What Is Master Data Management (MDM)? Definition, Examples, and Benefits

An organization’s data often comes from many sources and is touched by many people. If not effectively managed, this can result in redundant and even conflicting information. As your organization grows and becomes more complex, establishing processes and tools for managing your most critical data at scale is essential.

Master data is all the data critical to the operation of a business. This data is usually shared across the enterprise, and multiple departments and personnel depend on it for decision-making. The types of master data vary depending on the type of organization, but they all share some similar qualities:

• Less volatile. Master data tends to change less frequently than other data, but it does change. Data sets that never change are rarely classified as master data.
• More complex. Master data usually includes large data sets that are more complex in nature with multiple variables, as opposed to simpler entities that could be merely tallied or counted. Master data requires processes to keep information up to date and accurate.
• Valuable or mission-critical. Master data is essential to an organization’s day-to-day operations as well as analytical decision-making. Master data is used and reused over and over again.
• Non-transactional. Master data does not usually include transactional data. However, master data can be part of the transaction process, such as data describing the customer, product, or point of purchase.

Customer information—such as names, phone numbers, and addresses—is an excellent example of master data. This data is less volatile but occasionally needs to be updated when a customer moves or changes their name. If an organization has a large number of customers, this data can become very complex and the smallest error could result in a missed opportunity. Customer data is valuable because it provides a way for the organization to connect to its customers and delivers important insights for decision-making. Additionally, customer data is not considered transactional, even though it can be used when describing transactions. If customer information lives in multiple locations instead of a master database, then employees have no single source of truth for this essential data. A customer could, for example, update their contact information online in a logged-in account — but, if a different system handles email distribution, then the customer would no longer receive marketing communications. Mishandled, this could result in a real customer who appears to exist in one company database, but not another. Establishing a master data management solution can be an important step for growing organizations.

Many companies use master data management to organize, centralize, update, and distribute product data. If a company makes money from selling products, it’s essential that everyone in the company have access to a single accurate source of information for those products. A master data management solution would oversee and assist in the creating, reading, updating, and deleting (CRUD) cycle of this master data.

• Create. When a new product is purchased, acquired, or manufactured, the product information would be added to the company’s master data, and a master data management solution would be needed to streamline or even automate this process.
• Read. People throughout the company need access to product data—from manufacturing to sales divisions—and a master data management solution helps guarantee that each of these people has access to the same information. The solution could also be the source of product information for a company’s website or order-processing system.
• Update. If a product goes through changes, whether that be packaging, price, materials, etc., a master data management solution makes it easier to make those updates and disseminate the changes throughout the company.
• Delete. When a product is replaced or no longer available, a master data management solution would provide the tools to delete the product information from the data set.