6 Key Components to Enhance your Master Data Management Program

An optimized MDM solution will provide effective product management and be a key component in future business initiatives.

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Whether you are starting to build your Master Data Management (MDM) approach or are continuing to evolve your current program, there are key MDM fundamentals to keep in mind. This article outlines six steps to help businesses evaluate and sustain an extendable program to help ensure future success.

A well set-up and optimized MDM solution will provide effective product management and be a key component in future business initiatives. MDM insights are fed into supply chain optimization, customer 360 views, sales targeting and advanced business analytics. If you have built your own MDM solution or leveraged a commercial product, you will want to keep six key MDM fundamentals in mind. These fundamentals apply whether you are starting to build your MDM approach or are continuing to evolve your MDM program.

Setup Master data matching and linking

A well-constructed MDM solution will pull together disparate and diverse sources of data from different systems and applications. Algorithms should be applied that automatically identify duplicates and resolve multiple entries into a single record. This assures you are always describing the same customer/service/product etc.

Effective matching and linking eliminate duplicated data, share accurately across all systems, create a foundation for future enrichment, monitor the integrity of Master data and data integrity of source systems, and create a way to automate/eliminate manual values assignment and regrooming.

Create and Apply Master data business rules

You will want to ensure that you introduce conditions and actions as business rules that are shareable across subject area use cases. A good MDM will provide this functionality. This directly enables rules to be changed once and applied in multiple places with authority of an approval process. Governance strategies and policies you put in force define workflow decisions and approval processes that actively enforce data integrity.

Manage data location/localization as part of governance strategies proactively

Where data is generated, where it is physically present, and how it is safeguarded through the assigned responsibilities of its controllers is critical to establishing custody and proper use compliance. Regulatory and statutory requirements have high non-compliance costs.

Create effective and appropriate safeguards for data privacy and security

Data privacy policies and processes and role-based security policies must proactively define access rights to all Master data. This dictates active roles-based access to data including data obfuscation, masking and encryption be applied. This ensures auditable access controls and proactively eliminates vulnerabilities that could be exploited by hostile parties to gain undue access to data.

Establish data baselines with abilities to support future data enrichment and refinement

Create point-in-time 360-degree views of your data. Clean and streamline data to ensure incorrect and irrelevant data are corrected over time. This ensures levels of evolving accuracy and similarly supports actively integrating third-party data toward deriving maximum business insights.

Deliver baseline consolidation and control, including consent management

Actively consolidate subject area data into a single place to reinforce active collaboration between stakeholders throughout business processes. Key features like “rights to access” and “rights to object” must be implemented as part of a clear and transparent information custody framework. Clear policy on how PII data is maintained and authorized includes the “why” and proof of active “consent.”

Maintaining a centralized repository allows you to monitor and better protect sensitive data, reducing the risk of improper data exposure.

Evaluating these core components when starting your program or periodically via your governance process will allow for a sustainable and extendable MDM program.

Robert Eichelman

Robert is an IT specialist with 20+ years of combined experience in enterprise data ecosystem development, enterprise data warehouses and large-scale customer-facing applications. He is a leader in delivering large-scale Business Intelligence and Analytics solutions for Experis customers in financial services, energy, Healthcare, Supply Chain, Manufacturing and Web Analytics.


Robert is an IT specialist with 20+ years of combined experience in enterprise data ecosystem development, enterprise data warehouses and large-scale customer-facing applications. He is a leader in delivering large-scale Business Intelligence and Analytics solutions for Experis customers in financial services, energy, Healthcare, Supply Chain, Manufacturing and Web Analytics.