so important?
Data lineage is vital to data governance, providing organizations with the visibility and documentation required to truly understand their data flows. This process supplies critical context, making it a fundamental part of building and maintaining trustworthy data pipelines.
Assure data quality
Modern organizations rely on data gathered from a variety of sources to advance their strategic goals. Data lineage practices help businesses ensure this data is reliable, because troubleshooting data quality issues requires tracing the data back to its original source. Data lineage documentation helps organizations pinpoint what has gone wrong and why.
Reduces technical debt
When it comes to data governance, taking the fast and easy route typically increases the time and expense that will be required to adequately address an issue in the future. Data lineage practices help reduce technical debt by documenting essential information such as where the data originated, how and when it was changed, and where its final destination is.
Tracks changes in data over time
Data is not a static resource. As data is used, it is transformed and modified. Data lineage tools trace these changes as the data journeys through the organization’s data pipeline, and collect detailed information about data movement. Data lineage practices allow organizations to establish relationships between datasets and transformations, and analyze dependencies between data elements, processes and systems.
Streamlines data migration
Data lineage offers a detailed view of data movement and dependencies, which informs data migration. For example, when data is migrated from an on-prem server to the cloud or between clouds, understanding the location and life cycle of data sources reduces the chance for mistakes during the migration and helps ensure the data is ready for use in its new environment.
Improves regulatory compliance
With an end-to-end view of data lineage, organizations can more easily uncover issues and discrepancies within their data. This heightened visibility reduces security and compliance risks, helping organizations verify that sensitive data is stored and processed in accordance with internal policies and regulatory standards.
Track your data lineage with Snowflake Horizon
Horizon, Snowflake’s built-in governance solution, provides a unified set of compliance, security, privacy, interoperability and access capabilities in the Data Cloud.
With Snowflake Horizon, organizations can attain enhanced compliance through additional certifications, data quality monitoring and lineage. Snowflake’s new Data Lineage UI, now in private preview, gives customers a bird’s-eye view of the upstream and downstream lineage of objects—making it easy to see how downstream objects may be impacted by modifications that happen upstream.
Beyond data lineage, Horizon unlocks essential data governance capabilities without additional configurations or protocols. Advanced privacy policies and cross-cloud data sharing enable secure discovery and access to data and apps. Robust platform security and data security capabilities include authentication, encryption, continuous risk monitoring and protections, role-based access control, granular authorization policies and more.