Wednesday, June 12, 2024

Data Governance vs. Master Data Management: Key Differences

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Data is an invaluable resource for decision-making and strategic planning across industries, but the sheer volumes can strain resources and infrastructure if it’s not stored and managed properly. Enterprises often employ data governance and master data management strategies as part of a larger data management effort to meet that goal.

Data governance is a holistic approach to your data—at its core, it refers to the set of rules and permissions by which the data is orchestrated and accessed. Master data management (MDM) is the overlap of technology and business operations to enforce the policies of data governance, ensuring the uniformity, accuracy, and accessibility of data.

This article explores the differences between data governance and master data management, where they overlap, and how they can be used together to help businesses ensure their data is accessible, reliable, and secure.

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The Challenge of Managing and Governing Data

The challenges of managing and governing data go beyond the cost of storage to retain the massive volumes that businesses have come to rely upon for decision-making, operations, and competitive advantage. They also include security—through policies like the European Union’s General Data Privacy Regulation (GDPR), the California Consumer Privacy Act (CCPA), and the Health Insurance Portability and Accountability Act (HIPAA), the regulatory landscape has redefined the baseline for adequate data protection and management practices.

Maintaining data quality is another concern. The accuracy and reliability of data are critical. If either suffers, the decisions they inform aren’t grounded in truth.

The intertwined concepts of data governance and master data management serve distinctive roles. Differentiating between them is essential to mastering your data landscape.

Learn the 10 Best Practices for Effective Data Management.

What is Data Governance?

Data governance is a holistic approach to the how, why, and by whom of the data in the care of your organization. At its core, data governance refers to the set of rules and permissions that determine how the data is handled and who gets to access it. These rules have an effect on the data’s availability, usability, integrity, and security.

Not just a technical endeavor, it’s also directly related to business processes and organizational culture. Think of data governance as a rulebook, dictating processes and policies to ensure the data is strictly accessed by authorized individuals and isn’t used in any way that could harm the organization, its stakeholders, or clients.

The Core Principles of Data Governance

While every organization’s data governance policies will depend upon its needs and goals, here are the core principles they should follow:

  • Data Quality—Ensures the data is maintained to be as accurate, reliable, and actionable as possible.
  • Data Access—Makes the data readily available to all who need access to it, creating a streamlined process for access requests and approval procedures.
  • Data Privacy—Protects the data and personal information of clients, employees, and stakeholders.
  • Data Protection—Maintains the security of the data from potential threats and malicious actors using security measures to protect data assets.
  • Regulatory Compliance—Ensures that the data handling methods meet legal or jurisdictional requirements.

Benefits of Data Governance

In addition to helping organizations improve the processes around the core principles listed above by improving quality, enhancing security, and ensuring regulatory compliance, data governance also helps in other areas of the business.

For example, more accurate and reliable data better informs decision-making at all levels, leading to better outcomes. It also increases the agility of the business—in fluctuating market conditions, organizations are able to pivot more readily when they have a solid grip on their data.

Data governance isn’t purely an IT strategy. It’s an organizational imperative that ensures data, in all its forms, is treated with the importance and security it entails.

What Is Master Data Management (MDM)?

Master data management (MDM) is the overlap of technology and business operations to enforce the policies of data governance, ensuring the uniformity, accuracy, and accessibility of the enterprise’s data. By consolidating the data, MDM ensures it’s consistently synchronized across the various systems, bridging data islands to create a seamless flow of up-to-date data and information.

MDM combines software and other tools, processes, and the mechanisms outlined by an organization’s data governance policies to ensure that master data is governed and coordinated across the enterprise with high accuracy and integrity.

Components of MDM

MDM isn’t a single entity, but a combination of multiple technical and operational components. Each of the following plays a role in the data management process:

  • Data Integration—Integrates incoming data from legacy systems, CRMs, ERPs, and external data feeds into a central repository to offer a comprehensive view.
  • Data Quality—Ensures that the consolidated data maintains its quality by removing duplicates, rectifying errors, and filling in gaps to ensure a complete set.
  • Data Governance—Controls who can access the data, how it’s categorized, and how often it gets updated.
  • Business Process Management—Updates data seamlessly as processes change (for example, during a product launch or merger).

Benefits of Master Data Management

Master data management helps organizations by creating a single, reliable repository of information. This means higher quality data that’s devoid of redundancies and inaccuracies. It also streamlines business processes by consolidating the data and providing a unified view to remove roadblocks or discrepancies and allow for smoother operations in the supply chain, finance, and consumer relations management.

With reliable access to data-backed decisions, business leaders can craft strategies that are grounded in reality and aligned with goals and aspirations. MDM can also increase operational agility by ensuring businesses are highly adaptable, using real-time data access to shift strategies and responses as needed.

Master data management is not just an IT tool, it’s a strategic imperative. As organizations grapple with growing data complexities, MDM stands out as the beacon, ensuring data coherence, quality, and agility.

Data Governance vs. Master Data Management: Key Differences

While largely interconnected, master data management and data governance serve different facets of the data ecosystem. Here’s a look at what sets them apart.

Purpose and Objectives

Data governance focuses primarily on policies ensuring data integrity, security, and overall quality, treating it as one of the organization’s valuable assets. MDM, on the other hand, zeroes in on the various fragments of the data and how each of them should be precisely managed for comprehensive and reliable access to the data around the clock.

Processes Involved

The policies created by data governance are enforced organization-wide, often taking into account regulatory compliance. MDM is more process-centric, delving into data integration, quality assurance, and aligning data with business operations.

Stakeholders and Responsibilities

Data governance includes data owners, stewards, users, and stakeholders. It takes their input, both directly and indirectly, as to how the data is going to be kept accurate and safe from privacy violations. MDM, on the other hand, is solely concerned with input from the IT and business operations, allowing them to integrate data sets into necessary processes, upholding data standards, and refining business processes.

Role of Technology in Data Governance and MDM

Technology stands at the forefront of efficient data governance and master data management, and is often the catalyst for their effectiveness.

Organizations have access to specialized tools and platforms, allowing them to amass large amounts of data from varied sources while maintaining its integrity and long-term usefulness. But beyond the collection and storage of data, businesses need technological solutions to help them process and dissect data and extract actionable insights in real-time.

Combined with the advancements of processing algorithms and machine learning, the automation of repetitive tasks within data governance and MDM has become more affordable, making it more accessible to smaller organizations with limited resources.

The use of technology in data governance and management doesn’t only streamline operations—it also significantly reduces the rates of human error. On the security front, technological solutions allow for robust encryption of the data, alongside a myriad of protective and mitigative measures. This ensures the data remains shielded from potential breaches and leaks. It’s further aided by monitoring and reporting software that keeps an eye on the data both in-rest and in-transit at all times.

Can Data Governance and Master Data Management Work Together?

The fusion of data governance and MDM can lead to maximized outcomes. This is most evident when considering the overlap between the need to set and enforce solid policies while managing the data for processing and accessibility. Data governance and MDM are not only complementary—they thrive when implemented in unison.

Combining both concepts allows organizations to come up with frameworks that emphasize best practices while championing clear roles that uphold data quality and integrity. Through such integration, organizations can foster a seamless and efficient data management strategy that leverages the strengths of both disciplines.

Bottom Line: Data Governance vs. Master Data Management

Data governance is about managing data as an asset, while MDM is about creating a single, unified view of data. Despite the unique approaches of data governance and master data management and their myriad differences, they’re closely related and perform better when combined.

By understanding the key differences and similarities between these two disciplines, you can make more informed decisions about your data strategy and achieve better outcomes that allow you to effectively manage and govern your data, refining raw material into valuable insights that drive business growth and innovation.

Read about the 10 Best Master Data Management Tools for 2023 to learn how enterprises are implementing their own MDM strategies.

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