Reduce Complexity. Gain Business Value.
The importance of Data Lineage
Data Lineage describes the route data takes and transformations it undergoes as it travels through systems inside and outside your organization. Using data lineage, an informative view can be established that traces an attribute’s journey all the way upstream to the system of origin, and all the way downstream to all the systems consuming or passing the attribute along.
This is beneficial for integration architects, data consumers and data scientists. Looking upstream, it’s easy to identify which systems could be responsible for data quality issues, even if those systems are separated by several orders of integration. Similarly, the downstream view provides invaluable insight into which systems might be affected by a proposed interface upgrade on an external business partner’s system, for example.
Ask yourself how lineage could help with compliance?
"This will improve our turnaround time on new integrations so much. Usually, it takes weeks to do an impact analysis on affected systems, even for simple changes.".
How do we manage them?
Electric Utility Company
SOMETHING TO CONSIDER
3 steps to Data Lineage
The data lineage is only as good as the metadata associated within Affirma. To help gain the most value out of the data lineage ability we recommend the following key steps.
Import existing data mappings or create new ones
Data Lineage is dependent on Data Mappings. Importing or building some data mappings is a prerequisite to the Data Lineage capabilities. You are not required to manually capture additional information outside of Data Mappings for this.
Select a model to start tracing from
Affirma traces both upstream and downstream from a selected model. The first order mappings to and from the selected model and the source/target models are displayed alongside the selected model in a navigable diagram.
Expand your horizon!
You can navigate upstream or downstream from the selected model, and you can keep on navigating in any direction if there are more mappings associated with the selected data model.