Why Context Is Key in Smart Building Data: Max on Turning Telemetry Into Actionable Stories

In IoT and smart buildings, data is everywhere, but insight is rare. In this HR Journal conversation, Max explains how Mission Control turns building data into real outcomes, separating noise from signals, applying Digital Twins and ontologies for context, and pairing end-to-end ownership with curiosity to make insights reliable and actionable.

In the world of IoT and Smart Buildings, there is a literal ocean of data:

How does your team separate the noise from the signals, and how do Digital Twins and ontologies help your team use these models to predict a building’s behavior?

We separate noise from signal by starting with the outcome, not the data. What does the client actually care about: energy efficiency, tenant comfort, or asset longevity? We anchor our models to those priorities. Context is key: a temperature reading alone is noise, but mapped to a zone, an AHU, and historical trends, it becomes insight. Structuring the data properly turns telemetry into actionable stories.

Digital Twins help us understand relationships, not just points. A temperature spike may relate to occupancy, runtime, or weather. Ontologies give structure, letting us deliver analytics at scale across buildings and portfolios. At the heart of any dashboard is a clean, reliable model, without it, insights are just disconnected numbers.

Tell us about your first 90 days at KODE:

What was your ‘Aha!’ moment about the scale of impact, and which soft skill has been your secret weapon since?

The first 90 days were about listening & learning, to the tech, the product, and the people operating the buildings. I focused on how data moved through the platform and where decisions were actually being made. The ‘Aha!’ moment came when I realized we weren’t just reporting, we were shaping building operations and strategy at scale. 

Curiosity – that’s the secret soft skill weapon in Mission Control. Data rarely behaves as expected, and asking “why?” uncovers patterns others miss. Communication is a close second, insight is only valuable if someone can act on it.

How does End-to-End Ownership translate to the Data team?

End-to-end ownership starts before a dashboard exists. We need to understand the client’s pain points, shadow their daily operations, see what modules they’re hoping to leverage, and know what devices are installed. Only then can we build analytics that truly solve real problems. Ownership means carrying that understanding from raw data through to a finished dashboard.

Advice for someone moving from traditional analytics into IoT and Smart Buildings:

Learn how buildings work. SQL and dashboard can be taught. Understanding HVAC, BMS, asset hierarchies, and operations is what makes you effective. The best analysts bridge digital data with physical reality.

If the data from our buildings could talk, what would it say?

“I can do more than you’re asking.” Most buildings are under-optimized, systems drift over time, and small operational tweaks could have big financial and sustainability impacts. Often, issues aren’t failures, but misconfigurations.

What’s your favorite KODE feature, and why?

It’s the people. The Mission Control Data Analytics team is a feature in itself, building micro-products tailored to each building, user, and pain point. Every dashboard that saves money, highlights a trend, or simplifies multiple screens comes from analysts who know the building intimately.

The team relies on every KODE feature to work properly, trust in the platform is key, and the wider KODE team always delivers. None of this happens without their day-to-day effort. It’s astonishing how quickly client requests become core features. I’m amazed daily by the intelligence, drive, and passion across KODE. If I had to pick a favorite feature, it’s the team making it all happen.

The takeaway from Max is that end-to-end ownership starts long before the dashboard. You begin by shadowing day-to-day building work, learning the pain points, what devices are installed, and what outcomes matter most. Then you carry that understanding from raw data through to what the user sees. And curiosity is what keeps you asking “why?” until the insight is clear enough for someone to act on.

Coming soon, another HR Journal conversation. Stay tuned!