Going Beyond the “Golden Record”

In a recent post, Michele Goetz from Forrester raised the following statement: “Master Data Management Does Not Equal The Single Source Of Truth””. This remarkable post was followed by responses from Andrew White from Gartner and Lorraine Lawson.

The keys of this discussion (which was also reiteraring the fact that MDM should be implemented for the business, with the business and not only by the IT) are in the following quotes:

“MDM is not about creating a golden record or a single source of truth.”

“What MDM provides are definitions and instructions on the right data to use in the right engagement. Context is a key value of MDM.”

A common (mis)understanding about MDM consists in restricting the scope of MDM to the creation of the famous “Golden Record“. This misunderstanding leads commoners to see MDM as a kind of data modeling/integration/quality system with strong deduplication and cross-referencing features.

Well, MDM mandates these capabilitiries, but does not stop there. Although MDM is not Data Governance (this is a different debate), it goes way beyond “Technical Data Management”.

The “Golden Record” is not the ultimate target (or Holy Grail) for master data management. Creating value from the golden record is the real target, because the business wants value, and not data.

The MDM initiave has to deliver business-oriented information, applications and processes supporting better operations and decisions in a secured and compliant infrastructure, and have these constantly evolve with the ever-changing business needs.

The set of features (applications, business objects, views, workflows, advanced security, rich user experience, etc) introduced in Semarchy Convergence for MDM in releases 1.3 and 2.0 empower the MDM team to quickly provide the business with the elements to go beyond the golden record.

2 replies
  1. Manjeet
    Manjeet says:

    Just to add: Adapting the MDM solution to changing business requirements is part of it, but also MDM includes Data Governance which therefore both combined also need to put in place processes, policies, procedures and people who are going to define frequency of profiling data and establish technical solutions to allow monitoring and reporting on the data quality to ensure continued high-levels of data quality i.e. the master data.

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