In June, our team was in Canada for another round of industry conversations, sessions, and networking. As is often the case after a few packed days of conference talks, a moment of quiet was needed for reflection. And for our team, this happened on the flight home. Scrolling through the news at 35,000 feet, an article in The Guardian caught our eye: a description of the underground network that uses water from the Seine to cool buildings across Paris.
It’s a great read even if you’re not coming from a district energy conference. But reading it right after a conference full of district energy, one thing stood out: Paris didn’t import a cooling system built for another city’s river, climate, or building stock; it built one suited to its own. That same logic applies to almost any choice, including district energy choices around steam versus hot water, centralized versus distributed, build versus buy. The technology changes, but the discipline behind the decision doesn’t. Digital transformation and data strategy is where we spend most of our time, so that’s what we’ll dig into here.
Solve Your Problem, Not Someone Else’s
It’s easy to get pulled toward whatever technology or methodology is generating the most buzz at a conference. But the most important first step is grounding the effort in your organization’s actual mission. Before adopting anything new, it’s worth asking:
- How can this help us build more reliable and efficient systems?
- What would actually help us deliver better, more cost-effective service to the people who depend on us?
- How does this support our broader sustainability and operational goals?
Two organizations can look at the exact same technology and need different implementations, because their missions, constraints, and customers aren’t the same. Letting your mission drive the decision, rather than the other way around, keeps you from adopting a solution built for someone else’s problem.
Here’s a simple gut-check before signing off on any new tool or platform: can you explain, in one sentence, which specific operational problem it solves for your organization? If the honest answer is closer to “everyone else is doing it,” that’s worth pausing on before you invest time and budget.
Right Information, Right People, Right Time
Solving your own problem, though, doesn’t mean the answer is simple. Your problem is likely a complex one, and complex problems rarely yield to a single perspective, approach, or dataset. You need several, applied in the right combination, to create a meaningful and sustainable solution. That’s why the organizations that do this well build an agile structure that can keep matching the right information to the right people at the right time.
Take something as basic as aggregating operational data. Sometimes you need it aggregated by time (hourly, daily, monthly) to spot peak demand or usage patterns. Sometimes you need it to aggregate spatially, comparing performance across buildings, units, or processes. Sometimes it needs to be aggregated by category, like equipment type or building classification, to compare like with like. And even the math changes depending on the question: a running total works for cumulative flow-meter data, but an average or median tells you more about fluctuating sensor readings.
Apply the wrong method, and you can end up with numbers that are technically accurate and practically misleading. It’s worth keeping an eye out for a well-known statistical trap here too: patterns that show up clearly in separate groups of data can disappear, or even reverse, once those groups get combined. Knowing which lens your question actually calls for (and being cautious about what gets lost when you zoom out) is as important as the data itself.
That same matching problem doesn’t just play out at the level of a single dataset. It shows in how you view your operations as a whole, too. A complete view of your operations really needs three different vantage points working together: the past, to learn from historical trends and mistakes; the present, to make informed calls in real time; and the future, where predictive models help you plan for what’s coming. None of these views substitute for the others. Depending on any one of them alone is how blind spots creep in.
Built to Evolve
None of this is a one-and-done exercise. Getting the right mix of perspectives, methods, and vantage points in place isn’t a milestone you hit once and move past; it’s a setup that has to keep working as your organization, your data, and your questions change. The most sophisticated dashboard or predictive model in the world won’t hold up if it’s treated as a finished product rather than a living one.
That’s why purpose, process, and review matter as much as the technology itself. Everyone on the team needs to understand why a tool or workflow exists and how it supports the bigger mission. Clear processes need to be built around it (defined roles, useful documentation, and dedicated training).
It also means planning for turnover. As experienced staff retire or move on, the reasoning behind why a system was built a certain way needs to be documented and handed off, not lost along with them. And then it all has to be revisited: monitored, measured, and adjusted as conditions, technology, and your organization’s own needs keep evolving.
Building a habit of continuous review is what keeps a good system from quietly becoming an outdated one.
Bringing It Home
Solve your own problem, not someone else’s. Pull from more than one perspective, dataset, and vantage point to understand it. And treat whatever you build as something that has to keep evolving, not a project you finish and walk away from.
These aren’t new ideas, but they are worth continuing to investigate, especially after a week spent watching an industry work through exactly these questions in real time. District energy keeps advancing because the people building it keep asking whether their systems still fit the problem in front of them, not just the one they were designed to solve years ago.
This same discipline applies just as well to a data strategy. If you’re working through how to build a data and analytics strategy that fits your organization, we’d love to talk.
Past Conference Takeaways
