Introduction
Even in industries as large and long-standing as commercial real estate, things can change quickly. Seemingly overnight, data is overtaking location as the primary competitive differentiator in the market.
Thanks to the falling costs of sensors and ubiquity of mobile devices, collecting real-time data is affordable and information is available from anywhere.
However, when it comes to building operations, this has created a new problem. Even when information is collected and made remotely available, it doesn’t necessarily make its way to the people who can act on it, which is just as important as having meaningful data in the first place.
Too often, valuable data gets stuck with building engineers and is difficult for both management and building staff to access.
As real estate companies craft data strategies, how can they ensure the right information gets to the right people at the right time, so they can act on it?
Sometimes, technology completely revolutionizes how things are done. Much more often, technology is simply an evolution of what is already being done, making an existing process faster, more accurate and less expensive.
The latter is the case for technology in building operations. To better understand this evolution, it helps to use a framework developed by the CIA: Data, Analysis, Decision, Action (DADA).
Whether consciously or not, every building operator uses this framework to handle repairs, execute maintenance schedules, and make changes to system configurations.
For data and analytics to drive the maximum value for building operations in real estate portfolios, it must make existing processes more streamlined, not demand a revolution in how things are done.
Data
Today, data generally implies computer-generated information. But data is being collected every day even in the most unsophisticated buildings. Tenant complaints, audits, pen and paper checklists, spreadsheets, etc. are all forms of data.
Building operators are using these data points daily to determine the issues that need to be solved and to prioritize their work as appropriately as possible.
But there is plenty to be desired. An estimated 18–30% of every maintenance dollar is wasted, the useful life of critical equipment is reduced by 20–36% due to improper maintenance, and 30% of energy is lost.
Relying on manual data collection will never be sufficient to eliminate this waste. Take this recent advice from a Professional Engineer on how to reduce waste in HVAC systems: “…perform a visual inspection of the entire system, from the outdoor air intake grill to the room diffusers. At this point, it’s not necessary to take measurements. That will come later.”
It’s easy to imagine that this advice would get a hearty laugh from a building operator who is barely keeping up with the emergency repairs and tenant complaints as it is.
Data collection is all about affordability, accuracy, continuity, and granularity. A physical walkthrough fulfills none of these criteria.
In an ideal world, building operators spend exactly zero time on data collection. They are needed elsewhere, and this part of the process can and should be automated with IoT data throughout the building.
Analysis
Unlike data, analysis doesn’t necessarily imply that it is performed by machines, although that is becoming more common.
The intuition of a well-trained and experienced operator can be a powerful analytical tool. Unfortunately for many owners, the older generation of building operators is retiring, creating a significant skill gap that is not easily overcome.
In addition, as data collection is moved from manual processes to sensors, the type of information to be analyzed is becoming more complex and varied. At some point, even the brightest analytical mind cannot utilize the continuous flow of data from building sensors.
Analysis is all about speed and accuracy (the latter of which is driven largely by the size of the data set). Machines can make computations infinitely faster than humans and uncover patterns that humans are generally unable to.
To be fair to human abilities, an experienced building operator could do more with a handful of tenant complaints than a machine due to our ability to make inferences and abstract connections.
Still, since it’s obvious that data should be collected with sensors rather than through tenant complaints (meaning you’re already behind the ball), and because those data sets are too complex for humans, analysis should also be performed by machines.
Decision
When equipment inevitably breaks down, there are often dozens of potential issues that could be occurring. The process of testing theories to pinpoint the issue is known as troubleshooting.
In building operations driven by real-time data and machine analyses, decisions should be driven by a symbiotic relationship between man and machine.
While machines can make computations and uncover patterns, translating anomalous data into a real-world decision is impossible for machines without assistance. Creating this symbiotic relationship is not easy. That is why most data analytics technologies fail to deliver relevant information to the people who can act.
It’s not that they are inaccessible (they all have cell phones), it’s that without a “decision,” raw data and analytics are not actionable for busy operators.
Decision making is about speed and accuracy. For the same reason that an operator with 30 years of experience will be able to troubleshoot an equipment malfunction more effectively than a junior operator, machines must also be “trained” to make decisions.
Over time, more and more decisions should be placed in the hands of machines. But the only way to get there is by incorporating feedback into normal troubleshooting processes.
Action
Action is, and will remain, squarely the responsibility of humans. The world is nowhere close to a robot sophisticated enough to navigate mechanical rooms, understand what it is looking at, take apart equipment, and fix issues.
The exciting part is that by eliminating the time spent on data collection and analysis, and reducing the time spent on troubleshooting, building operators will have more time to devote to their core competency – ensuring that a building maintains a steady state of performance.
Every operator knows that a long-term solution to an equipment issue is better than a quick fix. But with all the other demands for their time, operators often have no choice but to cut corners.
Devoting more time to the well-thought-out solutions will not only save owners operating expenses related to maintenance, repairs, replacements, and energy, it will improve the morale of building operators by removing the most stressful aspects of the job.
Conclusion
One important thing to remember about the DADA method is that it is circular. As soon as an action is performed, data needs to be collected about the results and incorporated into the resolution analysis.
Recording work order resolutions have been standardized in some portfolios, but many are still lacking the connection between action and data collection. Handing over data and analysis to sensors and machines automates this connection. It also facilitates a virtuous feedback cycle of improved performance, better analytics, more accurate decisions, and further improved performance.
It has already been said by many of the smartest people in real estate – if you own and operate commercial properties, you are in the technology business, whether you realize it or not.
That doesn’t have to be as scary as it sounds. When it’s broken out into its separate elements, technology is just an enhancement of the existing process, not a revolution.
Want to infuse data into your building operations? Schedule a demo to see how Enertiv does it.
.webp)
.webp)
