Achieving CMMS Data Integrity for the Oil and Gas Industry

  • Achieving CMMS Data Integrity for the Oil and Gas Industry
  • Portugal - Lisbon

Achieving CMMS Data Integrity for the Oil and Gas Industry

Course Category : Oil & Gas Management

This programme provides a structured, practice-oriented approach to building and improving CMMS data integrity in the oil and gas industry, ensuring high-quality, reliable maintenance and asset data that support informed decision-making, risk reduction, and long-term operational performance.
Duration: 05 Training Days
Level: Intermediate.

Starts On

23 - November - 2026

Ends On

27 - November - 2026

Location

Portugal - Lisbon

Language

English

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View Course Introductory PDF

Targeted Audience

  • Maintenance personnel responsible for CMMS work-order and asset data
  • Inventory and warehouse teams managing spare parts data
  • Asset management teams in oil and gas organisations
  • Data analysts and reporting specialists in maintenance and operations
  • Cross-functional teams involved in CMMS upgrades or data migration projects
  • Engineers and supervisors seeking to improve maintenance and asset data quality

Targeted Skills

  • Understanding CMMS data structures in oil and gas environments
  • Applying best practices in data entry and integrity assurance
  • Designing and building robust asset registers with clear standards
  • Developing and coding spare parts databases for inventory control
  • Creating coding structures for failures, priorities, and work management
  • Building and refining PM data and job plans
  • Using tools such as Excel and Access for data build, review, and clean-up
  • Implementing data quality checks and validation routines
  • Managing legacy data migration and system upgrades
  • Establishing data governance and CMMS data standards

Expected Outcomes

  • Gain a clear understanding of CMMS data integrity and its impact on maintenance and asset decisions.
  • Diagnose common data build and data structure problems in CMMS environments.
  • Develop practical standards and guidelines for coding assets, spares, failures, and work orders.
  • Build structured asset registers using consistent naming, numbering, and attribute conventions.
  • Design spare parts databases that support effective inventory decisions and reduce waste.
  • Prepare and maintain PM data systematically linked to assets and tags.
  • Apply practical methods for data quality checking, data clean-up, and ongoing data improvement.
  • Use tools such as Excel and Access to support data build, review, and migration activities.
  • Support data migration and CMMS upgrades while maintaining data integrity.
  • Contribute to a data governance framework for maintenance and asset management.

Training Topics Index

  • Common CMMS data build problems in oil and gas
  • Scope of CMMS data and process interactions
  • CMMS version upgrades and critical data reviews
  • Using Excel and Access as data build and review tools
  • Introduction to Access tables, queries, and reports
  • Practical exercises in spare parts cataloguing

  • Approaches to criticality for equipment and spares
  • Equipment class and hierarchy concepts
  • Failure classes and shutdown codes
  • Work order priority codes and their use
  • Designing failure coding structures

• Practical workshop

building a failure code structure

  • Step-by-step asset register build process
  • Developing guides and standards for asset data
  • Naming and numbering conventions for assets
  • Maintainable groups and asset grouping principles
  • Defining equipment attributes and technical data
  • Performing asset data quality checks

  • Process for building spare parts databases
  • Developing standards for spares naming and numbering
  • Consolidating and rationalising spare parts data
  • Data quality checks for spares and inventory records
  • Determining inventory levels and reorder points

• Exercise

building a spares register linked to assets

  • Definitions and fundamentals of PM data in CMMS
  • Maximo versus SAP – common terminology and mapping
  • Developing generic PM strategy guides (including FMEA-based)
  • Writing effective job plans and task descriptions
  • Job plan formats, routes, and linking PMs to tags
  • Quality checks and practical exercises on PM data builds