In previous posts, we’ve explored the dual nature of community growth: the Human Network (people/collaboration) and the Knowledge Graph (data/structure). We’ve also discussed how our “smarts” are embedded in our buildings, requiring a new kind of workforce. Today, I want to tie these threads together using a classic framework—People, Process, Product—specifically within the context of Smart Buildings.

When we talk about “smart” infrastructure, it’s easy to fixate on the sensors, the dashboards, and the AI. But without the people to curate the context and the process to model the knowledge, the product (the smart building) remains just a collection of disconnected gadgets.

People: The New Librarians of Infrastructure

In my post on Connecting the Dots, I described the “Human Network” as the engine of qualitative understanding. In the context of a smart building, who builds this understanding?

Enter the Knowledge Librarian.

We often think of librarians as stewards of books, but their true expertise is Knowledge Modeling—organizing information so it can be retrieved and used meaningfully. A smart building generates terabytes of data: occupancy sensors, HVAC performance, energy usage, and security logs. But raw data isn’t knowledge.

We need people—facility managers, data stewards, and domain experts acting as “librarians”—to define the ontology of the building. They answer the critical questions:

  • “What defines a ‘room’ in this context?”
  • “How does this temperature sensor relate to that specific air handler?”
  • “What is the relationship between occupancy and energy demand?”

These are human insights. The “people” component ensures that our data models reflect the reality of how the building is actually used, not just how it was engineered.

Process: From Messy Data to a Knowledge Graph

If “People” provide the context, “Process” is the workflow that turns that context into structure. This is the Knowledge Modeling phase.

As discussed in From Digital Literacy to AI Fluency, building a robust knowledge pipeline involves:

  1. Entity Resolution: Identifying that “AHU-1” in the maintenance log is the same as “Air Handler Unit 1” in the BMS.
  2. Semantic Layering: Applying the ontology defined by our “librarians” to map relationships (e.g., Room 101 IS_SERVED_BY VAV Box A).
  3. Graph Construction: Linking these entities into a queryable network.

In a smart building, this process transforms a static architectural drawing into a living Digital Twin. It allows us to move from asking “What is the temperature?” (simple data) to asking “Why is the temperature rising despite the cooling valve being open?” (reasoning).

Product: The Intelligent, Queryable Building

The result of combining this human expertise (People) with rigorous modeling (Process) is the ultimate Product: a Smart Building backed by a Knowledge Graph.

This isn’t just a building with automated lights. It’s a platform where:

  • Facilities teams can query the graph to find root causes of failures across disparate systems.
  • Energy managers can visualize complex dependencies to optimize consumption without sacrificing comfort.
  • Occupants experience a responsive environment that “knows” the context of their needs.

The “product” is trust. Just as a library catalog allows you to trust you can find the book you need, a well-modeled smart building allows you to trust the data it provides.

Conclusion

We cannot buy a “smart building” off the shelf. We have to build it, not just with concrete and cables, but with knowledge. This requires us to value the “librarians” in our organizations—the people who understand the semantics of our spaces—and empower them with the processes to model that knowledge.

Only then does the product—the smart building—truly become smart.