What is Data Lineage?

What is Cross-Systems Lineage?

Cross-systems lineage refers to the process of tracing and visualizing data's lifecycle across multiple systems and platforms within an organization. It allows you to see where data originates, how it changes and is used, and where it ultimately ends up.

Understanding cross-systems lineage can provide several benefits:

• Improved Decision Making: By providing a clear view of data sources, transformations, and usage, decision-makers can have increased confidence in their data-driven insights. It validates that the data used in analysis and decision-making processes is accurate, trustworthy, and reliable.
• Risk Management and Compliance: For regulated industries, understanding data lineage can be crucial for compliance. Cross-systems lineage can demonstrate to regulators that data has been handled correctly. Furthermore, it can help manage risk by identifying where sensitive data resides and ensuring appropriate security measures are in place.
• Data Quality: Cross-systems lineage helps identify data quality issues. By tracking data from its source, through transformations, and to its endpoint, inconsistencies, errors, or anomalies can be traced back to their origin for resolution.
• System Migration or Consolidation: When merging systems or migrating data from one system to another, understanding the lineage can help identify potential issues, dependencies, or impacts to downstream systems or processes.
• Operational Efficiency: Understanding cross-systems lineage can increase operational efficiency by eliminating redundant processes and identifying areas for automation or optimization.

Tips and Tricks
Remember, the goal is to create a complete, end-to-end picture of your data's journey through your organization's systems. By doing so, you can ensure data trustworthiness and integrity, improve compliance, and enhance operational efficiency.

What is Inner-System Lineage?

Inner-system lineage refers to the tracking and visualization of data as it moves and transforms within a single system or platform. It provides a detailed understanding of data origin, transformations, and usage, but within the boundaries of one system.

In contrast to cross-systems lineage, which is about understanding data across multiple systems, inner-systems lineage is more focused on a single system's data journey. Both are essential components of comprehensive data governance, but their use cases differ. While inner-systems lineage is ideal for system-specific data quality, efficiency, and security considerations, cross-systems lineage is beneficial for broader, organization-wide views of data flow, particularly in understanding dependencies and impacts across systems.

Here are a few specific benefits and implementations of inner-systems lineage:

• Understanding Data Flow: Inner-systems lineage provides a clear understanding of how data is created, transformed, and consumed within a specific system. This is particularly useful in complex environments where data undergoes numerous transformations or is used by multiple applications within the system.
• Improving Data Quality: If a data quality issue arises, inner-systems lineage allows you to trace the problem back to its source within the system. This can be instrumental in correcting data errors and improving overall data quality.
• Streamlining System-Specific Processes: By mapping out the data's journey within a system, organizations can identify inefficiencies or bottlenecks in their processes. This can lead to better system-specific performance and efficiency.
• Safeguarding Sensitive Data: Within a system, sensitive data may be transformed or moved. Understanding the lineage of this data helps ensure that it is handled appropriately within that system, mitigating potential security risks.
• System Enhancements and Migrations: When updating system features or migrating to a new version, understanding the data lineage can help identify potential impacts or dependencies.

What is End to End Column Lineage (E2E)?

End-to-end column lineage involves tracking the life cycle of a specific data column or attribute from its origin, through all transformations, to its final form. This type of lineage gives a granular view of data handling and movement in your organization. It helps you understand how a specific data element changes, the dependencies it has, and the impact it may create throughout its life cycle.

Here's how end-to-end column lineage provides value:

• Data Provenance: It helps understand the complete history of a data element. This includes the source system, any transformations or processing it has undergone, and where it's used in downstream systems and reports.
• Data Quality Assurance: If a data quality issue is identified in a column, tracing its lineage can help find the source of the issue. This might include identifying transformation errors, incorrect data mappings, or source system issues.
• Change Impact Analysis: If a change is planned in the source system or a transformation process, tracing the column lineage helps identify all the downstream systems, processes, or reports that might be impacted. This can help mitigate risks associated with system changes.
• Regulatory Compliance: In regulated industries, it's often necessary to demonstrate where specific data comes from and how it's transformed. Detailed column lineage can provide this information for audit or compliance purposes.
• Data Trust: For end users, understanding the lineage of a data column can increase trust in the data. If users can see where the data comes from and how it's handled, they may have more confidence in using it for decision-making.