How IT Operations Can Play a Critical Role in Data Management

Don't underestimate the role of IT operations engineers in data management. Adopting these best practices can help ITOps simplify data management processes while maximizing efficiency.

Christopher Tozzi, Technology analyst

June 21, 2024

4 Min Read
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Enterprise IT operations engineers don't usually think of themselves as data managers. At most large organizations, data management responsibilities fall to dedicated data teams.

Yet the reality is that, whether data management is officially a part of ITOps engineers' job titles or not, IT teams play a critical role in helping to manage data — and the better they are at data management, the more easily their organization can make use of its data.

To that end, here's a look at ways that ITOps engineers can simplify data management, even if they aren't data experts per se.

What Is Data Management?

Data management is the process of collecting, storing, securing, and using an organization's data. It encompasses a broad set of tasks, such as:

  • Ensuring that data from disparate sources can be ingested into locations where it can be aggregated, analyzed, and stored.

  • Transforming data, meaning converting it from one form to another, to facilitate analysis.

  • Establishing data governance rules, which help ensure data quality and security.

  • Protecting data against the risk of data loss by backing it up and planning data recovery strategies.

The Role of Data Management in IT Operations

Since effective data management requires deep knowledge of different data architectures, transformation strategies, data security and privacy rules, and so on, enterprises typically hire engineers who specialize in working with data to be the primary leads in data management operations.

Related:7 Reasons to Pursue an IT Operations Engineering Career

However, IT operations engineers who are not data specialists also often play an important role in enabling effective data management. The reason why is simple: IT operations is responsible for implementing and performing many of the routines that support data management, as well as managing the infrastructure where data is stored and analyzed.

After all, most data management teams are not large enough — and do not have a broad enough understanding of generic IT operations — to perform all aspects of data management themselves. They might make high-level decisions, such as which data architectures to use and which data governance rules to adopt. But the implementation of data management policies often falls largely to IT operations engineers.

How IT Teams Can Simplify Data Management

This means that IT engineers should strive to work with data in ways that make it as easy as possible for their organizations to achieve data management goals. While best practices toward this end may vary from one business to another, the following guidelines can help ITOps contribute as effectively as possible to data management.

1. Document where data exists

First and foremost, IT teams should systematically track where data lives and what its purpose is. This practice should extend to all data within the organization that IT operations teams touch, not just assets that are already known to be important or are already being tracked. All data could potentially be valuable, but you can't derive value from data if you can't find it.

2. Document data lineage

In addition to tracking the location and purpose of all data within the organization, IT teams can enhance data management by tracking data lineage. Data lineage is information that records where data originated, how it has changed over time, and what may happen to it in the future. This information provides critical context that makes it easier to work effectively with data.

3. Consolidate data (where possible)

Since IT engineers are often on the front lines when setting up and moving data, they have a lot of control over where data ends up. They can use that control to simplify data management by consolidating data wherever possible. Consolidation means moving disparate data assets into central locations, where they are easier to track, manage, and secure.

Not all data can be consolidated, of course. Some data may not be able to exist in the same place as other data. But when consolidation is possible, it makes data simpler to manage.

4. Integrate data management into disaster recovery planning

When it comes to disaster recovery planning, data management needs are not always a priority. The main focus of IT teams during disaster recovery planning is often on getting systems back up and running as quickly as possible.

But by factoring in data management goals — such as the data security and governance policies that an organization adopts — IT engineers can help ensure that rapid recovery doesn't come at the expense of data management. You don't want to restore lost data in a way or to a location that makes it insecure, for instance, because you failed to take data management into account when making disaster recovery plans.

5. Track data management cost and effort

Knowing how much it costs — in terms of both infrastructure expenses and personnel time — to implement data management plans is critical for maximizing the ROI of data management. This makes it important for IT engineers to track their time when working with data. They should also monitor the costs of data infrastructure they set up — not just to help with data management ROI, but to help control infrastructure costs in general.


Data management isn't a job for data experts alone. IT operations teams have a key role to play in the day-to-day implementation of data management goals. Integrating data management into IT operations is an essential step toward helping businesses get more from their data with less time and effort.

About the Author(s)

Christopher Tozzi

Technology analyst, Fixate.IO

Christopher Tozzi is a technology analyst with subject matter expertise in cloud computing, application development, open source software, virtualization, containers and more. He also lectures at a major university in the Albany, New York, area. His book, “For Fun and Profit: A History of the Free and Open Source Software Revolution,” was published by MIT Press.

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