Editor’s note: FM Perspectives are industry op-eds. The views expressed are the authors’ and do not necessarily reflect those of Facilities Management Advisor.
Industrial organizations and building operators are rapidly adopting smart building technologies. From HVAC systems and lighting controls to advanced energy management solutions, facilities are evolving into connected ecosystems to boost efficiency, productivity, and operational insight. Yet, despite significant investments in technology, many facilities managers soon discover that their facility data remains fragmented, poorly structured, and difficult to integrate effectively.
Connecting diverse automation systems often results in large volumes of inconsistent data. Facilities management teams spend considerable time manually structuring, tagging, and organizing this data to make it usable. Rather than delivering real-time intelligence and optimized operations, these manual processes slow implementation and drive up operational costs.
The Challenge of Legacy Infrastructure and Data
Most commercial buildings include a combination of legacy and modern technology. HVAC units, lighting controls, environmental sensors, and energy meters installed decades ago coexist alongside newly deployed Internet of Things (IoT) devices. Older systems frequently utilize protocols like BACnet and Modbus, originally designed for isolated networks without consideration for secure connectivity or modern data handling.
As these legacy systems become connected to enterprise analytics platforms, the data they produce is typically difficult to reconcile. Because equipment vendors historically used proprietary protocols and naming conventions, buildings today often face major challenges with data consistency, interoperability, and usability. Facilities managers frequently find themselves dealing with time-consuming data cleanup and preparation, which significantly limits the agility and responsiveness promised by smart building technologies.
In an effort to address this data complexity, the industry has increasingly embraced open standards such as Project Haystack. These standards provide common frameworks for organizing and structuring building data, aiming to simplify integration and reduce manual tagging.
However, adoption of these standards typically occurs in post-processing steps after data collection, rather than at the source. Although helpful, this traditional approach introduces considerable delays. Engineers must still manually restructure and align data, making scaling across multiple facilities costly and slow. Consequently, facilities often miss out on real-time insights and efficiencies that smart technologies could deliver if data were readily available.
How AI-Driven Edge Computing Changes the Game
Artificial intelligence (AI) applied at the network edge offers a practical solution to the smart building data challenge. Edge computing platforms equipped with AI can instantly process and structure data at the exact moment of generation, eliminating most manual intervention and significantly streamlining integration.
These edge-based AI systems automatically classify, tag, and format raw data into standardized, actionable information. By using machine learning to recognize device patterns and understand domain-specific data structures, facility teams can ensure consistency and interoperability across diverse systems. Data arrives ready to use, directly supporting immediate analytics, automation, and operational improvements.
A growing number of leading property management firms and energy solutions providers are adopting edge-based AI to simplify data management across their facilities. These organizations use edge computing solutions to capture data from diverse building equipment, immediately structuring and standardizing the information to align with industry standards like Project Haystack. Such implementations have dramatically accelerated project timelines, reduced implementation costs, and enhanced the accuracy and usability of collected data.
Overcoming Organizational Barriers
Yet, technological improvements alone cannot fully address the smart building data issue. Cultural and organizational barriers within facilities management teams also require attention. IT and operational technology (OT) departments typically have distinct objectives and operate independently, limiting coordination on critical security, data management, and operational decisions.
To overcome this, facilities must emphasize cross-team collaboration. IT and OT groups need shared processes, coordinated policies, and a common understanding of how key decisions impact each other’s work. Executives and procurement teams should include security and integration requirements as major considerations in vendor selections and operational planning. Achieving this shared alignment will help organizations more effectively implement and manage edge-based AI solutions.
Facilities managers looking to leverage AI and edge computing to address smart building data challenges should consider several practical steps:
- Evaluate existing practices: Clearly identify and document current pain points in data management and integration processes.
- Choose suitable solutions: Carefully select edge computing platforms that support AI-driven data standardization, tagging, and normalization consistent with industry frameworks such as Project Haystack.
- Start small and scale up: Pilot initial deployments on a limited scale, demonstrate their value, then replicate successes across additional properties and portfolios.
- Foster collaboration: Establish effective coordination between IT, OT, and executive teams early, aligning them around shared objectives and clearly defined operational processes.
Additionally, facilities should plan for ongoing maintenance, management, and security updates to ensure that edge deployments remain secure and effective over time.
Closing the Smart Building Data Gap
The real promise of smart buildings depends fundamentally on reliable, structured, and interoperable data. Manual, offline approaches to data management are no longer sufficient; they inhibit responsiveness, increase operational complexity, and limit scalability. AI-driven edge computing represents a viable path forward, providing facilities with immediate, structured, and actionable data as soon as it is created.
By leveraging edge-based AI, facilities managers can accelerate integration, significantly reduce overhead, and unlock smarter decision-making capabilities. This approach establishes a solid foundation for future innovation, ensuring buildings remain responsive, secure, and efficient in a connected world.
Ultimately, a truly intelligent building is not just about having more devices or sophisticated analytics. It is about having clean, structured data readily available whenever and wherever it is needed. For facilities managers who understand this, adopting AI-driven edge computing is no longer optional. It is essential.
Andrew Foster is product director at IOTech, with over 20 years of experience developing IoT and Distributed Real-time and Embedded (DRE) software products. He has held senior roles in product delivery, management, and marketing, and frequently speaks at industry conferences on distributed computing, middleware, embedded technologies, and IoT. Foster holds an M.S. in Computer-Based Plant and Process Control and a B.Eng. in Digital Systems.
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