7 Master Data Management Best Practices

Editor’s Note: This post was originally published in May 2022. We’ve updated the content to reflect the latest information and best practices so you can stay up to date with the most relevant insights on the topic.

Your business is powered by data. Lots of data. And that data is growing larger, faster, and more diverse by the minute. But if we were to wager a guess, much of that data is messy and inconsistent, living in multiple systems and swampy data lakes across your organization.

In order to free your data and make it usable across the business, your organization needs to embrace master data management (MDM). AI-native MDM combines machine learning, agentic AI, and human feedback to clean, unify, and enrich your data, delivering the golden records needed to support analytical and operational use cases, as well as AI initiatives. With trustworthy data about business-critical entities such as customers, suppliers, products, and more, organizations can make better, more confident decisions that help increase revenue, improve operations, reduce costs, and minimize risks.

7 Master Data Management Best Practices

While thoughtful implementation of MDM requires strategy, commitment, and leadership buy-in, there are also seven MDM best practices that set an organization on the road to success.

1. Start with a business use case

Defining the business use case you want to tackle first is priority #1 when you’re getting started with MDM. There are many places you can start—from B2B customer and prospect data to consumer and household data to product and parts data. It’s important to remember, however, to avoid “boiling the ocean.” 

Instead, decide which initial use case aligns best with your organization’s goals. Talk to the business stakeholders. Understand their challenges and needs. And build relationships with them. After all, their support is key to your success. 

Once you’ve identified a use case, you’ll then want to build out the requirements and develop a business case. These crucial steps help to secure buy-in and support for MDM. And, they help you to clearly demonstrate how the investment aligns with your organization's strategic goals and addresses real-world business challenges. 

2. Adopt an AI-native approach

Traditional master data management solutions employ a rules-based approach to mastering data. But these solutions are rigid, inflexible, and can’t scale as the volume and variety of data accelerates. 

Instead, look for solutions that are built with AI at the core to solve entity resolution and data mastering challenges, not just using AI to recommend rules or serve as a chatbot.  From architecture to workflows to user interfaces, every component of a true AI-native MDM should be purpose-built to take full advantage of the power of AI/ML models and LLM-based AI agents. Using an AI-native approach also makes data mastering faster, easier, and more efficient, which will enable your organization to deliver far better outcomes at a much lower cost when compared with traditional, rules-based solutions. 

3. Engage humans, too

This MDM best practice goes hand-in-hand with the one above. While AI, AI agents, and machine learning are critical, so is human feedback. Make sure you are engaging your business users and subject matter experts throughout the MDM Journey to provide feedback on the data and the insights your solution delivers. These experts are invaluable when it comes to providing input on the relevance and accuracy of the insights that machine learning models produce.

Not only will human feedback improve your AI/ML models, but it will also foster tighter alignment between the data and business outcomes that require clean, curated, trustworthy data.

4. Enrich the data

The MDM you choose should support seamless data enrichment, which integrates trustworthy external data with your internal data in order to improve overall data quality. By adding additional relevant or missing information throughout the mastering process, you also ensure that your data is more complete—and more usable. External enrichment data varies by industry, but include data from providers such as Dun & Bradstreet, PitchBook, National Plan and Provider Enumeration System (NPPES), Centers for Medicare & Medicaid Services (CMS), and Research Organization Registry (ROR). 

5. Be truly SaaS-based

Our next MDM best practice helps to ensure that you are mastering data with an eye toward the future. We all can agree that the three V’s of data—volume, velocity, and variety—remain important elements to consider in your master data management strategy. That’s why you should adopt an MDM platform that is a true SaaS-based solution.

True, end-to-end SaaS solutions increase return on investment (ROI) by offering a host of benefits including lower upfront costs, quicker time-to-value, greater flexibility, and built-in scalability. 

By choosing a SaaS-based solution with a no-code UI, you’ll future-proof your investment and accelerate development, without the need for IT resources. Further, you’ll deliver consistent performance and capacity, even as your business evolves and you add additional analytical, operational, or AI use cases.

6. Architect within a best-in-class ecosystem

Historically, many companies wanted to use a single vendor for all things related to data. We believe this is a significant (and universal) blunder—and one you should avoid at all costs. Why? Because these traditional, single-vendor solutions lack the depth needed to solve your complex (and evolving!) data challenges.

Rather than relying on solutions from a single vendor, look for purpose-built solutions that are the best fit for the job at hand. When evaluating MDM solution providers, look for ones that built their solution with AI at the core—not ones that bolt on AI as an afterthought. You want an AI-native MDM solution that complements your current technology stack and uses an API-first approach to connect with your data ecosystem and power every workflow with real-time, trustworthy master data. 

7. Enable operational use cases

Operational use cases require organizations to structure and integrate their data so they can incorporate it into workflows and make it easily consumable across teams and departments. Just a few short years ago, implementing operational use cases wasn’t even feasible, but today, these use cases are essential to your success. 

Operational use cases drive day-to-day business processes—and AI-native MDM ensures that the data powering these processes remains accurate, complete, and up-to-date. By connecting your data to key business systems and feeding it to AI applications, you can demonstrate the impact of AI-native MDM and underscore the true value of your data as a mission-critical business asset. Even if your primary use cases are currently analytics-focused, be sure the solution you use supports operational use cases too. That way, when the time comes, the addition of operational use cases will be seamless. 

The Path Forward

Effective MDM best practices establish the foundation for how users across your organization trust, connect, and use the data to move the business forward.  By adopting these seven master data management best practices—and AI-native MDM—you’ll achieve more than just better data. You’ll also drive better decisions, stronger outcomes, and greater long-term value for the business.