Data Lineage
Data lineage provides a comprehensive view of data’s journey, tracing its origin and documenting its movements from creation and ingestion to transformation, reporting, and beyond.
What is Data Lineage?
Data lineage is a record of data’s lifecycle, from where it originated and to how it may have changed along the way, reflecting the data source, processes that systems that may have altered it, storage locations, and everything that occurred before it arrived at its current location and state.
Data lineage provides valuable information for data consumers, enabling them to understand the data in question, if it is suitable for their use cases, and who might have been involved with altering or moving the data during its lifecycle. Understanding how data has changed—maybe it was summarized, filtered, or had new data added or deleted—increases trust and confidence. If errors or questions arise, data lineage provides an avenue for better understanding of the data in question. It can also highlight areas where data processes can be improved, accelerated, or eliminated.
How Data Lineage Works
Data lineage is the tracking and documenting of data’s path through an organization and its systems. It typically includes:
Data source identification to pinpoint data origins like databases, data warehouses, reports, or dashboards.
Data movement mapping to track how data flows from source to destination and detailing any transformations, conversions, or cleansings along the way.
Dependency tracking to show how datasets or reports rely on other data sources, which can show how changes in one dataset impact others.
Data quality monitoring that adds data quality metrics to evaluate data’s reliability at each stage of its lifecycle and to identify inconsistencies.
Timestamps and versioning for a historical look at changes and a basis for auditing and compliance.
Data lineage applications typically include additional capabilities to visualize data lifecycles with maps, diagrams, and flowcharts. Solutions for data lineage, such as a data catalog, use metadata to uncover data’s structure, definitions, context, relevance, and related policies and procedures to enforce and highlight data governance and compliance rules. Leading solutions also add operational capabilities to identify redundancies, highlight bottlenecks, and surface potential process improvements.
Why Data Lineage is Important
Data lineage is fundamental to data intelligence, governance, and effectiveness. By providing a deeper understanding of the data lifecycle, data consumers can make better decisions about which data to use for different purposes and increase the value of data-driven decisions.
The key benefits of data lineage include:
Enhancing data consistency by highlighting changes over time and ensuring data consumers understand how those changes may impact the data’s value.
Improving data-driven decision-making through deeper insights into data lifecycles that further guide choices of which data to use and how to interpret the results.
Streamlining data migrations by eliminating bad or misunderstood data from migrations, tracking dependencies so data is not overlooked, and increasing efficiency of data migration efforts.
Strengthening data governance through faster, more accurate audits and easier identification of related risks.
Optimizing IT infrastructure and processes to remove redundancies based on usage patterns and data flows.
A Data Lineage Framework
To begin building a data lineage practice, a common framework is the “4Ls of Data Lineage,” used to catalog data and empower data teams via data lineage. These four pillars are:
Table-level lineage that monitors data through cleansing, transformation, and aggregation stages.
Column-level lineage that shows how individual data columns are transformed between systems, which can be crucial for troubleshooting and compliance.
Report-level lineage that provides visibility into how data flows into dashboards and reports, especially related to regulatory compliance.
Cross-system lineage for an end-to-end view of data movements across an organization’s infrastructure.
A data catalog is typically deployed to support this framework, thus allowing stakeholders to validate data and optimize data lifecycles.
Data Lineage Best Practices
With data and data-driven decision-making being so crucial to organizations today, data lineage can improve outcomes by increasing confidence and trust in the data. Here are a few data lineage best practices to increase the value of data lineage efforts:
Work closely with data consumers and business stakeholders to better understand how data is used, which data is relevant, and how data lineage insights can help build a data culture.
Document business and technical data lineage to show how data flows at organizational and technical levels to improve lineage’s value to the entire organization.
Align data lineage efforts with specific requirements such as improving decision-making, increasing data quality, and maximizing data management efficiency.
Include the entire enterprise in data lineage conversations to inform data governance and explore how data lineage can add value to other data-focused initiatives.
Use a data intelligence system that includes data lineage capabilities so data consumers have detailed insights to guide data discovery and usage.
Alation Ease Data Lineage Efforts
Alation makes it easy to provide end-to-end lineage, enabling stakeholders to understand data flows, relationships, health, and impacts across the data lifecycle.
Key features include:
Visualizations for data relationships at a business level and in detail to improve governance and highlight process issues.
Data maps to increase transparency, identify duplicate data, and guide users to compliant, trusted data.
Insights to eliminate waste, accelerate cloud migrations, and quickly identify the root cause of issues.
Next steps: Learn more about self-service analytics
Dive deeper into self-service analytics and why a data catalog is the best solution by using the following resources: