How to Prioritize and Improve Facilities Management Data

Many facilities teams recognize that their asset data needs improvement. The challenge is knowing how to address it in a way that is structured, manageable, and delivers measurable results.

Too often, data cleanup efforts attempt to address every asset, every data field, and every system at once. As scope expands, progress slows and operational value is delayed. Meanwhile, the data that carries the greatest risk remains unreliable.

A more effective approach is to focus on what matters most and follow a defined, repeatable process.

This blog outlines the key steps facilities teams can take to clean and standardize asset data, starting with high-risk asset classes and expanding gradually across the portfolio. By breaking the effort into manageable phases, organizations can improve data quality, build trust, and deliver value without overwhelming their teams.

The scale of the asset data problem

Poor data quality has direct operational and financial consequences, and the data shows it.

• 30% of construction documentation data is lost during handover and can cost the industry $1.8 trillion dollars
• $15.8 billion is lost each year due to poor system interoperability
• 67% of organizations are not confident in their data quality and prefer making decisions based on intuition

Anecdotal data from conversations with facilities teams points to issues stemming from data being fragmented across systems including CMMS, IWMS, BIM, spreadsheets, manuals, and work orders as well. Asset names, locations, and attributes are often inconsistent, making it difficult for teams to trust the data or rely on it for decision-making.

For a deeper look at what asset data includes and why it matters, read What Is Asset Data (and Why It Matters).

Why asset data cleanup efforts stall

In response to these challenges, organizations often launch large-scale data cleanup initiatives.

However, many of these efforts attempt to standardize all data across the enterprise at once. This introduces unnecessary complexity:

• Every asset class is included
• Every data field becomes a priority
• Multiple systems must be reconciled simultaneously

As scope expands, the effort becomes difficult to manage. Progress slows, and operational value is delayed while teams focus on incremental improvements.

This is what “boiling the ocean” looks like in facilities management.

Upcoming Webinar: How to Build Trustworthy Facilities Management Data That Actually Drives Results

Join me, Paul Head, as I challenge the idea that better facilities data requires massive, multi-year transformation efforts. Drawing on real-world experience across facilities operations, capital planning, and technology enablement, I will explore where FM data most commonly breaks down, why those failures matter operationally, and how organizations can regain confidence in their data by prioritizing what truly matters.

This webinar is designed for facilities and maintenance leaders across all industries who want to move from broad data ambitions to practical, achievable improvements that deliver real operational impact.

A more effective approach: prioritize what matters most

Not all assets carry the same level of operational risk.

Rather than pursuing enterprise-wide perfection, organizations should begin by focusing on the assets that have the greatest impact on operations, safety, and cost.

These typically include:

• Critical systems that support core operations
• Assets tied to safety or regulatory compliance
• Equipment with high negative impact if it fails
• Assets with frequent maintenance issues or elevated lifecycle cost

By prioritizing these assets, organizations can reduce risk more quickly while keeping the scope of the effort manageable.

This approach also lays the groundwork for more advanced strategies because without clean, standardized asset data, advanced initiatives, including predictive maintenance, struggle to deliver value.

See if your data is ready for predictive maintenance and AI with our AI Readiness Assessment.

A structured approach to asset data cleanup

Once a priority asset class or facility has been identified, the focus should shift to executing a consistent, repeatable process.

1. Start with a defined scope

Avoid enterprise-wide efforts at the outset.

Focus on a single asset class or a specific facility, such as HVAC systems in a hospital or critical equipment in a production environment. This allows teams to validate their approach, refine processes, and demonstrate early value.

2. Establish a reference data model

Before making corrections, define the standard for data quality.

This includes:

• Standardized naming conventions
• Clearly defined required fields
• Consistent classification structures

The model should remain practical and easy to maintain. Overly complex structures, such as excessive sub-classifications or rarely used fields, often create more inconsistency rather than clarity. A helpful way to guide decisions is to evaluate each field or classification based on the value it provides and whether it can be consistently maintained. If the value is limited or unclear, it is better to leave it out.

Important tip: A strong data model is not the most detailed. It is the one that is consistently used and trusted across the organization. Engage with technicians to ensure the model reflects how the assets are actually managed.

3. Consolidate data from relevant sources

Many organizations’ asset data is distributed across multiple systems.

To create a complete dataset, organizations should consolidate information from:

• CMMS or IWMS platforms
• BIM or COBie data
• Spreadsheets and legacy systems
• Equipment manuals and vendor documentation
• Historical work order data

Bringing these sources together gives teams a complete view of the assets’ performance and makes it easier to spot gaps, duplicates, and inconsistencies. It also eliminates the need to bounce between systems to find answers, allowing teams to work from a single, consistent source of information.

4. Benchmark current data quality

Before implementing changes, establish a baseline.

Key metrics may include:

• Completeness of required data fields
• Volume of duplicate records
• Presence of asset hierarchies
• Consistency of naming and classification
• Availability of maintenance history

Benchmarking enables organizations to measure progress and demonstrate improvement over time.

5. Identify and resolve data issues

This phase focuses on correcting known issues within the dataset.

Typical activities include:

• Removing duplicate asset records
• Filling gaps in required data
• Correcting inconsistent or inaccurate values
• Standardizing formats across systems

Because this work can be resource-intensive, many organizations rely on specialized service providers for support. For smaller or less mature organizations, AI tools can help identify anomalies and likely duplicates to speed up analysis.

However, subject matter expertise is still essential to validate findings and approve any changes.

6. Validate data with operational stakeholders

Technicians and asset managers play a critical role in ensuring data accuracy.

Validation should include:

• Confirming asset locations and relationships
• Verifying classifications and attributes
• Reviewing maintenance history

This step ensures the data reflects real-world conditions and builds trust among end users.

7. Measure improvements in data quality and usability

Data cleanup should lead to clear, measurable improvements.

Organizations should track:

• Completion rates for required fields
• Reduction in duplicate records
• Accuracy of asset hierarchies
• Improvements in maintenance reporting and insights

Equally important is how the data is being used in practice. Teams should regularly consult with technicians and audit how asset fields are being completed to ensure standards are being followed.

Without this, data quality can quickly degrade over time. Sustaining data quality requires ongoing visibility into how data is entered, not just how it is structured.

8. Expand in a controlled, repeatable manner

Once the process has been validated, it can be applied to additional asset classes or facilities.

Because standards and workflows have already been established, each expansion becomes more efficient and predictable.

This phased approach allows organizations to scale without introducing unnecessary complexity.

The bottom line

Attempting to correct all asset data at once often delays progress and limits impact.

Focusing on the most critical assets, supported by a structured and repeatable process, enables organizations to deliver value more quickly and scale improvements effectively.

Rather than attempting to boil the ocean, organizations should prioritize, execute, and expand with intention.

Take the next step toward better asset data

Improving asset data quality is not just about cleanup. It is about creating a foundation for better maintenance, stronger capital planning, and more informed decision-making across your organization.

A connected approach makes this possible.

Nuvolo helps organizations bring asset, maintenance, and operational data into a single platform, making it easier to standardize data, enforce governance, and maintain accuracy over time. Instead of managing data across disconnected systems, teams can work from a unified source of truth that supports both day-to-day maintenance operations and long-term capital planning.

If you are looking to move beyond one-time data cleanup efforts and build a more sustainable approach to asset data management, it starts with the right foundation.

Learn how Nuvolo can help you improve asset data quality and drive better facilities outcomes.

About the Author

Paul Head is a seasoned technology leader with over 30 years of experience in strategic and operational management of facilities and real estate. As the current IFMA Public Sector President and Director at Nuvolo, he is a passionate advocate for leveraging technology to drive operational transformation. His expertise lies in helping organizations align their mission with operational goals, using a digital-first approach to enhance effectiveness across the entire real estate lifecycle.