Wednesday, June 28, 2017
Master Data Roadmap

Whether just getting started or well on your way, a Master Data Roadmap is critical to the success of an MDM solution implementation. Any MDM or data governance program that doesn’t institute methods to measure, manage, and enforce data quality is ineffectual.  Having a single source of master data that is accessed by multiple systems certainly satisfies one key requirement of a master data initiative.  However, if the master data is simply a centralized collection of enterprise data acquired from various source systems then the initiative merely exacerbates the problem.  In fact, the result may yield an unfettered circulation of invalid data to a potentially greater number of master data subscribers.


  • Identify the business and technical stakeholders to involve in the governance process and clearly define their roles
  • Form a multi-tiered governance organization with a governance committee and supporting sub-committees
  • Document current and future state master data business processes, identifying key topics to be addressed through the governance process, GAP analysis, and multi-phased roadmap
  • Accelerate the governance process by facilitating committee and sub-committee governance meetings, driving discussions, and ensuring decisions are aligned with the Maestro platform
  • Define communication channels between business and technical stakeholders, ensuring technical teams effectively communicate questions to the governance organization and governance effectively communicates decisions to the technical team

Governance Dimensions

One of the responsibilities of a data governance program is to establish the necessary criteria and controls to measure and manage the quality of the master data.  The industry model used to evaluate a master data element’s quality conformance is measured along the following dimensions:



1. Timely

As master data is created, updated, or removed the changes are quickly reflected in the repository (e.g., a new account).  Master data that changes frequently may exhibit obsolescence if the data stewardship and integration processes are ineffective.

2. Relevant

Relevancy implies that a sufficient number of entities, hierarchies, and attributes are stored in the repository to satisfy the needs of master data consumers.  For example, a CRM system may require a global account indicator whereas HR system may require a PII flag.  Relevancy spans all business processes, functions, and systems that consume the master data.

3. Complete

Completeness complements relevancy to ensure that all of the necessary data elements (valid values) are present in each entity and that each attribute is populated if required.

4. Valid

Validity ensures that the data elements and definitions that comprise the master data are properly represented (e.g., the NAIC code for a client company is 5 numeric digits).

5. Accurate

Accuracy implies that even if the master data conforms to the first four criteria and is semantically valid, it is also correct.  For example, the NAIC code for ACME Corporation is, in fact, 90210. Accuracy falls under the purview of data stewardship and is the most difficult to manage, for it works in tandem with the other dimensions.  For example, the NAIC code may be accurate in January, but incorrect in February after an acquisition has concluded (i.e., it must be considered within the context of the first dimension).

6. Consistent

Consistency implies that the master data is complete and uniform (standardized).  For example, all U.S. postal codes are 9-digits across the universe of client addresses.  Consistency also indicates that, for most entities, redundant data are removed (e.g., the list of client companies is unique).

7. Compliant

Compliance ensures that the master data conforms to various reinsurance legal and regulatory guidelines (globally and domestically) that affect the entity, attribute, or hierarchy.

Key Benefits

  • Creation of a productive governance process, avoiding common challenges of a monolithic governance organization
  • Ensure governance decisions are supported by the functionality of the MDM platform
  • Accelerate the governance process, ensuring implementation time frames are met
  • Augment your investment on a governance process with the capability of making decisions quickly enough to support implementation efforts


  • The formation of a Data Governance Council
  • Current and future state business process designs
  • The creation of an agile communication process

Request more information about our Master Data Roadmap


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