Asset Data Quality: The Foundation of Better Asset Management

Fix the Data First: Why Asset Data Quality Is the Foundation of Better Asset Management

As organizations enter a new year, many look to improve reliability, reduce risk, and make better investment decisions around their physical assets. These goals often lead to conversations about analytics, risk models, capital planning, or advanced maintenance strategies. But there is a more fundamental question that must be answered first:

Can you trust your asset data?

In practice, many asset management challenges—missed risks, inefficient maintenance, or poorly justified capital spend—can be traced back to poor asset data quality. Incomplete, inconsistent, or inaccessible asset data undermines even the most sophisticated asset management programs. Before organizations can manage assets well, they must first fix the data that describes them.

This article serves as a primer on why asset data quality matters, what “good” asset data looks like, and what organizations unlock when they get it right.

What Do We Mean by Asset Data?

Asset data is the structured information that describes physical assets and their condition, context, and performance. As part of broader asset information management, this data typically includes:

• Asset identification (what it is and where it is)
• Physical characteristics (type, size, materials, age)
• Condition information (inspection results, observed defects)
• Operational context (duty, environment, loading)
• Maintenance and failure history

This asset information often lives across multiple systems—CMMS data, EAM data, GIS platforms, spreadsheets, and inspection tools—and is collected over many years by different teams for different purposes. The result is usually siloed asset data that exists but isn’t fully usable for decision-making.

Three Characteristics of Healthy Asset Data

To support effective, risk-based asset management, asset data must meet three core criteria: completeness, confidence, and usability.

1. Asset Data Completeness

Completeness answers a basic question: Do we have the asset data we actually need?

Common gaps in asset data quality include missing install dates, unknown materials, inconsistent asset hierarchies, or incomplete inspection coverage. For example:

• A water utility may not know which pipes are cast iron versus ductile iron.
• A power organization may lack consistent condition data across similar substations.
• A data center operator may have incomplete visibility into component age or configuration across sites.

These gaps force teams to rely on assumptions, averages, or subjective judgment—introducing hidden risk into asset management and capital planning decisions.

Complete data doesn’t mean “perfect” data. It means having the minimum asset data standards required to assess risk, prioritize work, and plan investments with intent. Improving asset data completeness is often a key step in advancing asset data maturity.

2. Asset Data Confidence

Confidence addresses whether the asset data can be trusted.

Even when asset data exists, organizations often struggle with:

• Outdated condition information
• Conflicting values across CMMS and EAM systems
• Subjective inspection ratings with no context
• No clear link between observed defects and condition scores

In power systems, this can mean relying on transformer condition scores that haven’t been updated after recent inspections. In water systems, it might involve pipe condition ratings based solely on age rather than observed deterioration. In data centers, inconsistent assessment approaches across facilities can make comparisons unreliable.

Low-confidence asset data leads to hesitation: planners double-check numbers, engineers override models, and leaders question recommendations.

High-confidence data is defensible and traceable—collected using consistent condition assessment methods, supported by inspection evidence, and aligned with clear asset data governance practices.

3. Asset Data Usability and Accessibility

Usability asks: Can the right people actually use the asset data when they need it?

Asset data locked in PDFs, disconnected systems, or proprietary formats limits its value. Even organizations with mature CMMS or EAM platforms often struggle with CMMS data quality and accessibility. When engineers, planners, operators, and leadership cannot easily access or interpret asset information, decisions default to experience rather than insight.

For example:

• Field crews may not see the inspection history of a pump or breaker.
• Planners may struggle to pull consistent condition data across multiple plants or facilities.
• Leadership may only see high-level summaries with little transparency into underlying drivers.

Usable asset data is structured, connected, and accessible—supporting shared understanding and asset decision support across the organization.

What Fixing Asset Data Enables

When asset data is complete, trusted, and usable, it becomes more than a record—it becomes the foundation for risk-based asset management. Several critical asset management outcomes depend directly on asset data quality.

Trusted Risk and Criticality Analysis

Risk models are only as good as the asset data behind them. Incomplete or uncertain data leads to skewed criticality rankings, where low-risk assets appear urgent and high-risk assets remain hidden.

With solid asset data, organizations can:

• Link condition to likelihood of failure
• Apply consequence consistently across asset classes
• Defend risk rankings with evidence, not intuition

Better Capital Planning and Investment Decisions

Capital planning often suffers from a lack of defensible justification. Projects compete based on urgency narratives rather than transparent risk or condition drivers.

Reliable asset data enables organizations to:

• Align capital spend to measurable risk reduction
• Compare renewal options using consistent criteria
• Forecast long-term needs across the asset lifecycle with fewer surprises

Whether it’s pipe renewal programs, transformer replacements, or upgrades to critical data center infrastructure, strong asset data supports more confident and explainable asset lifecycle management decisions.

More Effective Maintenance and Daily Work

Maintenance teams make dozens of decisions every day—what to inspect, what to defer, what to repair now versus later. Without trusted asset data, these decisions rely heavily on tribal knowledge.

High-quality asset data supports:

• Condition-based maintenance instead of time-based assumptions
• Better prioritization of work orders
• Clear feedback loops between inspections and maintenance actions

This leads to fewer unplanned outages, more targeted maintenance, and better use of limited resources across asset-intensive industries.

From Data to Outcomes: The Bigger Picture

Fixing asset data is not a technology exercise—it’s an asset management discipline. It requires clear asset data standards, consistent condition assessment practices, and a deliberate focus on how asset information will be used, not just stored.

When organizations invest in improving asset data quality, they unlock:

• Alignment across engineering, operations, and leadership
• Reduced uncertainty in risk and investment decisions
• A stronger foundation for advanced analytics and planning tools

This is where platforms like MentorLens naturally enter the conversation—not as a starting point, but as an enabler of structured, trusted, and connected asset information management.

Start with the Data, Build Toward Better Outcomes

As the new year begins, it’s tempting to focus on ambitious initiatives—advanced analytics, predictive models, or optimization programs. But lasting improvement in asset management starts with a simpler step:

Fix the data first.

Complete it. Build confidence in it. Govern it. Make it usable. Everything else—from risk management to capital planning to day-to-day operations—depends on a strong asset data foundation.

Let's Talk About Fixing Your Asset Data