Master Data Management: A Beginner’s Guide

As your business grows, it becomes increasingly challenging to keep track of all your data. For starters, these data are changing every day and some may become redundant and invalid. Now imagine trying to stay up to date and manage thousands of data from different sources—that must be exhausting to even think about!

However, it’s a challenge many businesses face. This is where master data management provides a solution. By the end of this article, you should have a wholesome idea of master data management, its essentials, and the difference between reference data and master data.

What is master data management?

The goal of master data is to provide synchronization to the critical pieces of data in a company. These pieces of data are not limited to customer data and product data; they encompass what we will call the nouns of a business. This includes all key players of any industry such as customers, partners, brands, products and services, and vendor data.

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Master data management (MDM) provides a focal point where businesses can identify data as accurate, coherent, and timely irrespective of how many other data sources they have. Furthermore, it produces the plumbing that synchronizes data between all other sources or underlying systems to the focal point. MDM does not stop here; it also ensures data quality. This means that whatever data is being synchronized is accurate, free of duplicates, and coherent.

What data should you manage as master data?

The concept of master data is thrilling, and you might want to treat it as a warehouse where you dump all your company data. However, remember that master data is only but a small portion of your entire company data from the perspective of volume.

As such, you must stick to maintaining only the most complex and valuable data under master data. As a recommendation, you can use the below MDM strategy to decide what data to manage as master data:

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Behavior Data.

You can describe this master data by how it interacts with other types of data.

Lifecycle or Crud cycle.

You can describe this master data by the way it is created, interpreted, updated, and erased.


Cardinality here is the number of elements in a set. As it decreases, the possibility of creating an element as a master data element—irrespective of its subject area’s acceptability—decreases.


As master data becomes more volatile, it gradually becomes transactional data.


The less complex an element is, the less likely it is to be managed as master data because it is easier to manage.

Value and complexity.

Data elements that are valuable to the company are more likely to be categorized as master data.


Data items that do not change do not need a master data program.


Reuse is one of the key motivators of master data, and as such, any data that requires multiple uses should be managed by master data.

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Essentials of Master Data Management


A centralized MDM program can maximize the operational efficiency of enterprise data assets and significantly reduce the amount of time and cost organizations spend in sourcing valid data. Therefore, to ensure the data quality of your MDM system, there are several essential attributes it must possess. However, the most important remains good data governance. Good data governance ensures the validity and security of all data silos.

Master data management differs depending on your company’s industry. However, successful MDM programs must share these attributes, complexity, high value, non-volatility, and non-transactional.

What’s the difference between reference data and master data?

While both reference and master data provide context for business transactions, they’re different. On the one hand, reference data concerns itself with categorization and classification, making it the type of data that is used by other data fields. Whereas master data, on the other hand, concerns itself with business entities. It’s shared by different applications, domains, systems, and processes in an organization.

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