The whole process of ingesting metadata, cataloging, data lineage visualization, impact analysis, and similarity analysis in an automated workflow within EIIG.
With EIIG, data citizens can perform similarity analysis to easily identify duplicate and redundant information just by clicking data points in a knowledge graph. This is automated self-service without any need to involve IT or a data governance specialist.
What is more, EIIG equips users with the flexibility to choose the level of detail and the type of datasets according to their roles and needs to conduct similarity analysis.
This is possible because EIIG’s similarity analysis is performed in a self-defined data fabric with wide-ranging datasets of all types as well as hierarchical views.
For example, the BI team may focus on just the duplicated reports, the data integration team on redundant ETL jobs, a data engineer on similar tables.
For the risk managers, they may use similarity analysis to detect fraudulent activities by comparing new transactions with historical records and identifying patterns of suspicious similarity.
Compliance officers can use similarity analysis to identify and tag PII datasets to ensure compliance with data privacy regulations.