What Is Data Lineage?
Data lineage is the process of tracking data as it moves through different systems and stages of its lifecycle. This includes tracking where data comes from, how it is transformed, where it goes, and who uses it. Data lineage is important for understanding the context and quality of data, as well as for maintaining compliance with regulatory requirements.
Data lineage can be represented visually, using diagrams or graphs, to show the flow of data between different systems and processes. This helps to identify where data is modified, where it is stored, and how it is used. It also helps to improve data management and identify data quality issues and data governance gaps. Data lineage can be tracked manually or with the help of specialized data management programs such as Dremio.
Why Is Data Lineage Important?
Data lineage is an important part of data governance and ensures regulatory compliance and data quality. By understanding the flow and history of data, organizations can ensure data is accurate, complete, and appropriate for its intended use. Data lineage is necessary for compliance with regulatory requirements, as data lineage can be used to demonstrate how data was obtained and how it has been used.
By providing a clear understanding of data ownership and accountability, data lineage allows companies to identify data governance gaps and data management issues. Additionally, data lineage can also help organizations to eliminate data silos, improve data integration, and promote data reuse across the organization. By having a clear understanding of the flow and history of data, organizations can make better-informed decisions and improve the overall quality of their data.
Data Lineage vs. Data Provenance
Data lineage and data provenance are closely related concepts, but there are some key differences. Data lineage is the process of recording the steps and locations through which data flows, from sources to consumption. It focuses on understanding how data is transformed, where it comes from, where it goes, and who uses it. On the other hand, data provenance is the process of tracking the origin and history of a particular piece of data. It focuses on understanding the origin, history, and ownership of a specific piece of data, including any changes or transformations it has undergone. While data lineage is more focused on the overall flow and management of data, data provenance is more focused on the specific history and origin of particular data points.
Data Lineage Techniques
Manual data lineage
Manual data lineage is the process of tracking data flow and history manually, by reviewing documentation, interviewing stakeholders, and analyzing data flow in the systems. This technique is time-consuming and prone to human error, but it provides a first-hand understanding of the data flow and history. It is most applicable for small organizations or when specific data lineage information is needed.
Automated data lineage
Automated data lineage is the process of tracking data flow and history using specialized software and tools. This technique can provide a more accurate and efficient way to track data lineage, by automatically collecting and analyzing data flow information from different systems as it occurs. Automated data lineage tools can be integrated with existing systems and provide a visual representation of the data flow and history.
Business process modeling
Business process modeling is a technique used to identify and model business processes that generate, transform, and use data. This technique creates a clear understanding of the data lineage from a business perspective. Business process modeling is also used to look for opportunities for data integration and reuse.
Data discovery
Data discovery is a technique used to locate data across different systems and stages of its lifecycle. Data discovery tools can automatically scan systems and data sources and can provide a visual representation of the data flow and history. This technique can help organizations to eliminate data silos, improve data integration, and promote data reuse.
