What Is Data Operations—and Why Does It Matter for Maximo?

An asset record loads successfully. But is it complete? Does it reference the right location? Were any records rejected—and does someone know why?

For organizations using IBM Maximo, these questions matter. Data supports maintenance planning, work execution, inventory management, and asset decisions. When that data is incomplete, inconsistent, or difficult to manage, the impact extends into daily operations.

Moving data is one task. Managing the process around it is an ongoing responsibility.

That is where Data Operations comes in.

What Is Data Operations?

Data Operations is the discipline of managing how data is prepared, moved, checked, and maintained so people and systems can use it reliably.

The related term DataOps describes collaborative practices that bring together people, processes, and technology to improve data delivery. It draws on approaches such as automation, continuous improvement, and shared responsibility.

For Maximo teams, these principles translate into practical questions:

How do we prepare data before loading it?
Can we repeat the process without rebuilding it?
How do we identify failed records and correct them?
Can we see what happened during each run?
Who is responsible for resolving issues and approving the results?

A strong Data Operations approach makes those answers part of a defined process.

Why Does It Matter for Maximo?

Maximo data represents connected operational information. Assets belong to locations. Work orders reference assets. Inventory records support maintenance activities.

Two engineers in hard hats and hi-vis vests reading a tablet at a substation — Assets, locations and work orders depend on each other, in the field and in Maximo.

IBM Maximo’s integration framework uses object structures to organize data for inbound and outbound processing. These structures provide a common data layer for exchanging information with Maximo.

That makes data movement more involved than transferring a spreadsheet. Teams need to account for the destination’s requirements, the relationships between records, and the sequence in which information is loaded.

For example, if an asset references a location that has not been created in the destination, the load may fail. The team must identify the issue, address the missing prerequisite, and determine which records need to run again.

Without a clear process, that work can become a cycle of manual investigation, corrections, and repeated uploads.

Where Data Operations Makes a Difference

1. More repeatable migrations and modernization projects

During a migration or MAS modernization initiative, teams may need to run data workflows multiple times for testing, corrections, and final deployment.

A defined, reusable process helps teams carry lessons from one run into the next. It also makes it easier to assess readiness before the final move.

Data Operations does not eliminate the need for planning. It gives teams a more consistent way to execute that plan.

2. Better visibility into data issues

A completed job does not necessarily mean every record was accepted or that the resulting data meets business expectations.

Control room of a power plant with long panels of gauges and switches — Teams need to see what ran, what failed and what still needs attention.

Teams need to understand what succeeded, what failed, and what requires attention. Relevant checks before loading, combined with record-level results afterward, help them investigate issues earlier and focus corrections.

Technical acceptance and business accuracy both matter. A record can meet system requirements and still contain information that an asset or maintenance team needs to review.

3. Less dependence on manual work

When each data task requires a new script, spreadsheet adjustment, or specialist intervention, routine work becomes difficult to scale.

Reusable workflows and documented handling rules can reduce repeated effort. They also make the process easier to hand over, support, and improve as requirements change.

4. Greater accountability and traceability

Teams need a clear record of data activity: what ran, what the results were, and which issues remain unresolved.

That visibility supports troubleshooting, project reviews, and governance. It helps teams explain the outcome of a data movement process and make informed decisions about the next step.

5. A stronger foundation for operational decisions

Incomplete asset information can complicate maintenance planning. Inconsistent classifications can weaken reporting. Missing relationships can make records harder to interpret.

Managing data more consistently helps organizations build confidence in the information behind their operational decisions.

What Does This Look Like in Practice?

Consider a team moving asset and location data into a new Maximo instance.

Two technicians in hard hats kneeling beside equipment, one holding a tablet — Each run should be understandable, repeatable and easier to manage.

A Data Operations approach would include:

Define the scope: Identify the records, fields, and destination requirements.
Prepare the workflow: Establish mappings and the required load order.
Check the data: Apply relevant checks and resolve known issues.
Execute and review: Run the load and inspect successful and rejected records.
Correct and repeat: Address failures, rerun as needed, and review the resulting data with business owners.

The goal is to make each run understandable, repeatable, and easier to manage.

How MAS Data Hub Supports This Approach

Highstep’s MAS Data Hub is a data operations platform purpose-built for IBM Maximo.

It supports repeatable data workflows, extraction and loading through Maximo APIs, selected validation checks, record-level results, error tracking, and an audit trail of data movement. Teams can manage workflows across development, test, and production instances.

These capabilities provide greater visibility and control over the execution process. Teams still need to define business requirements, plan dependencies and load order, and review data accuracy.

Make Data Readiness Part of Your Maximo Strategy

Whether you are preparing for a migration, modernizing Maximo, or managing recurring data loads, the process deserves the same attention as the destination.

Start by asking:

Can we repeat our workflows reliably?
Can we explain the results?
Do we know how to resolve failures?

The answers reveal where stronger Data Operations can help.

Planning a Maximo migration or MAS modernization initiative? Connect with Highstep to explore how MAS Data Hub can support your data operations.