There’s no value knowing how a vehicle relates to the physical world, however its data may not properly be associated with the vehicle.
For example, your car outputs error code 0101, but the code doesn’t know it’s coming from a 2003 Toyota Tacoma with a recently installed third-party component.
The diagnostic data exists, but without proper association to the specific vehicle’s history and configuration, it can’t be properly interpreted.
For buildings, there are many such cases where valuable data is available, but it lacks the context to understand it from an analytical and controls perspective.
connorjcantrell•4mo ago
- What equipment is associated with a given data point?
- How does it's host device relate to physical space?
- Does this host device have a one:one, one:many, or many:one relationship with equipment?
Without bridging the digital to the physical, your data, and your Unified Data Layer as a whole, can't deliver optimal value. I call this the "Context Problem".
For Smart Buildings, contextualizing this data is traditionally a manual intensive process. We have built ML and AI systems to tackle this challenge at scale.