Latest / Wired to Build / The Google Maps of Infrastructure with Itzik Malka (4M Analytics)

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The Google Maps of Infrastructure with Itzik Malka (4M Analytics) · Wired to Build

0:00
Hey, what's going on everyone? Welcome to Wire to build, a
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data-driven technology focused podcast digging into how
0:06
building owners and operators use technology to manage the
0:09
full life cycle of their projects on the Carabella, your
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showrunner and curator. And I'm excited that you're
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joining us today to talk about the people that drive the change
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in our industry. You today I'm joined by Christy
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Hunt. Hey, Nick, good to be back.
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How are you doing? Doing well, excited to welcome
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you. And it's the four M analytics
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back to the show. It's like you want to quickly
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reintroduce yourself for folks who may be turning tuning in for
0:35
the first time. Yeah, thank you.
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Happy to do it again. It's like Malka, the CEO and Co
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founder of Four M Analytics based in Austin, TX with my wife
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and four kids. Happy to be here today to talk
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about Four M and the first subset, this mapping engine
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database, centralized database that enables you to see what's 1
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beneath the ground. I'm excited to pick up where we
1:01
left off. It's such a, it's an interesting
1:04
problem. You know, I spent most of my
1:06
year practicing architecture worried about what's above the
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ground. But what's underground is so
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important as well as, you know, as we get more in touch with the
1:15
infrastructure, the roads we drive on, the water we drank,
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the electricity we use, all of this is deeply connected and
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ties to public systems that go into the services that make our
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lives easier. And I'm really excited to
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continue to learn about the work that you're doing in this space
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and create an understanding of why this work matters so much.
1:37
And so to maybe like jump in, let's do like a little intro
1:40
course. What does structured utility
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data mean? Yeah.
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So, so utility data today is not standardized, means you have
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records, you have imagery, you have even digital systems that
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you use in order to kind of understand or depict what runs
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beneath the ground. But it's not utility data.
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We are aiming to standardize utility data, as you know, as
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something that people use and get not just the not just the
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location, but also the metadata attributes that comes with the
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location of a line or a point that we are representing on our
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platform. So I would say it's the location
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and presence and the metadata attributes of every line and
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point that representing a network or a system that we as
2:40
people consume. There's that IT services on a
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daily basis. So I think that's that's the my
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definition to that. And, and when I mean metadata,
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it's going to be the sector, it's going to be the sub sector,
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it's going to be the name of the owner.
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It's going to be the elevation, the line length, the
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installation date, the position, the depth, the material, the
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specific material and the source which you used before the
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amalgamation to decide and to confront or maybe like a like an
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evident that you could use to say, yeah, that line exists.
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And this is how I know it. And, and it's not just the
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source that we provides, it's the image science that we apply
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to it. So every line or a point that we
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present needs to be visually verified.
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So that's the combination of multiple sources with the
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imagery science. That's great.
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Everyone's familiar with GIS, our our construction listers are
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certainly familiar with Asbelts. How does this differ from those
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traditional outputs from those traditional tools?
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Yeah. So I think GIS is a hosting.
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It's, it's a, it's a hub which you could upload data.
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And if you take like every entity or every Muni, probably
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most of the Muni's, most of the Munis in the US has some
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instance, instance of of a GIS engine.
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But the, the challenge with GIS engine is the feedback loop with
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the data. So it could host data, but it's
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not data provider, it's not a data producer.
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And if you are having an S built, that S built is is an
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isolated document. How can you take the document
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and vectorize like make it digital to in order to take it
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back to the GIS systems? Most organized established
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organizations that has GIS departments will will use many
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people, but eventually they will vectorize 2030% of their
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deliverables. So you ended up with some data
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that represent some truth, but it's not something that you
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could rely on. So, so managing data and you
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know, hosting data, that's great.
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But for production of data, that's where we come comes to
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play. We are, I'm, I'm like, I'm
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referring to foreign as we are the plumbers that comes with the
5:22
water. The data is the core of our
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product and we are producers of data.
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So we create the lines, we we generate the lines, we generate
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the points that we've given you. We're using, we're using
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references from from other sources, but eventually the
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amungamation help us to distribute the foundry of what's
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there below and above and above. It's not just the below because
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the above indicates that something runs below.
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So we're giving you the above and below the network of all
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networks above and below. This is this is really
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interesting because I'm thinking of the episode we recorded
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yesterday, which is coming out last month and he Matt Caldwell
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at hyper. They talk about the procurement
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of equipment and what they're doing is providing a service
6:14
Oregon, a system that's easily relied upon where the buck stops
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with them. And I think you just did kind of
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it's the same exact theme where you're verifying this
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information, you're producing this information.
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There is a a touch that's being made that's different from GIS
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where it's essentially a filing cabinet that everybody throws
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the documentation into. And you don't know how the
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quality of that documentation hasn't truthfully BeenVerified.
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It could be design work. It's not as built.
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All of that nebulous goes into you create an uncertainty in our
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space where the key difference here is really getting
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verifiable and trusted information.
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And so, you know, now having something like this, how does
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this get translated into usable insights that general
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contractors, departments of transportation and other
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infrastructure owners can rely on?
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And what how have they been using it?
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Do you have like an early use case that you can share with us?
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Yes. Yes, definitely.
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So since we started like that was our go to market strategy
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was who is is going to be the first audience that will be very
7:28
direct with us about how good it is.
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So we said we're going to go and try to sell to the engineering
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entities. Why engineers are, you know,
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people that has opinion and they, they're not going to trust
7:45
you so fast. So I said, if we're going to get
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over the threshold of their trust, if we're going to be
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trusted by engineering firms, it's going to be good enough to
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go forward. And, and I said engineer first,
8:00
like the AC, but the engineering and construction companies 1st.
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And then once we got trusted, I said, now let's go to the owner
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of the right of way, which is the dot and dot like
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transportation, it's not just like dot, it's the dot, it's the
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MTA, it's the transit and it's the counties and the Munis.
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And you don't have to go Muni by Muni, you just go by the the
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biggest ones. When we started the company and
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we said we're going to proactively map states, we only
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we based on publicly available sources like blueprints, like
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imagery and Geo servers. We said, my assumption was that
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95% of the network will be run within the right of way.
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And that was that was a good assumption because because of
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that we got access to the data and we invested heavily to build
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a robust infrastructure to digest, dissect, to amundement
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and to distribute a foundry of the data.
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And now we said, OK, it's the good enough approach, but let's
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take it to the preliminary phases of linear construction.
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So today it's boots trucks and locate a device that starts if
9:17
the if that's the damage you mentioned, it's the call before
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you dig 811. It's three days before
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excavation begins. But if I'm going to go to
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professional services to the Sioux sub surface utility
9:27
engineering, it basically starts on 60% design phase to 90%
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sometimes before that, but it's too late and too little.
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So I said, what if I'm going to give them data for the pre
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millionary phases like feasibility studies, NEPA
9:42
studies, what I'm going to give them data for the bidding, for
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the pre planning for the front end design.
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So we started from early beginning of construction, like
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the construction development phases.
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And now I could say anywhere from a minute before excavation
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begins, like the pre con to the upstream.
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And and that was a good segue because you don't need accuracy
10:07
at the beginning. You just need to have the
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comprehensiveness. You need to know who are the
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owners that you need to coordinate with.
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What type of lines, How many crossings per mile?
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How many of them will be 20 inches?
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How many of them will be gas pipelines?
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How many of them will be high voltage?
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Where are the features located? Show me the evidences.
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So that data worked and become like really trusted for the
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preliminary phases. So and that's where we started.
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And I think we, it was a blue ocean.
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So nobody was selling there. Nobody was offering there
10:42
because it wasn't feasible to build.
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But now I I would say cautionly that we in a good place that I
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think that place belongs now to foreign.
10:54
That's fantastic. One of the things that's always
10:56
interesting to us as we talk about technology and and as we
10:59
see adoption is those successes and and comparing them.
11:04
So we talked about public versus private sector, You've talked a
11:07
lot about the the DOTS, departments of transportation
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and agencies like that. Why do you think they're
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adopting this technology faster than commercial owners?
11:18
Yeah. I think commercial owners
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adopting it and using it and we see like how valuable it's
11:27
become for their practices. But I think now days with the AI
11:33
strategies, every company, every company out there, every public
11:37
sector company as almost CIO Chief Information Officer, and
11:43
they have like AI strategy in their objectives is the, I would
11:49
say implementation of AI solutions to make their
11:55
organization much more productive.
11:57
And I think here's the timing aspect and what we bring to the
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table. We started a company before
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anyone knew, you know what AI means.
12:06
Now it's easy. Everybody is like, if you throw
12:08
in a stone out of my building, you're probably going to strike
12:11
a CEO of a tech company that uses AI.
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Yeah, but we started as a really AI native company.
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It's a really robust, complex. Think about the, you know,
12:22
collection processes. Think about the amalgamation,
12:25
think about the line generation. We know we, we are generating
12:30
lines without any documentation whatsoever just because we could
12:35
identify hydrant and we could identify the second hydrant and
12:39
the third hydrant and we could connect between those 3 hydrants
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to be connected to a single line.
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So we could draw the line puristically without any human.
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Yes. So I think public sector
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nowadays are more open minded and receptive to technologies
12:58
and especially to AI, as long as it's not like offending their
13:02
privacy policies. But that's timing.
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That's, you know, nobody scares. And if you have transparency,
13:08
nobody scares from AI anymore I think.
13:12
Yeah, the AI is certainly broken down some barriers to using
13:16
different technology and and different adoption.
13:19
It feels like maybe that changed the mindset of some of these
13:22
organizations rather than, you know, oh, here's some new
13:26
technology vendor with something that's cool to sell.
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I think it's changed their minds in ways that nobody expected.
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And it's, it's interesting to, to like, think about the, how
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that adoptions changed with the munis, which I tend to feel have
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often times been slower to adopt.
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They're very tend to be set in their ways where now, you know,
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the AI has really, the AI boom has really forced people.
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And I think IIJA Bill has also forced people to think about
13:54
technology and process and how that that needs to drive our,
13:58
our public sector forward. And so it's not talking about
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like changing budgets or changing workflows or risk
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aetite. It's well, this is what we can
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do. And I think that, you know,
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you've done a really amazing job.
14:11
And if you haven't listened to the first episode, it's like
14:13
trying to make the Google Maps of subsurface utilities, which I
14:17
think is so cool. And So what does like, let's
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paint that picture a little bit further.
14:23
What does a fully mapped subsurface world look like?
14:26
And you know what? Are you hoping to connect?
14:29
Yeah. So I'm hoping to be recognized
14:33
by any entity as the stand out of, you know, the location
14:39
presence of the network that drives services to our life.
14:43
I I truly believe that, you know, we working on
14:47
construction, but I truly going back to the crystal ball.
14:50
I truly believe that I don't know as ACEO the potential of
14:54
monetization to the data that we creates.
14:56
And I assume there are many products that will be built upon
15:00
our infrastructure. You could it's it's a multi
15:03
vertical and I think it's like somebody told me forum reminds
15:07
him the Google search before monetization, before the ads
15:12
Meta before really realizing what to do with social media
15:17
yeah and I and I truly believe construction is like it's the
15:20
it's the impact that we want. It's not construction yeah, it's
15:22
it's it's construction is part of it, but it's the resiliency
15:25
to the infrastructure that drives services to our life.
15:27
It's the future opportunities that we could unlock.
15:30
If you want to build more, let's think about EV charging, think
15:33
about data centers, think about, you know, development of really,
15:40
you know, disconnected areas that doesn't have even broadband
15:45
fiber to the ARM project. So many, many initiatives think
15:49
about like roads, widening of roads or building new roads to
15:53
some places. I think we are touching every
15:57
aspect over being and to me to be part of this story.
16:02
I don't care how I just fascinated and so fortunate to
16:07
be working on that challenge and that kind of invaluable product.
16:12
And and and I'm probably going to build upon that other
16:17
products, but, and others will build other products based upon
16:21
that. But that's, that's what I feel.
16:24
I feel like we're doing something great, greater than
16:26
what we could consume or really realize.
16:29
I think that's like, you know, one of the things I'm really
16:32
picking up from you is just like you, you've really captured on
16:35
the like having a true dream of like what you could see that
16:39
this becoming and not so caught up in the details of the
16:42
monetization as much as solving the problem and, and creating a
16:46
space for people to get real value from the work that you're
16:51
doing and not afraid to leave a little bit on the table for
16:54
others to help augment. And I think that that, you know,
16:57
looking back at our last episode where you talked about this is
17:00
something that supplements the experience and adds value to
17:04
rather than becomes just another thing is, is so important.
17:07
And so, you know, do you really see this becoming like the
17:10
shared infrastructure layer across the industry?
17:13
Where are you going to compete with call 811 at one point in
17:18
time? Where is that something where
17:19
you start to have a synergy together with them and help them
17:22
do their job? Definitely complementary and I
17:26
think there is a saying like call before you dig.
17:29
I think we need to become call before you dig, but format
17:33
before you call or during or after it.
17:36
It it, it runs together and I think we Better Together.
17:40
I don't want to replace or displace anybody, not even the
17:42
soup. I think we have room for
17:46
everybody. In fact, I think with our
17:49
product they got to be much more adopted because now it could be
17:53
much more accessible and affordable.
17:56
It was very costly and timely, but if I could take them from
17:58
months to weeks and from like 7 figures to six figures, it's,
18:05
it's an opportunity for them. And that's something I, I would,
18:08
I would never say that I'm going to replace engineer.
18:13
I would make him 10X or 20X much more productive.
18:17
But I don't want to replace him. And and if you look ahead in
18:20
2036, the industry will become from 12 trillion to 23 trillion
18:27
and you're going to have a labor shortage of 50 through.
18:32
So I want to tackle that. Yeah, I think that's amazing.
18:35
It's something that we've talked a lot about between the labor
18:37
shortage, the challenge in the industry and then how we have a
18:41
lot of these tools and they should all go together.
18:44
And you know, we find that our, the owners that we work with a
18:48
lot, they have a lot of similar systems that do similar things,
18:52
but they figure out how they want to use it in a way that
18:54
works for them. And it becomes more
18:56
collaborative and much more of an open and environment of
18:59
here's our tech stack, here are the tools we use.
19:01
And this is what they all can do.
19:03
And sometimes they need to work together and exchange that
19:05
information. I think that same vision of,
19:07
yeah, why not do both? Why not do call before you dig
19:11
and for M and then even compare and contrast and get better data
19:15
and make better decisions rather than relying on just one piece
19:20
of information that you know who knows you.
19:22
It's not trust but verify. I think we talked about a little
19:25
bit earlier too. So I I think that's really
19:27
exciting to see going forward with the mindset of we're going
19:32
to work together. We're going to.
19:33
We would expect people to do use more than more than just us, use
19:36
more than our tool. That's exactly the intent.
19:40
And, and I think, you know, you talked about trust and I want to
19:43
talk about how you validate the data a little bit, but the the
19:46
trust, I think probably stems just even from your vision of
19:49
not wanting to replace and wanting to supplement the people
19:52
who can do the work and understand the local impact.
19:55
You know, there is so much fear, I think of what technology can
19:59
be doing to quote eliminate jobs.
20:02
But what it really is is like these are new tools in the tools
20:05
belt that just help us do artwork better.
20:07
And I. I really think you'd like kind
20:09
of bold underlined and like shouted out that and I, and I'm
20:14
curious, like on that trust journey, what was it like to,
20:17
you know, work with those engineers who weren't ready to
20:21
adopt you at the gate? What was it like to cultivate
20:23
trust with them and prove out how you validate the data so
20:27
that they can rely on you? Yeah, this is a good question.
20:30
But before that, I want to go back to talk about like, you
20:33
know, the differences of sending people to the field versus to
20:37
believe in like digital delivery that we provide to our
20:42
customers. And I think when I'm, when I'm
20:45
thinking about like my nemesis, like what, what's the
20:48
incumbents, What's the status quo looks like today?
20:51
I'm thinking about a company, beautiful company called USIC.
20:55
They have like 15,000 locators that have been dispatched on a
20:59
daily basis from Sunday through Monday.
21:01
And, and yes, they, they are like the tier of validation
21:05
before somebody wants to break a ground, but at the end of the
21:08
day, you know, they're going to be going to places and they can
21:13
do it in any conditions. They can do it, you know, during
21:17
the day, they can do it during like extreme severe weather
21:21
conditions. And they can do it because they
21:24
don't have enough people to send out.
21:26
And the capacity is like is pushing them and eventually, I
21:30
think adopting and creating like capacity, solving capacity
21:35
through digitization. It doesn't mean you're going to
21:38
replace people. And, and I'm telling you about
21:40
it because of what you ask about the products that I'm collecting
21:45
their flags, their marks. And if I were to displace them,
21:50
I'm going to, I'm going to be in a position that I have no data
21:54
to compare my data that's just been created by their outcomes.
21:58
So it's a, it's a catch 22. So I think we need to live
22:02
together, but we need to have like they're going to create the
22:05
physical twin and I'm going to create the detail twin.
22:10
And I think one day it's going to be like they're the same
22:13
physical that we create. It's like very like powered by
22:16
AI, verified by humans, by USIC. And, and definitely I think
22:20
there is place for both. You don't have to replace
22:24
anybody. You just need to make them much
22:26
more productive and contextual educated and well preferred to
22:30
reduce the damage eventually at the end of the day.
22:33
Sorry, I just wanted to go back to address what you said about
22:36
validation and and validation is like can you provide me an
22:39
evidence that shows that the line exists?
22:43
Is it abundant line? Is it active?
22:47
And if you are not able to verify those, so puts a mark on
22:51
it. That's the trust, that's the
22:53
transparency that you need to use in order to convince your
22:57
engineering firms. And that was really tough for
23:00
them at the beginning to to adopt.
23:03
But I said to them, you know what, don't take my word.
23:07
Just let the data speak. Go and do full blowing surveying
23:12
with any tool that you think of. And let's compare the data just
23:16
sent out to you. And once you see the
23:18
differences, the discrepancies, like you said, Christy, if you
23:23
have two systems, it's easier to see the discrepancies and both
23:27
of us are greater than one of us.
23:30
Yeah, I, I agree with that. You know, especially if you both
23:32
have the same discrepancy, then that's a, that's a really big
23:35
red flag of, of something not being right there.
23:38
Exactly. I want to talk a little bit
23:40
about AI. We touched on it a little bit
23:41
earlier. It's everywhere, not just for
23:43
making images of cats with laser beams, which is now one of our
23:47
favorite things to do here in our spare time.
23:51
But how? How do you see structured data
23:55
enabling things like machine learning or predictive modeling?
23:58
How is it going to help that out?
24:00
Yeah, I think for if you're taking the construction value
24:05
chain for every aspect of it from you know, feasibility
24:09
studies, the selections from pre planning for the planning for
24:14
the design, construction and maintenance, you have predictive
24:20
analytics for every phase. If you want to know what's the
24:23
cost of sending people to the field, just, you know, tap ask,
24:26
you're going to immediately cost estimation.
24:29
If you want to speak with owners immediately, you could show me
24:34
who's The Who are the owners within that collido?
24:36
Can you reach out to them and ask them to share with me data?
24:40
Can you submit ticket? Can you submit like permit?
24:44
Can you do an easement? Do you know who is the owner of
24:48
this property? Because if it's commercial
24:50
property, you need to speak with every single owner of that
24:54
property. It's going to take your
24:55
permitting process from months to more than a year just to find
25:00
the people. AI.
25:02
It is a really, really significant enabler to
25:07
consolidate those many workflows and many products into one.
25:12
And we've built our product from the very beginning as a data
25:15
play, but now comes the funny part of building products on top
25:20
of it that really, you know, consolidate those many workflows
25:25
that you need to, to do. And eventually, at the end of
25:28
the day, it's one place you want just one focal point, just one
25:33
and, and maybe more than one, but not a few dozens.
25:37
And I think AI drives that. And, and by the way, AI to me,
25:42
going back to what I said about the displacing people is going
25:45
to displace people who are not using AI.
25:48
That's that's the only threat. Yeah, anybody who's using AI
25:52
realizes pretty quickly the I don't need someone to write this
25:56
for me because I can now generate it pretty quickly going
26:00
back and forth over 1/2 hour of lunch.
26:02
Or I don't need, you know, to hire a deep, you know, a
26:06
creative person because now I can draw.
26:09
And that's the thing of like democratization and digital
26:12
enablement that I think is really cool about our AI is that
26:17
now more people can be contributing to something when
26:22
they maybe didn't have the, we took this class in architecture
26:26
one O 1 is like modes of communication and we learn all
26:29
the different ways of drawing from painting to pencil to
26:34
commute. Computer aided design at the
26:36
time, AI was not quite around then.
26:39
That modes of communication and the philosophy that not
26:42
everybody speaks the same language and not everybody sees
26:45
things the same way, I think is so neat.
26:48
And so when we can enable people to draw, enable people to write
26:52
music, enable people to make decisions about their
26:55
infrastructure with intelligence, I think really
26:58
incredible things happen. Because innovative thinking
27:01
starts with thinking differently, allowing somebody
27:04
who couldn't think before to now start to think in a space is
27:09
what opens stores to new ideas and new ways to do things.
27:13
So I'm with you all the way on this.
27:16
And to just kind of tie it back to construction and, and the
27:19
work that you're doing, I think people sometimes go back to
27:24
like, it has to be perfect. And we, we talked about this on
27:26
the last episode of like, well, if we, if it's not 100% perfect,
27:30
we're just going to do things the old way.
27:32
But construction inherently is all about managing and
27:35
mitigating risk. The risk exists.
27:38
Building is a risky business, whether underground, above
27:41
ground or both. And in between using AI, using
27:45
tools like yours, it's, it's about uncovering the problems as
27:49
early as possible so that we can come up with a plan to approach
27:53
them. And if we don't know what's
27:54
underground, then we have to pretend something is and then we
27:58
have to look. And so having systems like yours
28:01
that can help us get the information we need to be like,
28:05
yeah, we do need to call before we dig because we do know from
28:08
the model that you've roduced that there's going to be
28:10
something over here we can now plan.
28:14
We can actually, you know, have forecasted the cost for doing
28:18
that so that it's not a surprise because where construction gets
28:21
frustrating is the surprise. I didn't have a plan, I didn't
28:24
have a budget. And now I got $100,000 change
28:27
order that I do not know how to field.
28:29
And that's where the pain really hits.
28:32
So I really like this idea of the the vision of how do we give
28:37
people more information so that they can then manage and
28:41
mitigate that risk. And I'd love to untie this to my
28:45
next question, which is where you know, how are you seeing for
28:49
am actually moving up the pipeline of this construction
28:52
value chain to planning and design that helps people get in
28:57
front of that risk? Yeah, that that's a good
28:59
question and a good segue. I think about the the trust you
29:05
give to AAI products. I think it's it's only a matter
29:10
of, you know, AI risks and concerns is about the
29:14
hallucinations that that you get by using AII think the future
29:19
belongs to who owns the data that AI could sit based upon.
29:25
And eventually, if you own the data or or you are partnering
29:30
with data companies, you could be like a really trustworthy
29:36
products with AI. And I think I think to your
29:39
question about like, you know, eventually at the end of the
29:42
day, you want to, you want to be positioned as, you know,
29:49
provider of data that being validated before you go into, I
29:59
would say a genetic domain that you use all kind of capabilities
30:03
that comes from AI on like not a good depiction of what's true.
30:10
And I think our, our mission is not just to build and
30:15
standardize utility data, it's to do it in a way that it's
30:18
really drive the journey of trust.
30:21
We have three, I would say in, in parallel, It's the like path.
30:29
The first one is the the trust that refers to the precision,
30:34
the quality of your data. The second one is the coverage
30:37
that you have because you want to do it proactive.
30:39
And the third one refers to, you know, the trust that you get.
30:46
And as long as the trust comes, I think you could go from the
30:51
early planning to the construction and maintenance.
30:54
And we started as a building tool, but now we are precon tool
30:59
for we have utility insights, we have route planning, we have
31:03
transportation planning and we have conflict metrics.
31:06
We're building very fast, many products upon that, but we
31:09
started early on. You remember I told you that I
31:13
didn't want to test the data, but it's like building a plane
31:17
while, you know, flying a plane while it's like building it
31:21
during the the build of the plan.
31:23
So I didn't want to do it. So I went to a place that nobody
31:27
provides anything. People told me, but there is no
31:30
item, there's no line that you could use budget for.
31:35
I said, yes, there is. I'm going to pull it from, you
31:38
know, the 60% and they're going to they're going to understand
31:41
like the possibilities that we could unlock if the data will be
31:43
exist here. And that was a great decision.
31:46
It, it, it correlates with the good enough and then you move on
31:50
and on and on and, and you could move on, not just for
31:53
construction. You could be like insurance
31:57
engine before conducting insurance policy because you
32:01
have the ingredients to create an assessment of a risk
32:05
profiling of a risk. It's about the profiling, the
32:09
probability and the consequences.
32:12
If you have what it takes, you could build that engine instead
32:16
of like people that make decisions based upon scars.
32:19
They can say, oh, I know why it's so.
32:21
It's such a costly because there's 25 years of experience.
32:25
Oh. Yes, it.
32:27
Happened to be once before and now it happens OA.
32:30
Every time I that's a fantastic analogy and a fantastic point of
32:34
the scars being like, I don't want to do that again.
32:37
So let's build up systems and and processes to prevent this
32:41
thing that probably will never happen again.
32:44
In our discussion today, and even in the previous episode, we
32:47
talked a lot about the, the different roles you work with
32:49
contractors, owners permitting. You know, there there's so many
32:52
people across that construction value chain.
32:55
Love that phrase, by the way. What is it like working with
32:59
partners and, and how are, how do you help get people to give
33:03
you the data you need and, and have it be trustworthy?
33:07
So built not only building that trust, but getting that data in,
33:10
in that process. How does how?
33:12
How does that work? Yes, there, there are many
33:14
aspects, but let's let's touch on 2.
33:16
And the first one is like, I'm giving access to data to my
33:20
customers and now they're using it and getting the value that
33:23
they thought that I promised. And once they got to the point
33:27
they're using it, they want to upload their data and they want
33:31
to get even much more comprehensive.
33:33
So they're asking me if I'm, if I were to share with you my data
33:37
set, anybody outside would be able to see it.
33:40
No. We have the ability to
33:42
distribute to each customer the data set that he's using on top
33:47
of our data. So the data that he shares with
33:50
us is still isolated, protected and nobody could see it.
33:56
And and that's this trust. If you could show like a utility
34:00
owner that you know the location and the metadata attribution of
34:04
these facilities better than what he thinks, that's a trust.
34:10
That's a trust building that you have with him.
34:12
So now he's saying, oh, if you already know, so why don't I
34:16
will give you everything I know and you just verify to me what I
34:19
think I know. So that's the play.
34:22
Somebody came to me from the 811 industry, an executive director
34:26
of one of the 811's, the states, and she told me, you know, you
34:31
disrupted the industry and I said, how come I'm not selling
34:35
to any one of your customers yet?
34:37
She said yes, but because what you did, people are more open
34:42
minded for the shareability of their data.
34:45
So now the discussion is not about like are we willing to
34:48
share the data and how fast we are doing, how fast we are
34:53
sharing the data today. So I think you created that you
34:58
know that stream of shareability of data.
35:01
And in that you answered like the one big question I really
35:04
had around your platform was just the the fidelity of the
35:08
information and the privacy aspect of it, which was the
35:12
like, if I want to Add all my infrastructure information, I
35:15
don't necessarily want people to know what sore they can swim up
35:18
to, to break into my building, right?
35:20
So yeah, I watch too many movies.
35:23
But I think like that there is that inherent fear of, well, if
35:27
I'm going to share my data with you, how is it going to be used
35:30
in that ability to create create like a permission layer system
35:33
where now here is the the four M infrastructure layer of what we
35:36
know and what we know is publicly available.
35:39
And then here is your layer of your information.
35:41
And then what's really cool, I think part of the rosy vision I
35:44
see with construction data is that today, stay in need, you
35:48
buy a building, they hand you a bunch of drawings and their
35:52
project manuals and all that stuff.
35:54
And you got to hope it's accurate and, and good luck
35:56
versus, you know, really working towards this concept of like a,
36:00
a true digital and twin of the environment as a whole and being
36:05
able to say, you know what, I'm going to sell you my building or
36:09
my infrastructure or whatever I have.
36:11
And here is the metadata and all the layers that can go and be
36:14
passed on to somebody else while keeping it still all very
36:18
private. But it's now accurate, reliable
36:20
and trustworthy. And I think, you know, to me,
36:22
that's the difference maker and that's the trust maker is being
36:26
able to navigate that the like ethical responsibility and that
36:30
the privacy responsibilities that you have, which is so cool.
36:35
I know we're kind of getting toward the end of this episode.
36:37
Time really flies on these conversations.
36:39
I would love to ask you is like, what is, you know, something
36:42
that you'd like our listeners to be walking away from this
36:45
episode thinking about? And is there anything about your
36:49
platform or the topic that we're talking about that we haven't
36:53
touched today that you really want to make sure we talk about?
36:55
Yeah, I want them to think every time they're walking down the
36:59
street and they see a mental cover, they see like a pedestal,
37:04
they see like a drainage metal of like a storm drainage of just
37:09
a pole, electric pole or street light pole.
37:12
I want them to think about like the network that provides those
37:15
services. And I want them to think about
37:17
any time they see the road marks, the spray and paintings
37:20
that there is a company that tirelessly working to digitize
37:26
that space in order to provide consistency and security to
37:32
these services that we consume in a daily basis.
37:34
Yeah, I, I, I like to sit to think that one day and soon I
37:39
will be able to build some kind of a Pokémon Go to anytime
37:43
somebody, you know, walking down the street that wants to take a
37:45
picture for me and tag and manual cover so.
37:49
I'll download it right now. Yeah, if, if, if you don't do
37:52
it. If you don't do it.
37:53
Now think about if you see something that needs to be
37:56
notified to the Public safety Commissioner, any, anyone who
38:01
responsible for those participate, do it.
38:04
Anytime you smell something that there might be a leak of a gas,
38:09
you know, report it. There is like responsibilities
38:13
around those and any, anything that you could do to prevent
38:16
those devastating events I'd mentioned at the beginning,
38:20
please participate. I would, you know, I promise to
38:24
do my best effort to come up with something that everybody
38:28
uses and effects every one of us, not just for the sake of
38:33
building successful business and, you know, making money, but
38:37
doing something really good that really matters to people and,
38:41
and, and unfortunate and, you know, for the opportunity to do
38:46
so. And with the good things happen
38:48
to people who do put good out there.
38:50
So I really appreciate you sharing our your story with us
38:53
and and your vision. I, I think I'm gonna be walking
38:56
around outside looking at spots and dots and everything in
38:58
between now. So thank you for that curse or
39:01
blessing you will. I thought I liked that enough
39:04
stuff when I was inside building.
39:05
So, all right, but I'm really excited.
39:08
I love the conversation we had today and just want to thank you
39:11
for coming onto the show. It's been such a pleasure.
39:13
It was a pleasure meeting you and speaking with you today and
39:16
sharing my story. Thanks for inviting me.
39:18
Thanks for great discussion and see you soon.
39:24
See you soon. All right, so another episode in
39:26
the books. This was another fun one.
39:29
It's just left me with so much to noodle over.
39:32
I really kind of liked his like Niantic Pokémon Go reference
39:36
there. Being the game runner that I am,
39:38
when it first launched, I was working at Gensler in New
39:41
Jersey. Crazy.
39:42
I don't know if I'd ever told you this story and had a big
39:44
green right in front of the our office that we worked in.
39:48
And during lunch breaks you could watch people.
39:50
They had like 5 poke stops in this park and you'd watch people
39:53
walk around in a circle, me being one of them.
39:55
And it just was so funny because then you like flash forward in
39:58
the future and you learn about what Niantic was up to and using
40:01
not just Pokémon Go, but the other games that they created.
40:03
It was a way for people to actually walk and look around
40:06
and start to do Geo mapping and Geo fencing and all these other
40:09
really cool things. And so there is just, there's so
40:12
much power. We got to get someone who's like
40:14
a gamification expert on the show.
40:16
If you are listening, I'm soliciting this.
40:19
I will actually invite you on the show because I think
40:22
gamification is such an important way to make the work
40:26
we do fun and work should be fun.
40:28
So lots to think about. What's your takeaways here?
40:32
So. Great meeting of the sick.
40:34
I I love the vision, the passion.
40:36
I, I always say maps are cool and, and what we're doing with,
40:40
with data as it relates to the geospatial leisure of it is
40:44
awesome. I'll go back in, in the archive
40:47
as well. And my first real software job,
40:50
I was a consultant. I worked as a consultant project
40:52
manager for a company that started in asset management, so
40:56
water, sewer, storm, etcetera. And so I, I became very, very
41:01
much did the same thing. I'm like, oh, that's a manhole
41:03
cover. That's an asset.
41:04
You do work against this asset. And so now anything I see out on
41:07
the street, on the road, out in public, my brain flips to asset
41:11
management mode or permitting mode or utility billing mode.
41:16
And it it just really translates it into what that means beyond,
41:21
hey, there's something in the road.
41:23
It's really cool to understand the upstream and downstream
41:25
effects of of all of those pieces out and our great big
41:28
world. It really it's it's stuff you
41:30
just take for granted. And every time I learn more like
41:32
about water, wastewater, steam, stormwater, and it's just this
41:35
hike. It blows my mind how
41:37
interconnected everything actually is and how much we
41:41
actually take for granted for how easy it is to walk over to
41:44
your sink, flip open the tap and get some nice clean water.
41:48
But I think, you know, really to put the bow on it, you know, I
41:51
think it's like really hammered home decisions.
41:53
Start with trusted data, even the data that we can't see.
41:57
And the importance of relying on just how information is always
42:02
helpful. You just being able to get more
42:05
is part of being able to manage and mitigate the risk
42:07
instruction. So if you have a lot of fun
42:09
today, I know we did, make sure you go visit the forum analytics
42:13
website, learn all about their platform and the value that
42:16
they're creating. Share the episode with someone
42:18
in this space. You might get some benefit or is
42:21
really just a infrastructure. Sure nerd like we are.
42:24
And please don't forget to subscribe to the show so that
42:27
you can get the latest from us. O until then, we'll see you next
42:31
time and stay wired to build. Have a great one.