Cross-System Lineage

• Upstream & Downstream Impact Analysis: Initiate with a comprehensive evaluation of both upstream data sources and downstream consumer systems. Understand the ripple effects of changes in data structures, schemas, or values across the entire data flow.
• Technology Flow Mapping: Recognize and document every technology component within the data flow, from ETL tools, middleware, data lakes, ML applications and models, to BI platforms. This ensures a clear understanding of how data modifications in one system propagate and potentially impact other interconnected systems.

Inner System Lineage

• Entity-Relationship Exploration: Delve deep into individual systems, dissecting the relational models, table structures, stored procedures, and scripts that govern data behavior and storage.
• Data Transformation Lifecycle: Examine the transformation logic, data cleansing routines, and business rules applied at granular levels, providing insights into data quality, lineage, and provenance.

End-to-End Asset Lineage

• Data Provenance Tracking: Trace the lifecycle of specific data entities, from source extraction, through transformation logic, to final presentation layers, capturing every transformation touchpoint.
• Holistic Data Lineage Visualization: This representation offers a detailed map of a data element's journey, highlighting dependencies, transformations, and potential bottlenecks, crucial for optimization, root cause analysis, and ensuring regulatory compliance.