Best Practices for Developing a Master Data Management (MDM) Roadmap

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Sometimes even the most successful and established organizations do not have a clear idea where to begin when establishing a roadmap for a master data management program. However, effective Master Data Management can have huge business impacts that form the building blocks for trusted data, repeatable processes, and a more efficient way of managing enterprise information. 

Before starting on your MDM roadmap, it's helpful to first understand these four major data challenges:

  1. The inability to prioritize what departments and/or programs have the most significant impact on daily business operations and processes.
  2. Inconsistent fluidity in governance around misleading critical data.
  3. Non-transparent ownership of data at point-of-entry.
  4. Unreliable key performance indicators that fail to drive actionable insights about an organization.

Addressing the Major Data Challenges

The DATUM approach to resolving some of these major struggles starts with gathering the business goals and objectives (capability roadmaps) and determining the required data capabilities (core components). Understanding and determining what the end-goal of the Master Data Management Program will provide an outline for a strategic roadmap.

Click here to learn how Keurig built a centralized MDM organization

After mapping an end-goal, organizations should determine the strategic data domains and data elements that are most critical to the business and deliver the most measurable business value. These strategic data domains and data elements are used for the business rules and standards that our clients capture in our Information Governance Software Solution – Information Value Management®. But even before business rules and standards, you must first answer “How do we know what is most critical to govern?” and “How do we know it will make a measurable impact on our organization?”

 We follow a reverse engineering methodology where we can extract what an organization already has in place, and define what data is most important to govern. Applying filters, based on our guiding principles to the information gathered within Information Value Management®, allows us to drive prioritization and an execution strategy that delivers a value based roadmap and closes the current program gaps to reach our ultimate end-goal.

Templates and examples, like those found in Information Value Management®’s accelerators and content packages, have been very helpful to our clients when they are ramping up an MDM program. These features go hand-n-hand with all our applications (including our brand new “Goals” and “Objectives” applications) and provide customers a starting point to build-out a custom MDM roadmap to fit their organization’s unique needs.

Three Proven Methods

DATUM has found that there are three proven methods of driving a strategic roadmap for Data Governance that is aligned to business objectives and value. Each method has benefits and risks that need to be considered:

  1. Enterprise focused roadmap
    • Benefits: This roadmap looks across all strategic programs and targets governance efforts on the critical data. This impact is majorly on individual business units and concentrates on the improvement of governance around critical enterprise data.
    • Risks: The downfall of this program is the lack of initiative focus, and may need adjustments in governance in the case of initiatives like improvements in cash flow.
  2. Initiative focused roadmap
    • Benefits:This roadmap targets a specific program and initiates governance efforts to holistically address specific program benefits. In laymen’s term, this roadmap embodies the governance that has a significant or direct impact on a particular program or initiative.
    • Risks: A tradeoff of this type of program formulates from the inability to prioritize critical data outside of those programs over the initiative at hand.
  3. Hybrid approach roadmap
    1. Contrary to the two methods listed above, this roadmap is a solution and best practice to satisfying both an Enterprise focused and Initiative focused roadmap. This selects 2-3 complementary programs and targets governance efforts for the critical data with significant cross-program benefits.

The Guiding Approach

Once you determine the best approach, it will exemplify prioritization and in turn contribute to focusing on what matters to your organization. A guiding approach to this type of prioritization within your MDM roadmap is to ask your organization these four leading questions:

  1. Is the domain or data element functionally complex? This asks if the data element or domain is largely cross-functional and has competing impacts on the organizations day-to-day functions and initiatives.
  2. Is there ownership and structure around this domain or data element? Are decisions around this domain/data element dictated by an individual entity and does that entity support the governance of that domain or data element on an enterprise and global level?
  3. How complex is the governance? In terms of value-drivers, an organization must determine the level of complexity it takes to deliver required governance capabilities.
  4. From a domain/ data element level, what is the impact of governance? Can measurable business outcomes be derived from the domain/ data element’s governance?

Asking these questions helps define what is important within a MDM roadmap, as well as establish where these complexities lie. This can assist in determining the scope of particular initiatives and drive effective value based decisions. By following these Master Data Management roadmap best practices, your organization can establish business relevance, transparency, operational efficiency, and scale for your data governance programs.

Interested in learning how our software platform Information Value Management® can accelerate a Master Data Management Roadmap? Dowload our Solution Brief here.

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