What’s the Difference Between Data Management and Data Governance?

Understanding data and determining how to best use it to achieve competitive advantage and business success is the need of the hour for virtually every organization. As a result, users and stakeholders are keen to know how the data is stored and how to know if the data is timely and accurate. They need to know the answer to the following questions: Can we trust the data? What is the best data for my particular problem?

Fortunately, data management and data governance offer several means to organize data and answer such questions, which leads to two more questions: What’s the difference between the two concepts and can they be used interchangeably or together? Answering those questions is the focus of this article. 

What is Data Governance?

Data governance is basically a decision-making, monitoring and enforcement body that has authority over data management. For me, data management is the logistics of data, whereas data governance is the strategy of data. Overall, data governance is something that should feel more significant and more holistic in comparison to data management. Why? Because it is a crucial business program; governance requires policy, best reached by consensus across the company.

The original purpose of governance was to provide some tangible answers to how a company can determine, as well as prioritize, the financial benefits of data while mitigating the business risks of poor data. In addition, data governance requires:

• determining what data can be used in what scenarios;
• defining precisely what acceptable data is; and
• what is data, where is it collected and used, how accurate must it be, which rules must it follow, and who is involved in the various parts of data? 

Many readers might think that data governance is just limited to IT and stakeholders. Well, it’s not! Proper data governance ensures the safety, reliability and trustworthiness of all data across the enterprise. If each business silo approaches its data strategy differently, the end result is chaotic and, likely, not comprehensive enough to be useful.

Data governance includes an array of processes, practices and theories. It overlaps many data areas such as security, compliance, privacy, usability and integration. Of course, the end result may be some system that determines the decision rights and accountability of processes and individuals, such as which data processes are used when, and who can take certain actions under specific circumstances.

After all, it’s all about determining a holistic way to control data assets so that the company can get the absolute most value from the data.

Additional tip: One of the best ways to determine data governance is not simply with technology, but by making sure that the technology supports data governance through automation, scaling and augmentation. 

What is Data Management?

Data management is the control of data architecture, quality, security, policy, practices and procedures. Unlike data governance, data management is more basic in the sense that if you don’t have solid management in place, the rest of the data world won’t be within your reach. Often considered one of the best IT programs, data management’s objective is to organize and control your data resources so that it is accessible, reliable and timely whenever users call on it.

From an administrative perspective, IT teams are responsible for data management, and they rely on a comprehensive, customized collection of practices, theories, processes and systems – an entire suite of tools that on the whole collect, validate, store, organize, protect, process and more importantly maintain data. This also means that if the data is mistreated it can become corrupt or completely useless.

Related fields and categories include: data governance and data stewardship, data architecture, data quality management, data warehousing, business intelligence and analytics, metadata management and data security management.

In a nutshell, it’s time for organizations to recognize data as something inherently valuable and begin taking steps to develop a return on their data assets investment. More importantly, effectively using a combination of data management practices and data governance tools makes the best use of technology, people, policy and processes toward achieving better business outcomes.