MDM needs data governance

Thought leadership

Many organizations struggle to implement a master data management (MDM) program because it is so complex. A robust master data management program includes the entire enterprise, bringing together both IT and the business. The goal of an MDM program is to have a single source of truth for all your data across the business, including all sources and applications. While a master data management program can be daunting, a data governance solution can help ease the stress and make your program run smoothly. 

Learn how Collibra is helping HEINEKEN achieve their vision as the best-connected brewer by providing visibility into their data landscape.

Read their success story now and see how we can help you power your transformation with data governance solutions.

Frequently asked questions about master data management (MDM)

Before we dive into how data governance helps a master data management program, we need to define MDM and think about it in the context of data governance. Below are some frequently asked questions on this topic: 

What is master data management in data governance?

MDM in data governance focuses on maintaining accurate, consistent, and controlled data, known as “master data.” A master data management program helps to ensure that trusted data is used across the organization. 

What is the difference between MDM and data governance tools?

    MDM tools focus on managing core data quality and consistency, while data governance tools encompass a broader range of data-related processes, policies, and compliance, including Master Data Management. MDM is a subset of data governance, which is more comprehensive in scope.

    What is the master data management governance process?

    The Master Data Management (MDM) governance framework involves establishing data standards, roles, responsibilities, and policies to maintain consistent, accurate and controlled master data within an organization. It includes data quality assessments, stewardship and continuous monitoring

    Master data management vs data governance

    It is important to note that a data governance program is not the same as a master data management program, but they can work in parallel.

    In fact, many organizations start off their digital transformation by asking themselves, which project should we start with first — master data management or data governance? These organizations know they need to do both (MDM and data governance), but don’t know where to start. 

    The answer is simple. You should do both as one project. 

    The reality is that while you can implement a data governance program without doing an MDM project, you cannot implement MDM without doing a significant bit of data governance. The two efforts are not mutually exclusive. All business programs require good data, and all data needs governance.

    In fact, one of the most significant business use cases for data governance is the implementation of an MDM program. MDM can be used as a business offensive approach (to generate greater sales) or as an efficiency approach (to reduce the cost of operations). The efficiency approach is the more common use case, and IT typically is in charge of this implementation. Yet, to be successful, business data governance activities must be included in the implementation. Let’s explore both use case approaches and how data governance can be leveraged for MDM success.

    Master data management use cases

    An example of an “offensive” revenue generating business case for MDM can be implementing a business glossary. With an “offensive” use case, you typically start with clarifying and consolidating the customer base, identifying customer touch points and customer interactions. The business glossary helps with this by defining what each business unit considers to be a customer or prospect. This approach is generally driven by a business executive or even the CEO to ensure that all revenue generating data is clearly defined across the business. 

    MDM can also be implemented to reduce the systems and cost of creating and maintaining product, reference or customer data. This is often the use case when an organization has gone through many mergers and acquisitions (M&A) over the years.  The same customer may have the same data in many siloed applications that have point-to-point interfaces to share that data and metadata. 

    So why should we consider the above MDM implementation cases as data governance use cases? Why MUST one incorporate a data governance initiative in every MDM project? Let’s look at what resources, processes, data and metadata is needed to make the MDM project successful. And, of that, what should be provided by your data governance processes and resources.

    A master data management solution and implementation requires the following data governance activities:

    • Agreement on the business and technical resources that will be accountable for the mastered data: It is necessary to have one resource (person, business unit, or committee) to be accountable for the data in the MDM “golden record.”  This is a data governance activity and the results should be maintained in the business glossary.
    • Agreement on the data that will be mastered and the associated metadata (data length, type, etc.): The decision may not be a simple one since all business unit functions and usages of the mastered data must be considered. This is often a technology decision and may impact existing application systems. This too should be maintained in your business glossary.
    • Consensus for the business terminology of the data being mastered (customer or vendor or employee): This terminology is business terms and should be maintained in the business glossary.
    • Agreement on the business rules and policies that will be enforced for the management of the “golden record data”:  The business rules that govern the creation, management, archive and disposition of the customer data may vary by the legacy application and business unit functions, as well as regulations that govern an individual business unit. These rules and policies need to address all of the needs of all business units. These rules should be maintained in the business glossary.
    • Consensus on the data quality rules for the golden record data: This one can be a challenge given that all application and business unit usages must be factored into the decision. Defining data quality and the thresholds are a data governance activity. Agreement on the profiling rules and sources for determining the quality level is also a data governance activity. Data quality metrics should be maintained in the business glossary so all data consumers are aware of the level of quality prior to their usage.
    • Agreement on the technical data integration and consolidation rules:  There must be rules for the consolidation of like data into the golden record. The data from one application may take precedence over the same data from another application. This is also a data governance activity, determining the best data for the organization to use. The metadata of the merge/purge functionality must be maintained and provided to all data consumers. This is known as traceability and data lineage. Again, the rules and operational metadata should be maintained in the business glossary.
    • Agreement on the resources that will function as data stewards to maintain the mastered data and validate merge/purge records to maintain the best data in the golden record: Yes, these are the data governance resources in both the technology and business teams. You may need stewards from many business units to manage customer MDM data. The roles and responsibilities, as well as policies from data governance should be used by the governance resources.
    • Agreement on the measures and metrics that will be used: These may be metrics in the progress of the MDM implementation, metrics for data quality, metrics for MDM data governance. Again, this is a critical deliverable from data governance and the metrics should be maintained in the business glossary.
    • Agreement on all associated reference data that will be used with the mastered data: We see the increased importance of reference data in both product and customer MDM implementations. Many consider reference data as a type of MDM implementation. It is a core set of data that the data governance team must address as early as possible. Reference data is one of the data domains that must be considered as enterprise data and has significant value to the MDM program and all other applications in the enterprise. And yes, your reference data should be maintained in the business glossary (not that you would question that by now).



    An MDM implementation should leverage the data governance ability to communicate, educate and promote the critical data projects that impact everyone in the organization that uses data. And, today who does not use data! Thus, there should not be a question of doing an MDM project or doing data governance. It is not a question of which but a question of when do we start the MDM project so we can further the adoption of data governance. It is not two efforts but just one that has significant benefits and value to the organization. Yes, MDM is more than data governance and data governance is more than MDM. But the MDM program should be considered as an implementation of both practices. 

    Related resources

    Thought leadership

    AI governance


    How data governance is revolutionizing treatment efficiency in healthcare


    How to become a data governance expert

    View all resources

    More stories like this one

    No results for this post.