Distinguishing data governance from data management in this day and age is more complicated than it used to be. There are a few reasons why, but the main one is the advancement of technology and business. With every passing year, businesses and institutions require more data to keep running efficiently. What’s more, data is essential to success nowadays.
However, with so much data to use and keep track of, it has become crucial to come up with the best practices of managing it. Furthermore, these processes need to be kept as efficient as possible to ensure the safety and relevance of the data being used. Let’s have a closer look at both the process of data governance and data management to be able to distinguish between them:
What Is Data Management?
Data management is a broad term, no matter where you find its definition. According to the data management professionals organization, DAMA International, data management encompasses both the development and execution of practices and policies for managing the full data life-cycle within an enterprise. This definition makes it clear that data management also encompasses data governance, as it describes the practices that data governance needs to implement.
These aren’t the only data-related disciplines, but they are the ones most frequently confused. For example, data quality management is one aspect of data management, but all of them are tied together by data governance. To better understand what data management is, it’s important to learn more about data governance, as its definition is narrower.
What Is Data Governance?
Data governance is more about the strategy of managing data: the implementation of practices that data management outlines and suggests. In essence, governance is what puts the logistics of data management to practical use within an enterprise. There are certain questions to answer with data governance, such as who has access to data, who is involved in gathering data, what data is used for which processes, what are the rules of handling and storing data, and how accurate does it need to be used.
As an enterprise, you’ll need to define and create certain rules to truly put your data governance practices to work. The first thing is creating a glossary of data, as well as data quality definitions, that will provide a framework within which data managers can work. Furthermore, you need to create enough metadata to link your data implementation to technical processes, as well as create governed catalogs of data.
Improving Both Processes
Enterprise data quality is of the highest importance for any organization or business. That’s why, as two of the most significant disciplines of managing data, data governance and data management need to be implemented together. Only that way will you have a comprehensive strategy to use all the data your enterprise gathers effectively. With Runner EDQ’s software integrations solutions like CLEAN_Industry, you can unite your data management and data governance practices and not worry about data quality ever again.