Maximo Data Quality: Why It Matters and How to Fix It

Why Maximo Data Quality Matters

IBM Maximo is one of the most widely deployed enterprise asset management (EAM) systems in Australia, used across transport, utilities, defence, mining, and government sectors. Yet many organisations struggle with a fundamental problem: the data inside Maximo is unreliable.

Poor data quality in Maximo undermines every downstream process—from maintenance planning and reliability analysis to capital investment decisions and regulatory reporting. When your asset register is incomplete, your failure codes are inconsistent, or your preventive maintenance schedules are misaligned, the consequences compound across the organisation.

Data quality is not a technology problem. It is an asset management problem. And it requires an asset management solution.

Common Data Quality Problems in Maximo

Incomplete or Incorrect Asset Hierarchies

The asset hierarchy is the backbone of Maximo. It defines relationships between systems, assets, and components, enabling roll-up reporting and structured maintenance planning. Common issues include:

• Missing hierarchy levels — Assets registered at the wrong level (e.g., components registered as standalone assets with no parent)
• Inconsistent naming conventions — The same type of asset described differently across sites or business units
• Orphan records — Assets that exist in the register but are not connected to any location or parent asset
• Duplicate records — The same physical asset registered multiple times, often after organisational changes or system migrations

A poorly structured hierarchy makes it impossible to aggregate failure data, compare asset performance across sites, or build meaningful reliability models.

Inconsistent Failure Codes

Failure coding is critical for reliability analysis, root cause investigation, and predictive maintenance. However, many Maximo implementations suffer from:

• Too many codes — Failure code libraries that have grown organically to hundreds or thousands of entries, many of which are duplicates or overlapping
• Ambiguous descriptions — Codes like "Other" or "General Failure" that provide no analytical value
• Inconsistent usage — Different technicians coding the same failure differently due to lack of training or unclear definitions
• Missing codes — Work orders closed without failure codes, creating gaps in the reliability dataset

Misaligned Preventive Maintenance Schedules

Preventive maintenance (PM) schedules in Maximo should reflect the actual maintenance strategy for each asset. Common misalignments include:

• PM tasks not linked to assets — Maintenance routines that exist in the system but are not generating work orders
• Frequency mismatches — PM intervals that do not align with manufacturer recommendations, risk assessments, or operating context
• Task list drift — PM task lists that have not been updated to reflect asset modifications, regulatory changes, or lessons learned from failures
• Blanket PMs — A single PM schedule applied to all assets of a type regardless of criticality, operating environment, or condition

Stale and Missing Attribute Data

Asset attributes—such as manufacturer, model, serial number, installation date, and rated capacity—are essential for lifecycle management. Many organisations find that:

• Attribute fields were never populated during initial data migration
• Data has not been updated after asset replacements or modifications
• Custom attributes were added without governance, leading to inconsistent data entry

The Impact on Analytics and AI Readiness

Organisations investing in asset data analytics and AI-driven predictive maintenance must recognise that these technologies are only as good as the data they consume. Specifically:

• Machine learning models require clean, labelled data — Inconsistent failure codes and missing attributes make it impossible to train reliable predictive models
• Analytics dashboards amplify data problems — A dashboard built on unreliable data gives a false sense of confidence and can lead to poor decisions
• Benchmarking requires standardisation — You cannot compare asset performance across sites or against industry peers if your data is not consistently structured
• Regulatory compliance depends on traceability — Incomplete records can result in audit findings, compliance breaches, or inability to demonstrate duty of care

In short, AI readiness starts with data quality. Organisations that skip the data remediation step will find their analytics investments deliver disappointing results.

Data Quality Assessment Methodology

A systematic data quality assessment examines Maximo data across five dimensions:

1. Completeness — Are all required fields populated? What percentage of assets have missing critical attributes?
2. Accuracy — Does the data in Maximo reflect the physical reality? Are asset locations, specifications, and conditions current?
3. Consistency — Is the same type of information recorded the same way across the organisation? Are naming conventions followed?
4. Timeliness — How current is the data? When was the last asset verification or condition assessment?
5. Validity — Does the data conform to defined rules and classifications? Are failure codes used correctly?

The assessment typically involves automated analysis of Maximo data extracts combined with targeted field verification at representative sites. The output is a data quality scorecard with specific remediation priorities.

Practical Remediation Strategies

1. Establish a Data Governance Framework

Before fixing individual records, establish the rules. A data governance framework for Maximo should define:

• Asset naming conventions and hierarchy standards
• Mandatory fields and validation rules
• Failure code taxonomy (aligned to ISO 14224 where applicable)
• Roles and responsibilities for data creation, review, and approval
• Change management processes for modifying master data

2. Remediate the Asset Hierarchy

Start with the hierarchy because everything else depends on it:

• Define the target hierarchy structure (typically: Site > System > Asset > Component)
• Identify and merge duplicate records
• Reconnect orphan assets to their correct parents
• Standardise naming conventions using batch update tools
• Validate against physical asset registers and as-built drawings

3. Rationalise Failure Codes

Simplify and standardise the failure code library:

• Map existing codes to a reduced, structured taxonomy
• Remove or merge duplicate and overlapping codes
• Add clear descriptions and usage guidance for each code
• Train frontline staff on correct failure code selection
• Implement mandatory failure coding on work order closure

4. Align Preventive Maintenance Schedules

Review and update PM schedules to reflect current strategy:

• Map PMs to the corrected asset hierarchy
• Review frequencies against risk assessments and operating context
• Update task lists based on manufacturer recommendations and failure history
• Remove redundant or ineffective PMs identified through reliability analysis

5. Implement Ongoing Quality Controls

Data quality is not a one-off project. Sustaining improvements requires:

• Automated data quality reports run weekly or monthly
• Data steward roles with accountability for specific data domains
• Periodic audits of data entry compliance
• Integration of data quality metrics into asset management KPIs

Maintaining Data Quality Over Time

The most common failure mode for data quality initiatives is that improvements erode after the project team moves on. To prevent this:

• Embed data quality in business processes — Make it part of how work gets done, not a separate activity
• Automate validation — Use Maximo's built-in validation rules, escalations, and workflow to enforce data standards at the point of entry
• Report regularly — Publish data quality dashboards to leadership. What gets measured gets managed
• Link to performance outcomes — Show how data quality improvements correlate with better maintenance outcomes, reduced downtime, and lower costs

Integration with Predictive Analytics

Once data quality reaches an acceptable standard, organisations can begin leveraging advanced analytics:

• Failure pattern recognition — Clean failure data enables statistical analysis of failure modes, intervals, and contributing factors
• Condition-based maintenance — Reliable condition data supports the transition from time-based to condition-based maintenance strategies
• Predictive models — Machine learning algorithms can identify assets likely to fail based on historical patterns—but only if the historical data is trustworthy
• Digital twins — Accurate asset attribute data is a prerequisite for building meaningful digital twin models

The journey from poor data quality to AI-enabled asset management is achievable, but it must be taken in the right order: governance first, remediation second, analytics third.

How SAS-AM Can Help

SAS Asset Management brings deep expertise in Maximo data quality assessment and remediation. Our team includes experienced Maximo consultants and asset management professionals who understand both the technical and organisational dimensions of data quality.

Our Maximo consulting services include:

• Data quality assessments — Comprehensive analysis of your Maximo data across all five quality dimensions
• Hierarchy restructuring — Design and implementation of ISO 14224-aligned asset hierarchies
• Failure code rationalisation — Streamlined taxonomies that support meaningful reliability analysis
• PM optimisation — Alignment of maintenance schedules with risk-based strategies
• Data governance frameworks — Policies, processes, and roles to sustain data quality over time
• Analytics readiness assessments — Evaluating whether your data is ready to support advanced analytics and AI

We work with organisations at all stages of their data quality journey—from initial assessment through to ongoing governance support. Our approach is practical, evidence-based, and aligned with ISO 55001 principles of continual improvement.

Learn more about our Maximo consulting services or contact us to discuss your data quality challenges.