Mapped

More data ≠ better decisions Right context = decisions That’s the difference between data you collect and data you can actually use. We break this down in the session: bit.ly/4eclYwl #PropTech #DataInfrastructure

More data ≠ better decisions Most teams get stuck trying to model everything. What actually matters is having enough context to act. #PropTech #DataStrategy

Everything you see here is accessible via API. Not locked in a UI. Not buried in dashboards. Not export-only. That’s what makes this data usable across platforms like Microsoft Fabric. See how it works in practice: bit.ly/48n0Z6l #MicrosoftFabric #PropTech

If your building data only lives in dashboards, it’s not that useful. The value shows up when it’s accessible, structured, and ready to move. That’s what makes this data usable across platforms like Microsoft Fabric. #PropTech #DataInfrastructure

At portfolio scale, you’re not dealing with one environment. You’re dealing with layers of decisions, vendors, and naming built up over time. Approaches that worked in a controlled setup start to strain. What’s the most complex environment you’ve had to deal with?

Most buildings feel manageable on their own. Then you step into something like an airport. Dozens of buildings Systems installed decades apart What worked in one building doesn’t translate cleanly to the next. #PropTech

Too much data. Dashboards everywhere. But the work orders are still unclear. We will show how to fix that. Join us April 15 (virtual) at NexusCast #2, hosted by @NexusLabs → bit.ly/4tzYMNi #CMMS

Most buildings start with no data. Then they add sensors and suddenly there is too much of it. So teams build dashboards to make sense of everything. But the work orders are still inconsistent or unclear. The problem does not go away. It evolves. #PropTech #SmartBuildings

This came up in a recent Realcomm discussion. Sharing our portion for those working through the shift from pilots to scale: na2.hubs.ly/H04CLJz0

In a recent webinar, BGO shared they’re trying to move beyond pilots to scalable rollouts. We see this often: what works in one building starts to break across many. Multiple pilots. No clear path to scale. Where are you right now: piloting or scaling? #Interoperability

Most teams don’t struggle to run pilots. They struggle to move past them. We’re seeing more operators shift from “prove it works” to “can this scale across 50 buildings?” That’s where things tend to break. Pilots are clean. Portfolios aren’t. #SmartBuildings #CREtech

One of the most common integration issues we see: Point names that only make sense to the installer. No clear naming. No units. No context. So even if you can access the data, it’s not usable. This checklist helps catch this early → bit.ly/41aWmsc #SmartBuildings

If your building data looks like this: AI_104_OBJ You’re not ready for AI, analytics, or automation. Because without clear naming, units, and context, no one knows what they’re looking at. And that’s where integrations start to break. #PropTech #DataQuality

What does normalized building data actually look like? Not dashboards. Not exports. Not spreadsheets. Structured, queryable data that’s consistent across buildings. 👉 Full walkthrough (including Fabric): bit.ly/4tkRD32 #SmartBuildings #PropTech

Dashboards didn’t fix building data. Spreadsheets didn’t either. They just made bad data easier to look at. If the underlying data isn’t structured and consistent, nothing downstream actually scales. #SmartBuildings #PropTech

Operational intelligence can scale. Fragmentation is what slows it down. In this Realcomm webinar, we explain why data alone isn’t enough. Watch the replay + slides: bit.ly/47yJdgf #SmartBuildings #CRE

Operational intelligence can scale. But right now, it doesn’t scale easily. Every building speaks a different data language. So even simple use cases like chiller fouling detection get rebuilt again and again. That’s the bottleneck. #SmartBuildings #PropTech

Most “bad data” problems aren’t analytics problems. They’re collection problems. If the first mile relies on manual steps or spreadsheets, everything downstream is compromised, no matter how modern the stack. How close is your pipeline to this? #DataPipelines #Ops

Quick test: Does your data pipeline involve a notebook? In many environments, it still looks like this: Meter → notebook → photo → email → Excel → ops platform Every step adds delay. Every handoff adds risk. And then we wonder why no one trusts the data. 🤔 #DataOps

Most teams don’t estimate integrations. They guess. One weak API, missing endpoints, poor docs, or unexpected rate limits and that "2‑week integration" quietly turns into 2 months. It isn’t your team. It’s hidden integration complexity. #APIs #Integration