Predictive Maintenance Challenges & How to Overcome Them With Digital Twins

Predictive maintenance is a proactive approach to asset management that uses data analytics, machine learning, and condition monitoring to detect potential equipment failures before they happen, reducing unplanned downtime and extending asset life. Though the case for implementing predictive maintenance is strong, real-world implementation can present some challenges, particularly when it comes to unifying data streams, ensuring predictive accuracy, and managing multi-site operations..

This article looks at the main obstacles facilities face when it comes to building a predictive maintenance process and offers practical ways to overcome them. It also highlights how emerging technologies like 3D digital twins—virtual models that mirror physical assets—can help address many of these issues by improving system visibility.

Building documentation silos across systems and assets

Documentation silos—where maintenance records, equipment manuals, inspection reports, and sensor data are stored in disconnected systems—create significant barriers to effective predictive maintenance. When critical information is scattered across departments, trapped in incompatible formats or stored in physical documents, maintenance teams struggle to access the data they need and draw relevant conclusions about asset status. Fragmentation of information can lead to:

• Longer diagnostic times

• Inconsistent data

• Incorrect parts ordering

• Suboptimal maintenance scheduling

• Duplication of efforts

Centralize facility documentation

Consolidating asset information removes a major source of friction in maintenance workflows. By centralizing records onto a single, accessible system, teams can streamline planning, repairs, and response.

Centralizing documentation offers numerous benefits, including:

• Immediate access to manuals, documentation, and service history when anticipating or responding to maintenance needs.

• Simplified updates and version control, with a single source of truth instead of syncing changes across disconnected platforms.

• Reduced search time, since teams know exactly where to look—no more digging through emails, paper files, or multiple databases.

Incorporating a 3D digital twin into your facility’s document management strategy can add powerful spatial context and structure to centralized data.

• Tagging & annotations: Digital twin platform allows you to label specific assets with critical metadata and attach documents, notes, and media files directly to their location in the 3D model.

• Visual navigation: Locate equipment visually rather than relying on asset IDs or text-based systems.

• Real-time status visibility: See live operational data and service status in context—without switching systems.

Linking records directly to equipment also supports accurate maintenance decisions for a more effective predictive maintenance strategy.

With a digital twin as the interface, facilities teams can move from fragmented documentation to a cohesive, context-rich environment—reducing delays and improving maintenance efficiency at every step.

Integrating with legacy systems

Many facilities rely on outdated or siloed technologies that were never designed to support real-time data exchange or advanced analytics. These systems may lack standardized communication protocols, making it hard to connect them with modern IoT devices or cloud-based platforms. As a result, compatibility issues frequently arise when trying to merge sensor data from new equipment with older control systems or proprietary software, and valuable data remains locked in isolated systems. 

Bridging this gap requires custom integrations, middleware, or infrastructure upgrades—all of which add time, cost, and complexity to deployment.

Familiarize yourself with best practices for system integration

Integrating legacy systems with modern predictive maintenance tools requires a careful, phased approach. Upgrading everything at once is costly and disruptive—so teams should focus on scalable steps:

1. Start with high-impact assets and layer in predictive capabilities gradually.

2. Use third-party integration tools or middleware to bridge old systems with new platforms.

3. Collaborate with equipment vendors to access existing APIs or interface specifications.

Many predictive maintenance solutions will seamlessly integrate with existing maintenance systems. Digital twins simplify the process of digitizing documentation and enable cross-platform data syncing:

• Digitize legacy documentation by tagging manuals, service logs, and inspection records directly to equipment.

• Sync data across platforms with flexible APIs and SDKs. Matterport’s developer toolkit gives teams the flexibility to integrate its 3D digital twin technology with various facility management platforms like CMMS and BIM systems.

To ensure successful integration, it's essential to consider factors like APIs, data formats, and system architecture when connecting predictive maintenance solutions to existing systems. Middleware and integration platforms can play a crucial role in simplifying these connections, enabling seamless data exchange between disparate systems. Additionally, adopting standardized data formats and protocols can further streamline the integration process and promote interoperability.

Middleware and integration platforms play a key role in managing these connections without deep custom development. Choose systems that support open protocols like OPC UA, MQTT, or REST APIs to enable smoother data exchange.

A phased rollout helps minimize disruption. Start by integrating read-only data flows to validate connections, then expand to bi-directional syncing and full automation. Standardizing data structures early helps avoid rework and ensures seamless scaling across systems.

High-level security and privacy concerns

Maintenance systems often handle sensitive information like performance metrics, failure predictions, and operational processes, which can be targeted by cyber threats if not properly protected. Connected systems introduce additional vulnerabilities, such as unauthorized access to remote devices, vendor access, data breaches, and potential manipulation of predictive models. The risk of exposing critical business data, intellectual property, or even system control to malicious actors is high, making it essential for organizations to invest in robust cybersecurity measures.

Understand security and compliance fundamentals

For new predictive maintenance implementations, take a security-by-design approach. Ensure that security is built into the system from the start, with strong encryption, access control, and compliance measures woven into the development process. Be mindful of third-party access to sensitive data, especially with cloud-based solutions, and ensure that vendor agreements include strong data protection clauses. Start with regular risk assessments to identify potential vulnerabilities and prioritize actions. Data governance policies ensure that data handling complies with relevant regulations and is securely managed throughout its lifecycle.

Key security tactics:

• Regular risk assessments to uncover vulnerabilities before they can be exploited

• Data encryption for both in-transit and at-rest data to prevent unauthorized access

• Role-based access controls (RBAC) to ensure that only authorized personnel can access sensitive data

• Periodic security audits and penetration testing

• Providing security awareness training for maintenance staff

By tagging important documentation in 3D models, teams can track asset conditions and maintain visual records for inspections and audits. A clear, auditable trail simplifies compliance checks and regulatory reporting. Maintenance teams can easily demonstrate adherence to regulatory requirements, such as OSHA safety standards or ISO certifications, by referencing the digital twin.

Furthermore, Matterport's platform includes built-in security controls, such as secure sharing options and granular access permissions. This allows organizations to selectively share digital twins with authorized personnel, contractors, or auditors, while restricting access to sensitive areas or information.

Handling IoT sensor technology and data limitations

The sheer volume, variety, and velocity of data generated by IoT sensors in predictive maintenance can be overwhelming. Physical constraints such as sensor placement, interference, and environmental factors often lead to noisy or incomplete data, making it challenging to derive accurate insights, and monitoring hard-to-reach or hazardous areas poses additional difficulties in data collection and analysis.

Bridge sensor data gaps and reduce data noise

Accurate sensor data is essential for predictive maintenance. Common issues like drift, dropouts, and inconsistent readings can lead to missed faults or false alerts. To improve accuracy, teams should follow best practices:

• Calibrate sensors regularly to maintain consistency

• Use redundant sensors on critical systems to verify anomalies

• Clean data automatically using filters that remove outliers and incomplete readings

Digital twins provide a valuable layer of spatial insight that sensors alone can’t offer. Teams can visually monitor asset conditions inside a spatially accurate 3D environment and see exactly where issues are occurring. This helps maintenance staff identify blind spots in sensor coverage, validate conditions visually, and optimize sensor placement across the facility.

Emerging AI tools are beginning to be integrated into IoT systems to filter out noise, support more accurate predictions and fill in data gaps. Algorithms can analyze images captured in digital twins to detect signs of wear, corrosion, or structural shifts—even without sensors present.

Workforce adoption and training gaps

Upskilling teams for predictive maintenance can be challenging due to the need for both technical domain knowledge and data literacy. Many staff are unfamiliar with advanced analytics, sensor systems, or digital tools, making it difficult to interpret predictive insights or act on them confidently. Training takes time, and without clear guidance or user-friendly platforms, adoption often lags. And without proper training and support, even the most sophisticated predictive maintenance tools can fail to deliver their intended benefits.

Build an accessible training framework

To close the skills gap, facilities should invest in training frameworks that are structured, flexible, and accessible. Focus on:

• Structured training programs that cover core technologies and workflows

• Cross-functional mentoring between technicians, engineers, and data analysts

• Ongoing professional development to keep pace with evolving tools and standards

Digital twins are powerful environments for interactive training. Staff can explore real-world facility models, review asset data, and visually learn how systems operate. This boosts understanding through spatial context and accelerates comprehension of complex equipment layouts. New hires can safely practice procedures in a virtual twin, reducing on-site risk and training time.

Access to the “master reference” held inside a 3D digital twin also helps to preserve tribal knowledge. Subject matter experts can embed annotations, videos, and repair steps, ensuring critical expertise is retained and easily accessible.

Justifying the ROI of high initial costs

Justifying the ROI of predictive maintenance can be difficult due to high upfront costs and uncertain savings timelines, making executives hesitant to invest. Common misconceptions—like assuming predictive maintenance is only for large enterprises or that sensor and platform costs outweigh benefits—can further delay buy-in, even when long-term gains in uptime, efficiency, and asset lifespan are substantial.

Master the ROI calculation methods

Measuring the return starts with identifying benefits,  both tangible and intangible, gained from specific features of a predictive maintenance strategy:

To build a strong ROI case, start by modeling key cost factors:

• Average downtime cost per hour × estimated hours saved

• Repair frequency reduction × cost per repair

• Spare parts usage before and after implementation

Then apply standard financial formulas. Make sure to use conservative assumptions and real facility data when possible.

Embracing predictive maintenance for long-term operational gains

Making the shift from reactive firefighting to a proactive, data-driven predictive maintenance strategy is no small undertaking. By combining visual context with integrated maintenance data, organizations can make smarter decisions faster—building more resilient, efficient operations.

Predictive maintenance will continue to evolve with the rise of AI-driven diagnostics, expanded IoT sensor networks, and the integration of AR/VR for remote collaboration and guided repairs. Matterport’s open ecosystem and integration-ready platform make it well-positioned to adapt and lead as these technologies converge, helping teams unlock even more value from their maintenance investments.