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Master Data Management: Best Practices & Real Life Examples in '24

Updated on Jan 2
5 min read
Written by
Gulbahar Karatas
Gulbahar Karatas
Gulbahar Karatas
Gülbahar is an AIMultiple industry analyst focused on web data collection, applications of web data and application security.

She is a frequent user of the products that she researches. For example, she is part of AIMultiple's web data benchmark team that has been annually measuring the performance of top 9 web data infrastructure providers.

She previously worked as a marketer in U.S. Commercial Service.

Gülbahar has a Bachelor's degree in Business Administration and Management.
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There are many important types of data that companies use to improve their operations, such as product data, customer data, location data, or asset data. However, as the volume of data and the number of databases increases, it can become difficult to maintain control, and multiple departments struggle to work with the same data in a synchronized manner. Master data management enables companies to avoid duplication and data conflicts between departments and systems.

What is master data?

Master data is the core data that describe business objects such as customers, business locations, and products. Customer addresses, and phone numbers are examples of master data. Master data is one of the data types used by businesses. Other types include:

  • Transactional data: Transactional data records the information such as time and place of transactions.
  • Metadata: Data that describes another data. For example, it gives information about data such as the author of file, type of file or size of file.
  • Reference data: It is a subset of master data that is used to classify or categorize other data such as transaction codes, postal codes, etc.
  • Test data: Data that doesn’t include personal or other confidential data but still exhibits similar properties to other company data. This data is used while testing new systems.

What are alternative ways to say master data?

The word master has association with slavery and many vendors are replacing the term. We have kept it unchanged since most users still search for “master data” but please be aware that there are more modern terms for this data type and we will also be adopting alternative terminology in the future as alternative terminology becomes more common. Alternative terms and the projects that use them include:

For a more comprehensive list of alternative terms, feel free to check out Wikipedia page on the topic.

What is master data management?

Master Data Management (MDM) provides every employee in an organization with a one-stop shop for business-critical information by creating a master data record. MDM collects data from systems used by different departments in an organization to create a master record for all departments. The master data set is the most accurate and well-defined single resource for business units. MDM eliminates incompatible data and creates a single master record that is valid for all systems.

What are the differences between master data management and data governance?

Data governance is frequently confused with master data management. However, there are major differences between them:

  • Data governance is a broader set of policies that are applied across the enterprise. However, the scope of master data management is narrower and aims to execute specific processes in support of governance. Fundamentally, data governance represents a strategy, while data management is a practice used to support the data governance strategy.
  • Organizations use tools to support their data governance strategy, but it is not limited to specific technologies. MDM is enabled by specific tools.

Why is master data management important?

Data is an essential part of business decision-making processes and inaccurate data can ruin entire business functions such as planning, organizing, leading, and controlling. Duplicate and incorrect data must be fixed by employees to keep business processes running smoothly, and such challenges cause companies to waste time and money. Master data management enables businesses to reduce data errors and increase data quality.

What are the common steps required in a successful MDM implementation?

  1. Creating an MDM team: The formation of an MDM team is a critical component to a smooth process. First, relevant stakeholders need to be identified. They include departments heads, tech staff, and chief data officers who are responsible for collecting, monitoring, and analyzing data. From these stakeholders, a core team needs to be identified. Some of the key responsibilities of this team are:
  • Implementing master data management policies and procedures and revising them in line with the needs of the business.
  • Providing information to relevant employees about these policies.
  • Developing solutions for master data problems and managing the solution process.
  1. Choosing the right MDM solution: Not every MDM solution is right for every business. However, the MDM solution that you choose should be able to match and link data, manage data location (where the data is stored and generated), and support data privacy.
  1. Implementing MDM in existing system: Creating a new master data set from scratch can be costly, especially for organizations with large amounts of data. Since MDM is a complex process to apply to an existing database, the risk of failure can be reduced if it is implemented in the systems of record of the enterprise incrementally.

What are the benefits of master data management?

Data about products, customers, employees, etc. are the assets of companies. Master data acts as the main file that contains the customer IDs, item numbers, names, dates, and addresses used by different departments within an organization. However, especially in large organizations, it is not easy to synchronously manage and manipulate data that is used by different departments. This can lead to data inconsistencies. For example, customer registration information, such as customer addresses, may vary between customer service and order entry. 

  • MDM provides a unified view of critical business data with a single master dataset and ensuring consistency of data used in analytical and operational processes.
  • It provides up-to-date data for businesses. MDM collects, transforms, and corrects data and creates a golden record for businesses. The golden record contains the essential information such as customer-supplier, product, etc. that is used and required by various departments in organizations.
  • Manual data management is more difficult, especially in organizations with large amounts of data. Because MDM automates many steps of the data management process, the time and resources required to process master data are reduced.

Master data management programs combine data from different systems into a standard format. This is referred to as creating a master record. Some of the advantages of creating a single master record for the entire business are: 

  • Increasing data quality by eliminating data duplication
  • Managing all data entities in a single record enables data accuracy
  • Making data editing easier, as the data change made on the master record is reflected in all related systems within a business.

What are the use cases of master data management?

Master data management is primarily used to organize, localize, and synchronize sales, marketing and operational activities of companies.

Suppose that a customer has changed their address and billing address. The sales department may have the updated address, but the finance department may not have the updated information. This may result in invoices being sent to the wrong address until the customer confirms that they have the new address. MDM matches and updates the information when a contact is added to the address book by creating a single version of the address book and synchronizing it with all departments.

What are the challenges of master data management?

Some of the main challenges to master data management are:

  • Master data that is not managed in a timely manner increases the cost burden on organizations by leading to repetitive transactions in data stores. It can be difficult to apply master data management to existing data; it is more efficient to apply it to new data first. Applying master data management to existing data in stages makes the process easier.
  • There are many tools for master data management, but the most important point is to choose the right tool that fits your business. Before selecting a tool, you need to analyze and define the requirements and purposes of your business.  
  • There are different data types and sources from which a single master record is to be created. Transferring existing data into a master record can cause complications because the data integration process takes time. You need a data integration plan to be prepared for such problems. 

If you need a master data management tool, feel free to check our data-driven list of Master Data Management (MDM) tools, and Data management platforms (DMP).

If you have other questions about master data management, we would like to help:

Find the Right Vendors

Gulbahar Karatas
Gülbahar is an AIMultiple industry analyst focused on web data collection, applications of web data and application security. She is a frequent user of the products that she researches. For example, she is part of AIMultiple's web data benchmark team that has been annually measuring the performance of top 9 web data infrastructure providers. She previously worked as a marketer in U.S. Commercial Service. Gülbahar has a Bachelor's degree in Business Administration and Management.

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