Understanding the difference between data governance and data management can be confusing because of how often they’re used interchangeably. While they are closely related, they are distinct concepts with their own objectives and responsibilities that work hand-in-hand to create the most effective data strategy. Understanding the difference between data management and data governance is crucial for businesses that want to ensure their data is organized, accurate, and secure. In this article, we will define both data management and data governance to better understand how they are different and how they rely on one another to ensure data is effectively managed, protected, and used in accordance with established policies and procedures. 

What is Data Governance?

Data governance establishes a framework for data management by defining policies, procedures, and standards that ensure data is accessible, protected, and ethical.

The goal of data governance is to develop a strategy that will ensure data is managed in a way that meets the needs of the organization while complying with legal and regulatory requirements. This includes defining ownership of data, how data will be collected, stored, and analyzed, who can access restricted data, and how it will be protected.

Data governance consists of data security, data stewardship and data transparency but the two main components of data governance are data quality and data regulation. Data quality ensures data is correct, relevant, and useful to prevent inaccurate insights, and inefficient operations. Data regulation refers to the policies enforced by government entities for organizations to protect the private data of individuals. Each of these are crucial to a comprehensive data governance strategy and you can read more about these concepts in detail through the resources below.

How to Maintain Data Quality in Your Organization

What is Data Regulation and How Do I Make Sure My Data is Compliant?

What is Data Management?

Data Management is a generic term that can mean several different things depending on the context.  It’s often used as a catch all phrase to refer to all solutions related to managing data. In the case of understanding the relationship with data governance, we are specifically talking about Master Data Management or MDM.

Data governance without implementation is just documentation, that’s where Master Data Management comes in. Data governance defines the rules and processes for creating, updating, and deleting data, and MDM enforces these rules to ensure data is managed in a consistent, secure, and compliant manner. This helps to establish data that remains accurate and consistent over time, even as new data is added, or old data is updated.

Master data management aims to create and maintain a single source of truth that will ensure all stakeholders have access to consistent and accurate data. MDM can consolidate data from disparate systems, standardizing and cleansing the data, and creating a master record for each entity. The master record contains all the critical attributes of the entity and serves as the single source of truth for that entity across the organization.  This helps to eliminate data silos, data inconsistencies, and data conflicts that can arise when different departments or systems use different versions of the same data.

The Differences Between Data Management and Data Governance

‍Master data management focuses on the technical aspects of managing data, such as collecting, storing, and processing data and data governance is a strategic function of data management that provides the overarching policies and rules that govern how master data is managed and ensures that MDM processes align with the organization's data governance framework.  They each have their own capabilities and while they are considered their own unique solutions, they work in tandem and rely on one another to achieve the best results.

The most notable difference between data governance and master data management is the unique ways they leverage the power of man and machines. Establishing the framework and policies of data governance requires critical thinking, decision making and expertise that can only be accomplished by a team of expert human minds. To enforce these policies, MDM harnesses the power of technology and machines to automate and monitor data with the smallest amount of human error.

When establishing data governance and data management it is important to consider how the execution of each of these will impact your organization.

Data governance involves the work of executives, data stewards, compliance officers, and legal teams, more importantly, because data is used across an entire organization, the policies established through data governance are influenced by every data consumer in the organization.

MDM involves data architects, database administrators, data engineers, and other technical staff who are responsible for executing data management processes, such as data integration, data cleansing, and data storage, but the result of the implementation of these processes will affect all data consumers throughout the organization.

Data governance is a critical part of data management, without it, a data management strategy would have no framework for standardized procedures, which can expose data to breach, inconsistencies, and privacy violations. On the other hand, MDM is a critical component of data governance. Data governance without master data management is just documentation. Data management implements the policies and procedures established by data governance to manage data throughout its lifecycle. Consider data governance the recipe, and data management is cooking. Without a recipe, you have no reference for what you’re making or how to make it, and if you never follow the recipe, you’ll never bring the dish to life.

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By working together, data governance and data management help to ensure data is effectively managed, protected, and used in accordance with established policies and procedures. This, in turn, helps to improve decision-making, reduce risks, comply with regulations, increase efficiency, and support innovation.

Conclusion

While the terms are often used interchangeably, we can see how data governance and data management are two unique functions that work together to create accurate and managed data. Data governance is a critical part of developing an effective data management strategy, but without master data management, it would never be enforced.

Understanding the difference between data management and data governance and how they work together can help your organization develop a comprehensive data management strategy that meets your needs while complying with legal and regulatory requirements.

Onebridge has over 18 years of experience developing data governance and data management strategies for our clients. Our consultants can help you create a framework for managing your data that is uniquely tailored to align with your business goals and objectives.  We partner with industry leaders like Profisee to enforce these rules to ensure trustworthy data that is accurate, complete, and consistent.

‍Contact us to learn how Onebridge and Profisee can help you build and effective Master Data Management solution for your organization.

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