6 Master Data Management Best Practices for an Effective Strategy

Strategy   |   Bertrand Cariou   |   Oct 18, 2020

What is a Master Data Management Strategy?

Master data management strategy (MDM) refers to the governing procedures for entering, aggregating, consolidating, de-duping, standardizing, and maintaining data en masse throughout an organization. By ensuring control and reliability, MDM creates a single source of master data that can be applied and maintained by many different entities throughout a business. 

Why is a Master Data Management Strategy important?

Companies increasingly need cost optimization, faster product launches, and more efficient regulatory compliance, and having an effective master data management strategy is critical for all of that to happen. Without it, cross-organizational data misalignment can lead to suboptimal decision making and decelerated growth. But developing a Master Data Management strategy and putting it into practice across an organization is no simple task, and achieving dependable data quality is one of the biggest pain points for enterprises.

Creating a Good Master Data Management Strategy

There are six master data management strategy best practices your enterprise should consider when developing and implementing an master data management strategy:

  1. It’s not one and done: Your master data management strategy should be woven into the foundation of your business. If data alignment is considered merely a one-time occurrence, you will encounter the same data mismanagement issues repeatedly.
  2. Buy-in at the top: For master data management strategy to be successful, leaders within all business units must be engaged in the development of the strategy, as well as continuously involved in ongoing governance conversations.
  3. Education is key: On the other hand, all personnel and departments must be trained and regularly retrained on how to format, enter, store, and access data.
  4. Start small, but think big: When rolling out a new master data management strategy, you want to first focus on a smaller data set that may be causing some current business pain (e.g. customer or product data for a specific geography). Done this way, you can assure buy-in for a larger rollout.
  5. Keep your eye on ROI: Since business units have different objectives, a common ROI should be established at the outset of a master data management strategy development, and the return on investment should be examined after each phase of rollout to maintain buy-in.
  6. Don’t forget to update: Your master data management strategy must include regular, synchronized updates to ensure that your single source of data has the most accurate information.

What’s on the Horizon for MDM?

There are a number of trends shaping the future of master data management strategy. Here are a few:

Multiple Domains

Master data management software products that manage multiple master data domains – customer, product, locations, finance, and employee—will increase. Today, most large companies maintain at least two master data management  technology solutions because no single master data management strategy product meets all their needs.

The Cloud

The need to secure and quickly integrate vital master data may make companies reluctant to put their master information in the cloud. Still, increasing integration of data with SaaS applications may increase the development of MDM solutions that are cloud-based.

Big Data

These days master data management must incorporate big data strategy. A protocol must be developed for how to handle the volume and complexity of data, be “social networking aware,” and have a way to tie unstructured data to master customer profiles.

Common Challenges Presented by A Master Data Management Strategy

Implementing a master data management strategy across your business isn’t an easy task. Here are some common challenges to be aware of that many companies face when rolling out their master data management strategy plan:

  • Data Complexity: There are often many complex data quality issues when dealing with master data management strategy, particularly if data is coming from a multitude of systems and departments.
  • Duplicate Data: A Master data management strategy often results in duplicate data, especially in cases where companies or organizations are managing multiple master data domains.
  • Common Standard: When organizations are using more than one data management platform or data management software, it can be difficult to create a universal standard for data. This can cause differences in data formatting, entering, storing, etc.
  • Data Governance: For a master data management strategy to be successful, leaders within all business units must be engaged in the development of the strategy. They also need to be continuously involved in ongoing governance conversations like ownership, stewardship, and policies. Without unified governance, many problems can arise with organization and accuracy in your data management.

Data Wrangling: The Next Frontier

As MDM strategies and data itself become more sophisticated, incorporating data wrangling, or cleaning and unifying diverse data sets, will be imperative to incorporate into a successful master data management strategy.

Forward-looking businesses seeking to differentiate themselves on speed and efficiency are already integrating data wrangling into their master data management strategy.


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