CMMS and EAM Consulting Services

NRX Our Professional Services team provides CMMS and EAM consulting services to help our customers solve data quality, maintenance productivity, and asset visualization issues across multiple business scenarios. In combination with the powerful capabilities found in the NRX AssetHub platform and its modules, our Professional Services team is well-positioned to enable our customers to maximize their investment in our technology.

Every CMMS and EAM consulting engagement we undertake is staffed by an expert project team, typically comprising a Project Manager and a Technical Lead or Solution Architect. We also have the ability to assign Product Management Experts, Technical Consultants, and Solution Deployment Specialists to projects as necessary. Our team is globally dispersed and available to service customers in virtually any market.

Building and Repairing Data

Our Professional Services team leverages a proven customer engagement model to ensure successful adoption of our solutions.

A typical CMMS and EAM consulting engagement begins with a ‘discovery process’ that helps mitigate project and implementation risks by establishing a mutual understanding of requirements, existing business processes, and project success criteria. This process offers the opportunity to validate existing business processes, and identify additional asset and maintenance data requirements before designing and implementing a solution for an extended audience.


Discussion typically involves some of the following business processes:

  • Current project requirements, including goals and expectations regarding the building, repairing, or migration of asset and maintenance related data
  • Current processes used to acquire data from various resources, including Engineering, Spares and Materials, and Preventive Maintenance
  • Existing processes for building data (assembly of asset hierarchy; identification of asset criticality, classifications, and attributes; association of assets with spares and materials; association of assets with maintenance tasks and strategies; resourcing requirements, including the role of EPCs, data builders, and other contractors)
  • Existing processes for validating and deploying data in live or ‘staged’ CMMS applications
  • Gaining a full understanding of existing maintenance-specific or enterprise-wide data governance processes
  • Gaining a full, and mutual, understanding of KPIs for ongoing data sustainment

Following the discovery process, our team will deploy, configure, and provide training for the solution based on the requirements and solution design. Throughout the project, deliverables are measured based on prescribed metrics to ensure success criteria are met.

Managing and Approving Data

Foundational asset and maintenance data must be maintained at a high quality throughout the asset life cycle to optimize asset efficiency and support Environmental, Health, and Safety regulations. This is especially true if the data has been cleansed and enriched through recent data build or remediation initiatives.

Our team leads the effort in defining governance and data Management of Change (MOC) processes to ensure new data (or changes to existing data) are properly validated and approved prior to production use.


Streamlining Data Collection

We work with our customers to streamline data collection processes (from various data sources) and facilitate the delegation of approval requests to the proper subject matter experts. All processes are complemented by full audit trails so that revision and approval history are clearly visible.

Data Migration

Our Professional Services team provides expertise in planning data migration from legacy systems to Enterprise Asset Management (EAM) solutions.

With our software solutions, we provide the design and project plan to make sure that data is migrated in a complete, accurate, and efficient manner.


We help our customers with:

  • EAM Data Dictionary definitions
  • Data preparation, including data profiling, quality definition, and data cleansing
  • Data acquisition, transformation, and validation
  • Data transport
  • Ongoing data governance