An Inside Reference Data special report covering aspects of data governance, including EDM and MDM methods.
November 2015 -- Sponsored by AIM Software and Eagle Investment Systems
The EDM/MDM Challenge
Asked whether enterprise data management (EDM) as a method of data governance has weaknesses, AIM Software's Olivier Kenji Mathurin says EDM's problem is a lack of transparency (in the Virtual Roundtable in this report). A possible reason for this, Mathurin says, is that retracing the technical implementation of data policies can be necessary to uncover implementation information and make the data governance transparent.
While EDM can ensure consistency, it does not address whether processes should be governed centrally or in more distributed or federated fashion, says Mathurin's colleague, Paul McInnis of Eagle Investment Systems. Master data management (MDM) uses aggregation of sources to ensure a consistent view, McInnis adds. Yet, MDM and EDM can possibly complement each other, according to Acadian Asset Management's Brian Buzzelli.
These views raise the question of whether both MDM and EDM should be used, and used together, to get data governance benefits that would otherwise be impossible to achieve - especially transparency. MDM should be viewed as a component within EDM, according to Scotiabank's Paul Childerhose, interviewed in a Q&A. Setting up MDM and EDM this way ensures improvement of data governance and data quality, he says.
These data governance professionals all say progress has been made in the field, but it's apparent that with more attention paid to data governance and more data governance work being done, the issue of figuring out MDM and EDM plans is taking center stage.
It’s a trio of problems: Mifid II’s data problem; blockchain projects stalled; and data quality issues for machine learning.Subscribe to Weekly Wrap emails