Machine learning in CRE spaces is one of the most exciting ways new technology is changing the facility management landscape. On the surface, machine learning appears to be an end-all solution to energy efficiency and basic business needs. However, machine learning is much deeper than that, and Facility Managers need to understand what machining learning in CRE spaces means.
Misunderstanding Machine Learning Remains a Big Problem
- It’s Expensive. Measurement and verification (M&V) are less expensive. As a result, investing in machine learning and applying it is more accessible and more affordable so that more companies can take advantage of it without the worry of investment risk.
- It’s Unnecessary. Cities and states are increasing requirements for building transparency. Changing needs for commercial building transparency and sustainability will require a radical overhaul of fundamental business processes, and machine learning can help CRE owners, prioritize upgrades and verify results.
- We Can Maintain the Status Quo. Black swan weather events are increasing in frequency. Although CRE Facility Managers may have the resources necessary to maintain the status quote in building conditions, increased incidence of black swan weather events will result in higher demand for machine learning, artificial intelligence, and data-based decision-making processes.
- We Have the Resources to Manage Everything. Extensive demands and tightening processes require simplification. Similar to maintaining the status quote, companies may face a time when available resources are scarce, and this is expected to continue as the talent gap widens. However, machine learning and artificial intelligence are the precursors to digitized facilities management.
- Smart Meters Will Automate Energy Management. Smart meters alone cannot manage energy use. Although automation is the end-goal for investment in new technology and machine learning in CRE spaces, current levels of technology, such as smart meters, act as conduits for information that must then be applied. Smart meters may automate the data collection process, but making changes will still require human input.
Machine Learning in CRE Makes Sense of Data and More
According to Rita Tatum of Facilities Net, the highest capability of machine learning in CRE spaces is rooted in the vast amounts of data. These buildings generate a mountain of data on a daily basis, and insights may exist within the furthest reaches of the data. Machine learning provides CRE managers with the ability to analyze and act upon data. Humans lack the capability to data mine to the degree possible in the volume of data available.
How to Effectively Use Machine Learning in CRE Spaces
- Rebalance the property portfolio.
- Focus on tenant centricity.
- Diversify inventor base.
- Develop an enterprise-wide strategy.
- Use agile, digital processes.
- Consider occupant and tenant health and wellness.
- Leverage the Internet of Things (IoT) to track data.
- Educate the tenant to enhance engagement.
Put the Power of Machine Learning to Work in Your Properties Now
Preparing to use machine learning in CRE spaces may seem daunting. Systems will need integration, and outdated assets may require upgrades through the installation of wireless sensors and bringing the maintenance backlog under control. Instead of becoming stressed or overwhelmed, streamline the process by contacting ENTOUCH online or calling 1-800-820-3511 today.