Data Governance and Master Data Management. What is the difference and why do I need both?

Data Governance and Master Data Management (MDM) are both important components of managing an enterprise's data assets. While they have somewhat different goals and remits, they are complementary and work together to ensure that an organization's data is accurate, consistent, and secure. The close relationship between the two can often lead to confusion over which discipline is responsible for different areas of data management, and sometimes means that the terms are used interchangeably.

Let's start by defining what Data Governance and Master Data Management are:

Data Governance: 

Data Governance refers to the overall management of an organization's data assets. This is the process of managing the availability, usability, integrity, and security of the data. It involves establishing policies, procedures, and standards for data usage and ensuring that they are followed by everyone who interacts with the data. The primary objective of Data Governance is to ensure that data is properly managed and that it is used in a way that aligns with the organization's goals and objectives.

Some of the key components of Data Governance include:

• Data policies: These are formal statements that outline how an organization's data should be managed, who has access to it, and how it should be used.
• Data standards: These are established guidelines and rules that govern how data is collected, stored, and used across the organization.
• Data stewardship: This is the process of assigning ownership and responsibility for managing specific data elements within an organization.
• Data quality: This refers to the overall accuracy, consistency, completeness, and timeliness of an organization's data.
• Data security: This involves protecting data from unauthorized access, theft, or loss.

Master Data Management

This is the process of creating and maintaining a single, accurate, and consistent version of data across all systems and applications within an enterprise. It involves identifying the most critical data elements that need to be managed, and then creating a master data record that serves as the authoritative source for those elements. The primary objective of MDM is to ensure that these critical data elements are accurate, complete, and consistent across the enterprise.

Some of the key components of Master Data Management include:

• Data modeling: This involves defining the structure and relationships between different data elements and creating a data model that represents the organization's master data.
• Data integration: This involves integrating master data from various sources and systems to create a single, authoritative source of master data.
• Data quality management: This involves ensuring that the master data is accurate, complete, and consistent across all systems and applications.
• Data governance: This involves establishing policies, procedures, and standards for managing master data and ensuring that they are followed by everyone who interacts with the data.
• Data stewardship: This involves assigning ownership and responsibility for managing specific master data elements within an organization.

It is fair to say that there are several areas of data management in which both Data Governance and Master Data Management have a role to play. For example, defining data quality standards and policies would most likely fall under the remit of Data Governance, whereas assuring the integrity, consistency, and relevance of individual records is the responsibility of Master Data Management. Similarly, data stewardship also has a foot in each camp. While it is generally Data Governance policies that specify how data should be managed and maintained, it is Master Data Management platforms that provide the tools for data stewards to ensure that these policies are followed.

The main differences between Data Governance and Master Data Management are:

• Focus: Data Governance focuses on managing an organization's data assets as a whole, while MDM specifically targets critical data elements.
• Scope: Data Governance covers all data assets within an organization, while MDM is concerned only with master data.
• Objectives: Data Governance aims to ensure that data is properly managed and used in a way that is compliant and secure, and that aligns with the organization's goals and objectives. MDM aims to ensure that critical data elements are accurate, consistent and ready for use by all systems and applications.
• Processes: Data Governance involves developing and implementing policies, procedures, and standards for managing data, while MDM involves creating and maintaining a single, authoritative source of master data.
• Ownership: Data Governance involves designating ownership and responsibility for managing all data within an organization, while MDM enforces those roles and responsibilities for managing specific data assets.
  
  

Do I really need Data Governance and Master Data Management tools?

If you want to be able to use your data for value creation, and do so in a compliant and secure way, then the answer is yes.

Data Governance and Master Data Management are complementary disciplines in the sense that they both work towards ensuring the quality and integrity of an organization's data assets. Here are some of the specific ways in which they complement each other:

1. Data Governance provides the framework for MDM: A robust Data Governance framework provides the foundation for MDM. It establishes the policies, standards, and procedures for data usage that MDM relies on to create and maintain accurate and consistent master data records.

2. MDM ensures data consistency across systems: MDM provides a single, authoritative source of master data that is consistent across all systems and applications within an enterprise. This helps to ensure that data is not duplicated or inconsistent across different systems, which can lead to errors and inefficiencies.
3.  Data Governance ensures data security and privacy: Data Governance policies and procedures help to ensure that sensitive data is properly secured and that data privacy regulations are adhered to. MDM relies on these policies and procedures to ensure that master data records are secure and comply with data privacy regulations.
4. MDM enables effective decision-making: With accurate and consistent master data records, organizations can make better decisions based on reliable data. Data Governance ensures that the data is trustworthy, while MDM ensures that the data is accurate and consistent across all systems.

Benefits of implementing Data Governance and Master Data Management

Conclusion

Data Governance and Master Data Management are complementary yet independent disciplines of data management. Both have distinct areas of responsibility and roles to play within a data estate, and in practical terms, there is little overlap between the two. While Data Governance provides the overall framework within which Master Data Management operates, one doesn’t necessarily have to come before the other and either can work autonomously.

However, as with most technology fields, the real value comes from having a set of tightly integrated tools and systems that work together to deliver greater value than the sum of their individual parts. That is certainly the case with Data Governance and Master Data Management. Organizations are demanding more from their data than ever before – they want more insights, more intelligence, and as a result, more opportunities to grow the business. Meeting that need means that you can’t afford to waste valuable time and money wrangling with data that is of poor quality and difficult to access. In combination, Data Governance and Master Data Management can provide a reliable, trusted pipeline of data that is ready to deliver insight across the business, and that is what most organizations today need to succeed.