Data synergy in integrated facilities management: The key to clearer insights, smarter decisions and optimised operations
Chris Lilly
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Chris Lilly: ESFM, USA
Corporate Real Estate Journal, 2024, vol. 14, issue 1, 75-85
Abstract:
Today’s corporate real estate landscape has evolved within the digital age. Operations, including hard and soft services critical to a facility’s infrastructure and occupant experience, can now produce vast amounts of data to better adapt, generate reports and optimise resources. Many facilities, however, are not reaping the rewards of technological advancements because of solutions operating in silos. Data is being cobbled from disparate streams, leading to inefficiencies and a burden on operational leadership. This paper explains the paradox within integrated facilities management (IFM), wherein five or ten standalone applications that do not interact with each other are often used to perform vertical functions. By looking at how other industries have approached similar challenges, including the emergence of enterprise resource planning, standalone buildings, and those within a corporate real estate portfolio, can integrate technology solutions and processes for a more synergised approach. This move can lead to clearer data both at unit and above-unit levels. Combined with the power of artificial intelligence (AI) and machine learning (ML), infinite variables can be incorporated into processes via smart technology to maximise the impact of resources, including labour, critical systems and infrastructure. From data-driven cleaning to asset management, integration opportunities abound. Whether enhancing existing platforms or seeking new technology solutions to broaden functionality, it is critical to understand the power of synergy.
Keywords: technology; integration; software; building systems; data-driven decision making; AI (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:aza:crej00:y:2024:v:14:i:1:p:75-85
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