6 min read

Nexus #16 (3/31/2020)

Getting started with smart buildings; Sharing a great data normalization standard; What GIS data can tell us

👋 Welcome to Nexus, a newsletter for smart people applying smart building technology—written by James Dice.

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And by the way: if you missed last week’s edition, you can find it here.

Disclaimer: The views, thoughts, and opinions expressed in the following text, on the Nexus website, and on the Nexus podcast belong solely to the author, and not necessarily to the author's employer, organization, or other group or individual.


Here’s an outline of this week’s newsletter:

  1. A quick note: What I’m thinking about this week
  2. Our LinkedIn discussion: are FDD rules valuable?
  3. Getting started with a smart building strategy
  4. GSA’s Data Normalization Standard and why you need one
  5. My intro to What GIS Can Tell Us

from Nexus:

1. a quick note

As the impact of COVID-19 continues to unfold in front of our eyes, I’ve been thinking a lot about the work of investor, mathematician, and superstar author Nassim Nicholas Taleb. His book Antifragile is one of my favorites. In case you missed it, I unpacked this concept and how smart building technology can help in this week’s Deep Dive: COVID-19. Here’s a teaser for a summary video I’m working on:

Read the whole deep dive here.

In other news:

2. are FDD rules valuable?

We had some great input on this week’s LinkedIn prompt. My take:

  • Rules are not valuable in and of themselves. What’s valuable is the “job” being done for the building owner.

  • These sorts of roadblocks are confusing and ridiculous from the owner’s perspective, who is already taking a leap of faith as an early adopter. If the whole “job” is being done well, they will tell the others. FDD will then enter the mainstream.

  • Regardless of your approach, the owner deserves to know several things about your rules:

    1. Whether you’re identifying “true positives”

    2. Whether you’re avoiding “false negatives”

    3. Whether they need to modify your rules for their situation

    4. How to fix the faults

Add your take on LinkedIn



+ GSA’s Data Normalization Standard—A vital best practice for analytics success is to define controls and analytics standards that ensure the transmission of information from edge to cloud. Human- and machine-readable point names are part of this effort, but they are not enough. Standards should also include standard equipment types, system types, sequences of operations, required points, and semantic modeling.

The closest shareable example I’ve seen is this document from the U.S. General Services Administration (GSA). Here’s their stated philosophy behind the standard:

To allow a human to instantly identify a device or object simply by reading the name. At
minimum, the name must indicate which building, what equipment/system to which it is
associated, the type of object, and the object’s primary function. The parts of the name
must be human-readable using standardized abbreviations. These standardizations
allow an operator or analyst to read, search, sort, group, and filter objects with ease.

A computer interpreter of any type would be able to use the building and
equipment/system indications to group objects. To make the function of an object clear
to a machine, the object type/function portion of the name is composed of standardized
“camel-cased” abbreviations that a computer can break apart and use to automatically
apply metadata tags. These tags allow applications, such as analytics engines or
CMMS, to interpret information directly


+ How to get started with a smart building strategy (Propmodo)—My friend Joe Aamidor’s wrote an excellent essay on how building owners can establish a strategy to deploy modern technologies.

I was taught to think about strategy by Strategic Coach founder Dan Sullivan and my former boss and partner Doug Sitton. Strategy should always come before planning, which should come before action. Most people jump right into something new—either straight to action or straight to planning out the action. Good strategy starts with plotting out the obstacles to what you’re trying to achieve. What landmines need to be avoided? Then planning and action flow from there. Joe’s article aligns with that and he highlights many obstacles to smart building success and how to avoid them.

One highlight: What it takes to run a successful smart building pilot:

A common theme for many real estate firms that have been deploying smart building technologies for years: they recognize the risks and try to focus the scope of their initial experiments around a focused and definable problem. While it’s easy, though time-consuming, to engage solution providers to learn about their offerings, it is not the best way to start a research and procurement effort. Instead, internal conversations with key stakeholders should be conducted to identify where there are problems that could be solved with smart building technologies.

Certain potential, identifiable problems many buildings face include tracking energy consumption and reporting it to a third party, addressing an aging facilities management team staff, adoption of condition-based equipment maintenance procedures, or an automated way to bill tenants for energy use

Only after problems are prioritized, internal investigations have been done and goals have been set is it prudent to search the market and identify vendors and solution providers.

One key statistic for analytics firms: only 29 percent of firms are using data analytics techniques, and 54 percent do not have current plans to use them. Lots of room to grow, friends.

I highly recommend the whole article if you’re helping building owners put together a smart building strategy.


+ What GIS Can Tell Us (Dude Solutions’ Operate Intelligently Podcast)—I have an upcoming project that leverages geographical information system (GIS) data for analytics and automated work orders, so I’ve been diving into resources on GIS. If you’re new to this technology, this podcast is a great introduction.

Some highlights:

  • GIS is simply geographically referenced data. It can describe both the locations and characteristics of spatial features.

GIS historically has been more for things that are outdoors. And that's changing—indoor-based GIS is also becoming very powerful.

You can use it just like GPS to know where you are. So that's opening up a whole new world, as well as with the 3D modeling so that it's not just a flat earth, you know, we have that vertical component as well. It’s like a topographical map.

Think of GIS as layered data, kind of like an AutoCAD file

a good example is Google Maps. So when you're looking at a Google Map, you're seeing a couple different pieces of information at once, you might be seeing the street layer, and you might be seeing restaurants or hotels. You're seeing all kinds of different information together on one map. So you can see all the spatial relations and how everything interacts with each other.

Location information increases operational efficiency, combines well with mobile and digital twin technology, and supports resiliency

having a reference base of where things are an organization, especially true for anybody that has any assets are spread out over a large facility (…), knowing where those things are, a lot of times is locked up in people's hands. So if somebody is out sick, one day, somebody retires, where's that information?

There is a town and they had a water leak and they were searching and searching and searching for water valve to turn that off, so they could stop the league get it repaired. And they just can't find this water valve. (…) if they had a GIS system combined with some modern technology, like the GPS on your smartphone or tablet, they would have been able to walk right there and say, I know it's there, I noticed flower bed, but I've got confidence in this data that I can literally dig in and find that to get everything repaired. So it can be a real big time saver.

GIS data can amplify the power of analytics platforms

And another one is the ability to find additional patterns in data that you can't find otherwise. A great example of that if we think of somebody who is running a water wastewater system, certain pipes might have additional failures are in need of additional maintenance. And when you just look at it, why is that?

Using GIS, there might be different layers that can [be used to determine the root cause]. Having different soil types like clay versus sandy might impact settling for those pipes. Somebody's got some heavy equipment out for the roads, you know, maybe that has damaged the pipe below.

One future application of GIS for buildings is in augmented reality (AR):

And so underground features, again, those pipes and other things, it's like you're literally seeing through the earth and combining with that someone back in the office can see that as well. So yeah, it's really, really cool technology.

OK, that’s all for this week—thanks for reading Nexus!