7 Steps to Develop Your Master Data Definitions

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7 Steps to Setting Master Data Definitions in Your Company

After you have resolved the governance paradox, the first step to starting an MVP data governance program is to build the foundation or establish the structure. This includes creating a standard set of definitions that the people of your company will follow when working with the data.

Why are definitions so important?

Let’s say the CEO asks you: “How many customers do we have?”

It sounds simple, but many times, different people in a company will answer with a different number.

Why is that?

They’re defining a customer differently and thus, they’re seeing their data differently. This is often the reason why companies seek to build a governance program in the first place.

Team alignment starts with having the same language to communicate.

Master Data is your trusted bits of information about all the things you need to successfully execute and run your business on a day-to-day basis. It sounds easy on paper, but data like this is very difficult to maintain because your organization contains diverse environments where multiple departments such as marketing, legal, and finance will all have a unique way to look at a customer, a contract, or a product.

Time and time again, I see companies invest in Master Data Management (MDM) software without a clear idea of how they will define customer in those systems.

With proper definitions, you won’t have multiple competing definitions for the most important business entities within your information systems (customers, partners, suppliers, products, etc.) As a result, your team gets consistent and trustworthy data that can act as the anchor—the source of knowledge—so clear discussions can be had, strategies can be made, and systems can scale.

That is the beauty of Master Data. It allows you to take the information you have about the specific things in the organization that you need to manage and find a way to scale that data to monetize it and operationalize it to fulfill your corporate strategies.

The goal is to gain a high-level, accurate view of the companies with which you do business. This means having a common structure, common definitions, and a common set of quality metrics that you can apply to be able to say, “This is how we define a customer at a corporate level, enterprise-wide.”

7 best practices for creating Master Data definitions

Below is a preview of the 7 best practices for creating Master Data definitions in your company. Download the whitepaper, The Data Governance Minimum Viable Product, to gain a more thorough understanding of these steps.

  1. List your goals

    What challenge are you trying to solve today? What’s the vision of success? An example of a requirement is “having a consistent measure of customer satisfaction across business units.”

  2. Choose one focus (e.g., “customer” or “supplier”)

    Limit your scope to a single entity. Consider the number of sources and volume of data you’re trying to govern. Where’s customer data coming from across your organization? The more data you’re looking to govern, the more definitions and exceptions in your data that will likely need be resolved.

  3. Define an extremely limited set of governed data for your chosen entity

    This can be a handful of record fields, such as name, address, and phone number. Reverse-engineer from your goal. What data elements are important to have at the executive level?

  4. Use Master Data to help you

  5. Protect your scarce resources and optimize your processes by using industry-standard reference data, instead of establishing standards for data that likely already exist.

  6. Understand the lineage and business usage of your governed dataset

  7. Where is the data sourced? Who has the rights to create it and modify it? How will this data be ultimately used to support the business? Creating an end-to-end flow of the data will give you clarity on how and where data is used in each department.

  8. Avoid forcing or requiring drastic business process changes

  9. Find the pockets where you see potential to implement change in an incremental way—without requiring a major process overhaul. This makes your effort more feasible with fewer bottlenecks ahead.

  10. Get the data architecture right – don’t cut corners

    You have a starting set of definitions, and a data structure. Now’s the time to work with your IT partner to ensure those definitions are correctly supported within the systems and databases that are housing your enterprise data.

I put together a detailed guide on how to start extracting the maximum value from your data through an agile approach to data governance. Download my paper, The Data Governance Minimum Viable Product (MVP), to learn more.


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