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“If you look at the fundamental problem in the industry, there is really a disconnect between [building] construction and operation,” stated James Lee during a Memoori webinar last week. “Financial interest of developers, engineers and contractors are not really in alignment with long-term owners and occupants,” he continued.

Lee believes this lack of transparency in the construction and operation phases results in a building’s long-term occupants not really understanding what they have and what the problems are. He believes this factor is eroding trust between vendors and customers in the industry. Despite identifying the problem, Lee believes that it’s not that easy to change.

“It turns out that the profits in the existing business models are based on this dysfunction. In other words, the industry makes more money in a dysfunctional state than it does doing things the right way,” he told listeners. “For instance, facility management companies are often compensated on the gross operating costs of facilities, so they are incentivized to waste.”

Lee is CEO & President of Cimetrics, a pioneer in building analytics and an early starter in the internet of things (IoT) space. Cimetrics began working in Industrial Automation in the 80’s, worked on BACnet and control network standards in the 90’s, evolving into TCP/IP based networks and the Internet in the 00’s, and now focus primarily on cloud computing, bots, and big data. Lee believes that analytics is key to removing many of the complexities that we build into our buildings.

Analytics starts with collecting data but that alone is not useful. “There are different types of analytics that we can do; statistical, model based, or machine learning, but ultimately what we are looking to do is generate value for the building owner. That’s the main goal,” he points out. “Basically I have a sea of information, the problem becomes reducing that information down to something that’s manageable. Using analytics we can let people see what they couldn’t see before.”

Lee draws comparisons to Uber, as an example. In the same way that seeing where the taxi is on its way to a pick-up, creates efficiency; analytics can do the same for a building. Where previously we simply visualized data in one way or another, to help us understand systems; with analytics we can process more data to create greater accuracy and unearth actionable intelligence. This is about more than just developing energy efficiency, in fact energy is not even that important according to Lee.

“People often fall to energy management as the principle value proposition for analytics. I’m here to tell you that building owners didn’t care about energy before and they don’t care about it now,” he said during the interactive webinar (Subscribe on iTunes or Soundcloud). “It is quite difficult to make a sale on energy alone,” Lee points out, and this is in contrast to other enterprise management systems.

When inventory management emerged or when IBM introduced accounting software, even on to the Salesforce type systems we have today, success was not due to promises of hard payback. In essence these systems just helped make a businesses work better. However, it appears the landscape is different when it comes to enterprise building management systems.

“When it comes to the sphere of buildings and energy, people demand hard payback,” explained Lee. “What’s fascinating is that people are looking for hard energy paybacks but, ultimately, just as justification for doing the project. Other goals, such as comfort, compliance, forensics, vendor management or fault detection and diagnostics, are the real reasons that people want analytics.”

It is an interesting argument. While energy payback is good, it pales into insignificance when compared to the impact of increasing productivity in an office, even by 1%.

We seem to latch on to the energy savings brought about by analytics, simply because it is more tangible, easier to understand and quantify. If electricity were more expensive, or other factors easier to quantify, perhaps the situation would be different. Analytics, however, is improving, creating greater transparency and bringing light to the true value of information.

“Analytics is hugely useful in identifying ‘needle in a haystack’ type solutions, where there’s a piece of eureka knowledge that provides significant advantage to an organization. So you can learn something and apply that golden nugget to your business. However, once you’ve found it, the customer will often say, ‘you found that golden nugget but what have you done for me lately?’” Lee replied to one question from the interactive webinar audience, before providing the solution:

“When considering sustainable IIoT analytics applications, if we apply a models-based analytics to track complex systems we can then understand those systems better as we model them. As those systems change, evolve, wear-out and breakdown we can provide recurring value to the owner and provide root cause analysis.”

You can listen to the whole one-hour webinar through our site or subscribe via Soundcloud or iTunes. To be involved in our next live webinar sign up to our newsletter below.

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  • Jonathan Coburn

    There are many disconnects at play here in addition to construction vs. operation. For example, there is a large gap between the technology providers (computer / IT people) and building managers. The former lack the domain knowledge to think beyond energy / FDD and the latter do not yet understand the power of data and analytics beyond making what they already do more efficient.

    What the smart Building / IoT evangelists overlook is –
    (a) buildings are managed using functional organizations, which use only a subset of the data that could (and should) be collected. Applying analytics to only available data leaves a lot of value on the table.
    (b) not all useful data is real-time data. There is no sensor I can install that tells me how much it costs to maintain a building or its assets, or what the risk to the business is if something fails. Applying analytics to only real-time / sensed data leaves a lot of value on the table.