It’s no secret that businesses are using their abundance of data to generate insights that will advance their position in the marketplace or to make strategic decisions that will enhance value for their customers. Businesses that get the most out of their data aren’t those that happen to collect the most data, but those who do the best job of controlling the data they collect.
Many mechanisms define the act of data control. The two most talked about – and most easily confused – are data management and data governance.
Some organizations consider data governance as a component of data management. Others give governance a higher rank, suggesting that data management carries out governances’ policies. So, what’s the truth? The truth is that the model works both ways. Data governance and data management are two separate terms that cover different functions. They also work together to ensure that enterprises make the best possible use of their data.
What is data management? The term is best described as the management of all architectures, policies and procedures that serve the full data lifecycle needs of an organization. It is an IT practice that aims to make sure that data is accessible, reliable, and useful for individuals and the organization. The term can also refer to broader IT and business practices that enable the use of data in the most strategic way possible.
Key aspects of data management include processes connected to data preparation, the data pipeline, and the data architecture. It’s critical to prepare data to make it usable for analysis. The pipeline pulls the data from various sources and loads it into storage options, such as a data warehouse, data lake, or cloud data platform. The data architecture defines the formal flow of data across its lifecycle.
Where does data governance fit in? While data management focuses on practices, data governance is about rules. The rules determine the appropriate use, handling, and storage of data. Data stewards set guidelines for who owns which data sets internally and who is authorized to access, edit, and circulate them. Governance rules also spell out how organizations secure data and comply with ever-increasing government regulations.
A well-designed data governance program includes several teams – usually one that oversees governance, a governing committee, and data stewards. They set standards and procedures for important matters ranging from data quality to data stewardship to data transparency.
The differences are clear: data governance charts out a broad set of policies implemented across an organization, and data management puts those policies – along with other best practices implemented along the way – into action. Data management is the execution, and data governance is the roadmap that guides the execution.
How data management and data governance work together
Looking at these terms another way, they complement each other like components in a landscaping project. Data governance scopes out the project – how it is going to look, what materials to use, and who is on the project team. Data management carries out the work. You could build a house without a blueprint, but the possibilities of making mistakes and overseeing critical aspects of building a home will run high. As a result, it will take much longer to build at great expense. Data management and data governance work together to maintain and protect data through a blend of processes and policies. The following are examples of the two concepts in action:
Your data governance policy could require that organizations keep customer data on-site for 10 years to meet regulatory requirements. Implementing data management processes can ensure that data is archived and deleted in a systematic manner.
Data management and data governance can also ensure proper data access. If a data governance policy dictates that only employees who need personally identifiable information (PII) to perform their jobs can access it, a data management process can grant role-based access to employees with appropriate authorization.
Organizations need to optimize their use of data to take advantage of digital transformation initiatives, machine learning, artificial intelligence, and other emerging technologies and practices. Creating sound data management and data governance practices provides control over data assets that will go a long way toward contributing to future success.