Reversal Data Lineage
Companies need an integrated view across the entire organization, providing a common way to govern and manage data. This view combines critical items of metadata – technical, business and operational – across the entire data supply chain, from sources to transformations and targets (reports and analytics applications). This holistic view is still largely heavy manual activity today.
According to Gartner, by end 2021, organizations will spend twice as much effort in managing metadata compared with 2018.
WHY REVERSAL DATA LINEAGE?
Why should a company use a Reversal Data Lineage solution?
WHAT IS REVERSAL DATA LINEAGE?
What is Reversal Data Lineage? How does it work?
WHERE REVERSAL DATA LINEAGE IS KEY?
Where is Reversal Data Lineage useful the most?
What we Do
Reversal Data Lineage
Company view on Data and Processes
As data related demands increase within enterprises, the data landscape becomes more complex and far-flung, impelling organizations to shift their data management practices from managing data to managing metadata.
The critical interdependencies between successful business processes and the use of information effectively demonstrate the need to understand how information flows through processes and how information is transformed and used to support those processes. Reversal Data Lineage reveals where data is coming from, where it is going, and what transformations are applied to it as it flows through multiple processes. It helps users understand the data life cycle. It is one of the most critical sources of information from a metadata management point of view.
From data-quality and data-governance perspectives, it is important to understand physical data lineage to ensure that existing business rules exist where expected, calculation rules and other transformations are correct, and system inputs and outputs are compatible.
Mapping data lineage across the process will shed light on where decision steps can be improved by actionable knowledge.
Data Lineage is defined as a kind of data life cycle where it is possible to view data origin, relationships and transformations over time.
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