For 21st-century organizations, data is lifeblood. If you’re looking into strategies to improve the way your organization collects and manages data, you’ve probably heard of both data governance and master data management (MDM).  

You might be forgiven for thinking that these two terms are interchangeable. However, they actually refer to very different concepts. In this article, we’ll take a closer look at the differences between data governance vs MDM, and explore when you might use one approach over the other.  

Data Governance vs MDM: What’s the Difference?

In a nutshell, data governance is the process of ensuring that data is accurate, consistent and compliant with organizational standards and regulations. Data governance covers all aspects of data management, from data acquisition and storage to quality and security.     

An effective data governance program will have clear policies and procedures in place to ensure that data is managed in a consistent and controlled manner. It is often overseen by a dedicated board or steering committee.  

MDM, on the other hand, is a process and set of technologies for managing the critical data assets of an organization in a consistent and accurate manner. So, whereas data governance is the overall framework for managing data, MDM is one specific tool that can be used either as part of a data governance strategy, or as a standalone program.  

“Master data”, is any data that underlies an organization’s major business and operational decisions. Organizations set their own criteria for what qualifies as master data, but it typically includes data on products, suppliers, customers, and employees. Usually, the relevancy of master data cuts across multiple departments.  

An MDM program frequently revolves around the creation of a central repository for master data, which can be used to provide a single source of truth for the organization. MDM programs often make use of data quality and cleansing techniques to ensure that the data in the repository is accurate and useful.  

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When incorporating data cleansing techniques, it’s vital for them to recognize the fact that, for the majority of inputted data, there isn’t a control mechanism to ensure the consistency or accuracy of the information entering your database.

Inevitably, some garbage, such as misspelled names or incorrectly formatted dates, will find its way into your database. However, this doesn’t mean it has to stay there. RecordLinker’s machine learning-based solution is specifically designed to assist you in cleaning up poor-quality data, regardless of the number of sources it originates from.

So, in a nutshell, data governance covers all data, while MDM focuses only on critical business data. And while data governance is focused on all aspects of data management, MDM targets quality and consistency. 

Colleagues sorting sticky notes into categories

When Should You Use Data Governance?

If you need to ensure all data within your organization is tightly controlled throughout its lifecycle, robust data governance may be the right framework.  

Usually, data governance programs are driven by compliance requirements. They are extremely important in highly regulated industries, such as financial services, where data accuracy, completeness, and security are paramount.  

These organizations need excellent oversight on the status of all their data, with the ability to track and audit data changes. Data governance programs often include data quality management initiatives, as well as policies and procedures for managing data.  

However, data governance can be very complex and time-consuming to implement due to its broad scope. Along with changes to IT systems and business processes, it may require the creation of new roles and even teams: data governance should be managed by a cross-functional body with clear roles and responsibilities, the authority to make decisions as to data management and use, and a dedicated budget.  

Embarking on data governance requires executive sponsorship, a clear understanding of business requirements and compliance standards, and buy-in from all stakeholders.   

Data governance is not the right solution for every organization. If you’re stuck on whether to implement data governance vs MDM, think through the costs and benefits. If you have a small amount of data, or your data is neither critical to your business nor subject to complex regulations, a fully-fledged data governance program may not be worthwhile.  

However, it is always worthwhile to keep your data highly organized. Avoiding messy data is essential for businesses to thrive and grow. One of the best ways to do that is by using RecordLinker. With RecordLinker, you can efficiently and accurately link records, saving precious time and organizational resources that would have been dedicated to fixing data errors.

When Should You Use Master Data Management?

Unlike data governance, which tends to be driven by compliance, master data management often emerges in response to basic business needs.   

Frequently, organizations discover that their decision-making abilities are increasingly affected by poor data quality– an issue that costs businesses an average of nearly $13 million per year, according to a 2021 survey by Gartner–or by inadequate means of sharing data between disparate systems.   

In other cases, an organization might be consolidating legacy systems and need to clean up and standardize its data in the process.

Establishing a centralized repository that acts as a single, trusted source of master data makes monitoring accuracy, standardizing formats, and synthesizing information much easier.   

When building out this data warehouse, it’s vital to recognize that as your data converges from multiple different sources, it is likely there are inconsistencies in the way product codes, company names, or location names are formatted across each of those source systems. 

To address this issue effectively, it’s important to link all your reference data to a canonical record seat. RecordLinker is designed to assist you in doing just that, providing a quick and user-friendly system that integrates seamlessly into your data management workflow without causing disruptions.

Implementing MDM can also have secondary benefits, such as reducing IT costs by eliminating the need for multiple siloed systems, and improving customer satisfaction by providing a single source of customer information that can be used across the organization.   

Like data governance, master data management requires the leadership of a cross-departmental team in order to be successful. But a key difference is in scope. Master data management is usually more focused on the data itself, while the data governance team is more concerned with the processes and policies around data.   

For instance, the master data team might be responsible for developing and enforcing rules around how master data is entered, while the governance team would be more focused on developing comprehensive policies for who can access and use the data. 

Woman behind screen showing data

In Summary

Ultimately, your decision regarding data governance vs MDM should be based on the specific business needs you are trying to address.   

If you are struggling with data quality issues, or need to establish a single source of truth for your organization to support decision-making and collaboration, master data management may be the right solution. Comprehensive data governance, on the other hand, is more likely to be the best strategy if you are looking to establish standards and compliance around your data.   

Both data governance and master data management are important approaches for organizations seeking to become more data-driven, streamlined, and efficient. By understanding the difference between the two, you can choose the right approach for your organization’s needs.  

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Interested in improving the quality of your data, but don’t have the time or resources to create a master data management program from the ground-up?  

RecordLinker is here to help. Our data integration and management platform can quickly connect your disparate data sources, identify and deduplicate records, and keep your data clean and up-to-date.