Comly Wilson, director of marketing at Enertiv, shares lessons from more than a decade of bringing transparency to commercial real estate and digitising operational workflows in buildings.
1. Leading with hardware is difficult
Enertiv started by solving a very difficult challenge: how to bring radical transparency to equipment-level energy consumption. Once that was solved, we led with our ability to deploy sensors to capture a new and powerful dataset. Over time, however, we learned that there were just as important problems with building operations that could be solved with software alone.
Now, we are leading with software first, building a working relationship, and deploying integrations to “power up” the experience and deliver deeper value.
2. Commercial real estate is drowning in point solutions
We recently performed a portfolio technology survey in partnership with a client of ours. It turns out that, just in our world of back-of-house operations, this 53-building portfolio had 50 unique technology vendors. For each of the categories, whether maintenance, utility management, ESG reporting, tenant utility billing, capital planning, building optimisation, and air quality monitoring, there were usually a handful of different solutions across the portfolio.
We’ve learned that it’s necessary to be a comprehensive platform so that portfolio managers can have a single source of truth for understanding how their assets are performing.
3. There needs to be a human touch
Despite the rapid adoption of technology, commercial real estate is still a relationship-based industry. That doesn’t just mean building a relationship to get a contract signed. It means ongoing client success to make sure that every stakeholder is onboarded and communicated with, in a way that makes sense to them.
The difference between rolling out across a portfolio and remaining stuck in a small scale pilot often comes down to communication.
4. Quantity of data is king
There is a lot of hype around AI and machine learning. The problem is that owners and operators have absolutely no way of knowing who wrote a better algorithm for analysing building data and delivering insights and optimisations. Fortunately, that’s usually not the question.
Instead, the major differentiator when it comes to competing algorithms is purely a function of quantity of data. Machine learning algorithms thrive on huge quantities of data; it’s an advantage that cannot be overcome with smarter data scientists or better data models.
5. Convert complexity into simplicity
Asset managers do not care how many kilowatts have been consumed. They do not want to know that air handling unit 4 did not shut down at its normal time or that chiller 2 is short cycling.
They do want to know how often maintenance schedules are adhered to generally. They want to know how energy costs are trending. They want to know how much of their utility costs they’re recovering from tenants. They want to know how many dollars have been saved.
The trick we’ve learned is to be able to handle the complexity of on-site operations, while still rolling data up into intuitive metrics that asset managers can understand, trust and act upon.
6. Data must be actionable
Raw data can be worse than useless, it can be downright counterproductive. It doesn’t matter if you have spent a lot of time and effort turning that data into bar charts, pie charts, heat maps, tree maps, scatter plots, or any other visualisation. What matters is the action that is to be taken as a result of the data.
In the world of building operations, this means providing operators with crystal clear identification of the root cause of an equipment issue, the specific schedule or set point that would be optimal, or specifically what to turn off during a peak demand event.
7. Technology is built with partnerships
It is impossible to build a technology platform in a vacuum. Even the most well thought out ideas rarely survive the real world. Because of this, all the best solutions Enertiv has created have been born out of close partnerships with owners and operators. This is not just getting regular customer feedback or creating a custom product for a specific portfolio, innovation partnerships are mutually beneficial collaborations that are essential for building valuable, scalable products.
8. Context is key
It doesn’t matter what the data point is. If an asset manager is shown any piece of information, the first question is invariably around context. Context generally comes in a few forms: comps, trends and normalisation.
Comps are when a particular data point is compared to that for another similar property in the same market. Trends are time series, basically an understanding of how things are now, relative to how they used to be. Normalisation revolves around the trust that this data point has been standardised so that it’s not simply a function of higher occupancy, different weather or other common variations.
9. Never forget the end user
Technology decisions are often made at the executive level, but it is unwise to ignore the influence of on-site teams. True, there is a tendency for on-site teams to be sceptical of change and resist technology. That doesn’t mean they can be steamrolled. A glowing report of the ease-of-use of the solutions, responsiveness of the client success team, and thoroughness of understanding of the particulars on-site is worth its weight in gold.
10. No one eats the elephant whole
The flip side of a comprehensive platform is that it can appear to be a big lift to transfer wholesale from a web of point solutions to a centralised system. That’s why modularisation and flexibility are critical. Not every solution is going to be applicable to every portfolio. Each portfolio should be able to pick and choose the options that work for them at a given time.