Developing a Master Data Management Strategy

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.

See if this sounds familiar. Your organization wants to become data- and AI-driven. It invests in a myriad of tools and technologies, all with the goal of generating more value from data. But you know that these efforts, while valiant, lack one thing: a master data management (MDM) strategy.

Having a master data management strategy helps you align business goals with technical objectives, ensuring that the whole organization is on the same page about the definition of success. It will help you decide the best ways to spend your efforts and resources. And on a tactical level, a data mastering strategy also helps ensure that everyone is “speaking the same language” and agrees upon what the data is and is not.

Without an MDM strategy, your organization will continue to fall short of its goal of becoming truly data- and AI-driven. And while developing an MDM strategy may sound daunting, it isn’t that difficult if you know where to begin.

How to Develop a Master Data Management Strategy

Every good MDM strategy starts by identifying the challenge and articulating ways to address it. Below, you’ll find the elements your strategy needs—and the steps you should follow—to create one for your organization.

1. Business case: Job number one is to create a business case for master data management and the tools you’ll need to support it. Include elements like:

• Why master data management is important
• What happens if you fail to invest
• Which tools and technologies you need to be successful
• Who should be part of the project team, and
• How you’ll measure success

Creating a rock-solid business case sets your strategy up for success. Not only does it help you to justify the investment of time and money, but it also helps you articulate the risk—and cost—of doing nothing.

Further, it helps you secure buy-in from the right leaders across your organization. Buy-in is key to ensuring your organization is committed to making master data management a success.

2. Deployment plan: Every master data management strategy needs a well-thought-out deployment plan. Consider all the elements you’ll need to make your MDM strategy a success. At minimum, make sure your plan includes the following three things:

• People: Bring together a cross-organizational team of people who can help make your strategy a soaring success. Consider colleagues who are passionate about data. Include others who are struggling because they don’t have access to trustworthy golden records. And reach out to business partners who will ultimately benefit once the master data management strategy is fully executed.
• Process: Define the process you’ll follow to execute your plan. Create timelines. Assign roles and responsibilities. Define phases and deliverables. And articulate the outcomes you expect.
• Technology: Every successful master data management strategy has best-in-class technology to support it. An AI-native MDM solution combines AI's efficiency and scalability with business context and human expertise to deliver the advanced capabilities you need to deliver golden records everyone can trust.

3. Architecture scope: Like a solid deployment plan, it’s also important that you define the architecture scope as part of your master data management strategy. Start by prioritizing one or two use cases. Define their requirements. And evaluate which MDM solution meets your needs.

Selecting a modern AI-native master data management platform like Tamr that is SaaS-based and built from the ground up with AI at its core is a good choice. Be wary of “AI-enhanced” solutions that bolt on AI capabilities, creating a Franken-monster solution that introduces unnecessary complexity and can’t scale. Instead, look for platforms that deliver entity resolution, semantic search, real-time APIs, agentic data curation, and LLM connectivity. 

Once you’ve successfully rolled out the first use case, do a retrospective with the team. Understand what went well—and what didn’t. Learn from your initial experience. Adjust your plan. And then get started on the next use case.

4. Maintenance/DataOps: The final element of your master data management strategy is maintenance. And this is where you want to consider incorporating a DataOps mindset.

According to Gartner, DataOps is “a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data managers and data consumers across an organization.”

DataOps acknowledges the interconnected nature of data engineering, data integration, data quality, and data security/privacy. And it helps organizations rapidly deliver clean, trustworthy data at scale for use in analytics, operations, and AI applications. It also provides a wide range of benefits ranging from faster cycle times to fewer defects and errors to happier customers.

DataOps will help you maintain your master data management strategy in a way that is efficient and effective. And, it will help you continue to reap the benefits of your master data management software for years to come.

Old Mutual: A Master Data Management Strategy Example

Financial services group Old Mutual (OM) had a goal: to digitize customer journeys in order to improve accuracy and quality. But the data they needed to do so was trapped in multiple, siloed MDM systems, making it impossible to obtain a trustworthy, 360-degree view of their customers. Complicating matters further, their shift to the cloud surfaced interoperability issues with their existing MDM solutions, underscoring the need to modernize their data systems. 

To address these issues, OM kicked off an initiative to modernize their infrastructure and their MDM solutions, which included decommissioning their three legacy MDM systems. In partnership with Tamr, Old Mutual built the foundational elements for their data strategy, including replacing their legacy MDM solutions with Tamr’s AI-native MDM.

As a result, Old Mutual:

• Improved the accuracy of their data by 69% in just six weeks, resulting in their ability to deliver trustworthy golden records
• Fully decommissioned their three legacy MDMs within nine months—replacing them with a single, real-time, modern MDM platform, saving the firm millions of dollars in costs
• Reduced data integration complexity and eliminated data silos by simplifying their IT landscape

Equally as impressive, Old Mutual achieved these results on-time, on-budget, and without hiring new resources. 

A Strategy for Success in the AI Era

‍As you can see, investing in a master data management strategy is critical for data- and AI-driven organizations. Not only does it help you align business goals with technical objectives, but it also ensures the entire organization agrees on the definition of success.

By taking the time to identify your challenges and goals, develop the deployment plan and architecture scope, and embrace a DataOps approach, you’ll set your organization on the path to becoming truly data- and AI-driven.

For a deeper look at the common pitfalls organizations face on the road to achieving data and AI success, download our ebook 6 Pitfalls to Avoid on the Road to Becoming Data- and AI-Driven.