What is data management?

Data management is the full administrative process by which data is acquired, validated, stored, protected, and processed, and by which its accessibility, reliability, and timeliness are ensured to satisfy the needs of the data users. Put another way, data management is how you manage all aspects of your data.

The goal of data management is simple: to help organizations and people optimize the use of data within certain boundaries of policy and regulations to make decisions and take actions that benefit the organization. Data management spans a wide spectrum, but among the core functions?  Creating and updating data across an organization, storing that data across multiple platforms including the cloud, using the data for any number of purposes, and securing the data.

Policy management automation platforms improve operational efficiency and significantly mitigates risk by enabling policy and legal teams to systematically reduce the potential for reputational damage. Ultimately, policy management solutions enable organizations to build an ethical and defensible compliance program.

What are the key building blocks of any program meant to collect, protect, and manage an enterprise’s data?

Data quality: Making sure the data is usable and accurate.

Data integration: How different data sets are integrated.

Data governance: A set of rules to manage the data.

Data usage: Guidelines for the use of your data.

Data security: How will your data be protected?

Why is data management needed?

Quite simply, data management is needed because organizations need a framework or structure to manage all the data they have. Data management provides that framework, guiding organizations and individuals on how to properly use the data for the organization.

Such a framework is needed because of the important data plays throughout society. It’s the lifeblood of many organizations. And there’s lots of it. Over 2.5 quintillion bytes of data is created every day, and that data is the lifeblood of many organizations. And if an organization doesn’t manage its data well, it’s just information floating around a computer system.

What is data governance?

Data governance is a key component of data management. Data governance determines and defines the collection of practices and processes which ensure the formal management of data assets within an organization. The Data Governance Institute defines it as a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods. If data management is the complete structure that protects and produces good data, then data governance produces the principles from which that good data is born.

However, the definition of data governance largely depends upon the context in which it is used. The term “data governance”, can refer to a variety of things, from organizational bodies to the rules, standards, and guidelines implemented by a company to determine decision rights around data. In many cases, data governance is particularly focused on data hygiene processes and practices, and ensures data is consistent and trustworthy for all users.

The rise of data governance

In 2018, the Wall Street Journal proclaimed that there was a “global reckoning on data governance.” Massive data breaches across numerous industries resulted in an erosion of confidence and loss of trust among governments and the public. That, combined with the rise of privacy laws like GDPR, caused many companies to look anew at their data governance policies. Since then, data governance is no longer just an option. It’s a best business practice driven by a raft of new privacy laws like the California Consumer Privacy Act.

Today, a strong and effective data governance policy does more than govern how data is managed. Many organizations are thinking of data governance as a way to manage risk and are integrating risk management principles into their data governance policy. Such a linkage between risk and data only makes sense. Data and all that it encompasses are part of an organization’s risk profile and those organizations can use their data governance policy to mitigate that risk.

What’s driving the need for data governance?

  1.  Cyberattacks: Data breaches are on the increase
  2.  Increased regulation: Governments are eyeing more regulations
  3.  Cost: Saves money by clarifying rules around core data
  4.  Liability: Bad data governance in inherently risky

The differences between data management and data governance

The differences between data governance versus data management can be boiled down to scope. Data management is broad in scope, concerned with all aspects of how data is managed, from how it’s acquired to how it’s used, secured, and everything in between. The data governance scope, on the other hand, is just concerned with how the data is governed, including but not limited to the availability, usability, integrity, and security of data.

A useful way to look at the difference between data management and data governance is by thinking of data management as an overarching umbrella term for all practices related to the development, execution, and supervision of data and information. And underneath this umbrella is data governance.

Data governance defines the rules of data management, so it provides guidance about under what conditions that “umbrella” should be opened.

Working together to strengthen an organization

Data governance enhances and makes data management stronger by imposing a set of rules and policies for how an organization’s data is governed and protected. Without that framework for data governance woven into their data management, organizations open themselves up to greater risk and liability. But by having data governance and data management work together, the organization is better protected against risk and liability in the event something does happen to their data.

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