Aug 5, 2026 · 27 min · 9 segments
On Industry Trends & Insights, **Bob Francis** speaks with **Ben Dutro** of California Water Service and **Tacoma Zach** of Mentor APM about solving one of asset management's biggest challenges: poor…
Tacoma ZachGuest
Ben DutroGuest
Bob FrancisHost
So Ben, let's, let's define for the audience the scope of this case that we're discussing today.
Um, how did, how did the problem statement get put together? How did you develop the scope for the project? I mean, w- really, what was the problem? The big-- I'm sure there were many, but for-- in terms of this project that we're discussing today, define that for our audience.
In my mind, the biggest challenge was the scaling issues that we were facing, because when you've got a state's worth of assets and, um, something on the order of three thousand control valves and another thousand motor control centers and hundreds of generators, just the data entry and collection process is daunting.
You-- we've got limited staff that all have, um, other things to focus on, and so taking, taking expensive field techs and making them do data entry was not something we were real keen on.
But at the same time, we recognized that we really needed to put higher quality data into our Maximo system, and then d- if we ev- we're even gonna have a hope of leveraging, um, risk analysis, the classic five-by-five matrices on, on consequence and probability and all that other fun stuff that, you know, is our bread and butter
So how did we tee up the solution? Hearing Ben's problem and l- studying the scope of what they were trying to solve for this project, talk to us about how you and your team put together the solution, and a little bit about Mentor Lens, which I think is really focused in this area, especially on that data question that every industrial and municipality is wrestling with.
Um, you know, I mentioned AI before, and Lens is really an AI-enabled asset data collection and enrichment platform.
And what we found was is that w- and, and Ben's organization is, is, is no different than most of the rest, and that is, is there's an incomplete data, uh, record in, in everybody's CMMS.
It's about plate data, and it's about condition information and, you know, b- much more of that.
And so if you sort of summarize it, most people's asset records are incomplete or insufficient, and then, you know, sort of one word, you know, they, they c- they're broken.
So what Lens allows us to do is, is really help organization capture that information of those physical assets out in the field just by taking some photos.
So you really go ahead and just take, take some photos o- of the asset, take photos of the plate, take photos of the, of the tags, take photos of the actual assets itself, a full three sixty, and you just take those photos, and it automatically captures that tag information, the plate information, the condition information, and it does a very, very rapid condition assessment on those assets that, um, allow us, or that are standardized against, um, call it an RCM grade, uh, set of condition elements that are all tied back to, to, to the failure modes.
What this does, though, is that this unifies all the data then about every particular asset, puts it into a d- structured database that is then governed as well, and we export that data to someone like, you know, like Maximo, that then has it ready for whatever other analysis that they, you know, wanna, wanna do with, uh, with that particular data.
Uh, what, what Lens does is it'll pull all that information together, and it'll, um, provide that then in a completed, enriched data set for Maximo users to be able to use, use elsewhere.
So Ben, let's, let's define for the audience the scope of this case that we're discussing today.
Um, how did, how did the problem statement get put together? How did you develop the scope for the project? I mean, w- really, what was the problem? The big-- I'm sure there were many, but for-- in terms of this project that we're discussing today, define that for our audience.
In my mind, the biggest challenge was the scaling issues that we were facing, because when you've got a state's worth of assets and, um, something on the order of three thousand control valves and another thousand motor control centers and hundreds of generators, just the data entry and collection process is daunting.
You-- we've got limited staff that all have, um, other things to focus on, and so taking, taking expensive field techs and making them do data entry was not something we were real keen on.
But at the same time, we recognized that we really needed to put higher quality data into our Maximo system, and then d- if we ev- we're even gonna have a hope of leveraging, um, risk analysis, the classic five-by-five matrices on, on consequence and probability and all that other fun stuff that, you know, is our bread and butter
So how did we tee up the solution? Hearing Ben's problem and l- studying the scope of what they were trying to solve for this project, talk to us about how you and your team put together the solution, and a little bit about Mentor Lens, which I think is really focused in this area, especially on that data question that every industrial and municipality is wrestling with.
Um, you know, I mentioned AI before, and Lens is really an AI-enabled asset data collection and enrichment platform.
And what we found was is that w- and, and Ben's organization is, is, is no different than most of the rest, and that is, is there's an incomplete data, uh, record in, in everybody's CMMS.
It's about plate data, and it's about condition information and, you know, b- much more of that.
And so if you sort of summarize it, most people's asset records are incomplete or insufficient, and then, you know, sort of one word, you know, they, they c- they're broken.
So what Lens allows us to do is, is really help organization capture that information of those physical assets out in the field just by taking some photos.
So you really go ahead and just take, take some photos o- of the asset, take photos of the plate, take photos of the, of the tags, take photos of the actual assets itself, a full three sixty, and you just take those photos, and it automatically captures that tag information, the plate information, the condition information, and it does a very, very rapid condition assessment on those assets that, um, allow us, or that are standardized against, um, call it an RCM grade, uh, set of condition elements that are all tied back to, to, to the failure modes.
What this does, though, is that this unifies all the data then about every particular asset, puts it into a d- structured database that is then governed as well, and we export that data to someone like, you know, like Maximo, that then has it ready for whatever other analysis that they, you know, wanna, wanna do with, uh, with that particular data.
Uh, what, what Lens does is it'll pull all that information together, and it'll, um, provide that then in a completed, enriched data set for Maximo users to be able to use, use elsewhere.
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