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).

The efficient management of data is necessary for sustainable success and growth. Across industries, organizations collect volumes of data daily, including customer profiles, transaction records, operational metrics, product information, and more. However, without organized management, this wealth of data can quickly transform into a liability, hindering decision-making and impeding progress.

Data Governance vs MDM: What’s the Difference?

Data Governance sets the rules and guidelines governing data usage, access, and security. It ensures data is accurate, consistent, and compliant with organizational standards and regulations. An effective data governance program will have clear policies and procedures in place to ensure data is managed in a consistent and controlled manner. It is often overseen by a dedicated board or steering committee.

Master Data Management (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. MDM 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.

  • Data governance is about rules, policies, protocols, access control, responsibilities, and culture around data.
  • MDM is more about actual technical execution and automation of data management for the most critical data assets.
  • They share a common goal of ultimately making your data accurate, consistent by proper maintenance and management.

The 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.

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What is Data Governance?

At its core, Data Governance serves as a foundation for effective data management strategies. It establishes the guiding principles and protocols governing the handling, utilization, and protection of data assets within an organization.

By establishing a robust framework of policies, procedures, and accountability structures, Data Governance ensures consistency, accuracy, and security across the data lifecycle. It dictates who has access to what data, how data is stored and managed, and the measures in place to safeguard against breaches or misuse.

The overall goal of data governance is ensuring data is accurate, consistent, and compliant with organizational standards and regulations.

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, especially in highly regulated industries such as financial services. 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.

Robust 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 feel over the top.

However, it is always worthwhile to keep your data highly organized. Avoiding messy data is essential for businesses to thrive and grow.

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What is Master Data Management?

Master Data Management (MDM) is a specific toolset for managing critical business data assets in a consistent and accurate manner. Unlike data governance, which focuses on the overall governance framework for managing data, MDM is more concentrated on the technical solutions and processes for managing master data, as well as automatically enforcing defined rules of how data should be handled..

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 repository, typically a 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 set. 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.

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Synergies between Data Governance and Master Data Management

Data governance and master data management (MDM) may seem like separate concepts, but in reality, they work hand in hand to ensure the effective management and utilization of data within an organization.

At its core, data governance establishes the rules and guidelines for managing data, ensuring its accuracy, consistency, and compliance with regulations. On the other hand, MDM focuses on managing critical data assets in a consistent and accurate manner, often through the creation of a centralized repository for master data. These are two sides of the same coin.

The synergy between data governance and MDM lies in their complementary roles in managing and maintaining data integrity.

Data governance may establish policies for data access and usage, while MDM implements ensures that master data is standardized, cleansed, and stored accurately in a centralized repository. By working together, data governance and MDM ensure that data is not only reliable and accurate but also accessible to those who need it, when they need it.

Furthermore, the collaboration between data governance and MDM enhances data quality and consistency across the organization. Data governance ensures that data quality standards are enforced throughout the data lifecycle, while MDM implements data quality and cleansing techniques to maintain the integrity of master data.

In essence, the synergy between data governance and MDM creates a strong foundation for effective data management within an organization. By aligning their efforts and leveraging each other’s strengths, businesses can ensure that their data is not only accurate and reliable but also compliant with regulatory requirements and aligned with organizational goals.

Suggested Reading About Data Management and Quality

Check our recommended reading list for heplful resources to help you establish good data practices in your organization. Proper data management is not simple – learn foundational concepts to discover helpful solutions to your data challenges.

Data Governance vs Master Data Management Wrapped Up

While data governance and master data management work great in synergy, your decision regarding data governance vs MDM ultimately 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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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.

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