Defining Data Management

By , Friday, November 18th 2022

Categories: Analyst Blogs

Data Management’s Expanding Requirements

The term “Data Management” has become a popular phrase within the IT industry over the past few years, and for good reason. When considering massive data capacities, data sprawling out from the datacenter to the edge and multiple public clouds, increases in ransomware and other cyber threats, and new data compliance regulations – data clearly needs some type of management.

Many organizations have implemented functionality, such as backup or archive capabilities, that they consider to be data management. The challenge however, is that with increasingly complex data environments, requirements for data management are expanding, and many organizations may be falling short of a comprehensive data management plan. But this begs the question, what does Data Management really mean? Is it a product? A practice? A feature? Is it data protection? Is it data security? Or is it something else entirely?

The short answer is “yes”. The longer answer requires a bit more examination. Data management can include a wide range of functionality and span across various areas of IT. It has expanded beyond a simple feature definition, such as backup and archive, to include an extensive number of areas across increasingly complex environments. The expanding need for data management includes broad categories such as data protection, security, governance and compliance, lifecycle management, data movement, storage optimization, data availability, data visibility, and others, each of which involve their own complexities.

There are a number of products and services that claim to be “Data Management”, often offering different features and capabilities related to one or more of the categories listed, but in general, a specific feature or functionality is likely too narrow of a scope to properly define data management. Data management also surpasses the classification of a “product” and extends to the data practices and strategies implemented by an organization. Specific data management practice and feature requirements will differ between organizations and they may change over time. The key for organizations is to have a combination of technical functionalities, insight into their data, and organizational practices to achieve a broad mixture of data management requirements that fits their data’s needs.

What is Not Data Management?

As a hot topic in the industry, and perhaps a bit of a buzz-word, the tag of “Data Management” has been associated with a large number of products and features – some of which may be misleading. Often times a feature that may make up a single portion of a larger data management strategy may be marketed as a full data management solution. This only leads to further confusion on what data management really means as requirements continue to expand.

For example, backups are not data management – backups are backups. They are a part of a data protection strategy – which can factor into an overall practice of managing data, however this point can often become confused as the term data management becomes a fancy new way for data protection to be marketed to customers. With that said however, it should be emphasized that backup often goes hand in hand with additional data management functionalities, and some data protection vendors have expanded their offerings to provide many of these features. The point remains however, that backups alone simply do not qualify as data management.

The same can be said for a number of other areas such as security, performance monitoring, archiving, or data classification. These are all individual functionalities, that while important, shouldn’t be considered data management on their own. But when organizations begin combining features such as these, and others, they can achieve a greater level of control and understanding of their data – which ultimately leads to data management.

Understanding and Control of Data

To say that data is being “managed” implies that the data can be controlled or handled in different ways as needed. This involves the ability to take required actions on data such as backing up data, archiving or deleting old data, maintaining data availability, moving data, or hardening data’s security to protect from threats. It is this ability to take various actions on data as needed that begins to resemble data management. Different actions may be associated with one or multiple products, and the requirements for specific actions will vary between organizations and their individual needs. But, a simple ability to manipulate or control data is still missing a key component: understanding the data.

In order for any of the functionality above to make a meaningful impact, the data needs to be understood. Taking the above actions on data without an understanding of the data would be aimless, and potentially disruptive. Imagine the scenario of archiving data without an understanding of what the data is. How would the organization know which data should be archived? What if they archive data that is critical to day-to-day operations?

By identifying and tagging metadata, context can be given to data, allowing it to be understood. In the same archival scenario, additional information such as the age of the data, frequency of use, and even custom tags explaining the purpose of the data can be used to make informed actions. This insight into data becomes increasingly valuable as organization’s need to understand increasingly large amounts of data, often spread across multiple different locations.

It is this combination of understanding and action that forms the basis of data management. Associated metadata can be used to provide an understanding of data, which can then be used to intelligently take actions and inform the organization’s data management practices and strategy.


Data management is a broad category and the definition has expanded to address increasingly complex data environments and requirements. It is an umbrella term that can represent a number of different features and functionalities, and it may be represented by one or multiple products. While data management can be used as an overdone marketing term, and in some instances may misrepresent what should be considered specific features or areas within the data management umbrella, it is a crucial tool for organizations to maintain increasingly large and complex data environments. Data management requires visibility and understanding of data, as well as the technical abilities to take action based on this understanding. It also involves the practices and procedures set within an organization to leverage this knowledge and decide which actions may be necessary.

Evaluator Group covers data management technologies within the Multi-cloud Data Management segment. This is a technology area that examines solutions with a mix of data management features across hybrid and multi-cloud environments. More information regarding Evaluator Group’s coverage of Multi-cloud Data Management can be found in the Evaluator Group Research Library.

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