- Umesh Bhutoria, Founder and CEO, Xempla
This episode dives into the critical, but often overlooked, topic of Data Quality in Asset Operations & Maintenance.
Hosts Siddharth and Umesh explore why data quality hasn't been a priority in the traditional "business as usual" approach. However, with the shift towards Data-Driven O&M, reliable and accurate data becomes essential. In a nutshell, this episode explores:
Tune in to learn more about the impact of data quality and how it can empower your team to make better decisions to optimize O&M practices.
Siddharth Sharma: Hello and welcome to yet another episode of the forever forward podcast. Today we are tackling a topic that might not be the talk of the town but can be a powerful element in your transition to more efficient and reliable maintenance
In this episode Umesh and I will be exploring the state of data quality in maintenance, uncover the challenges teams face with data quality, and most importantly some practical solutions to clean things up. So Umesh let's get down to business and start today's episode. I have three questions for you and this will be like a rapid fire round instead. You'll have a little bit more time than just a few seconds. So are you ready? Umesh Bhutoria: Yes, let's give it a shot. This is an interesting topic and I can't wait to discuss further on this. Siddharth Sharma: Here's my first question. How important is data quality and what is its current state in the operations and maintenance landscape? You could probably start by talking about by addressing why we're even talking about it. Umesh Bhutoria: Makes sense. Thanks for the first question are associated. You reactivate. I think it's important to understand why are we even talking about it right now. We covered in the earlier the episode and the number of other content pieces that we've taken out. If you look at conventionally the way operations and maintenance has been run in built environment, it's been planned or reactive there generally teams don't tend to look for data evidences, they don't make decisions based on data and health. The quality doesn't even matter for you the quality of something matters when you're looking for it, but when you're not looking for it, why would you even bother about the data quality and that's what I would say is the current state but now that people are wanting to move to detail it operations and maintenance right people are realizing that they look at the end what they're looking at data. They're reliving that okay, we don't have the right quality of data. I mean, it was known consciously or subconsciously but it's just become a bigger problem right now that has come to the Forte and people are saying okay. This is something that we would need to address. Siddharth Sharma: Right. But what kind of data are we talking about here? And what are the factors that can cause its quality to suffer? Umesh Bhutoria: So I think there are two distinct kinds of data sets. That might be relevant very look at classified just very broadly one is what goes into cmms when you are doing your plants or inspections or any form of interaction that a technician and engineer has with the asset and the kind of data that they capture it would be It could be visuals. it could be their observations on and so forth which is what usually goes into the CMS. The other kind of a data is what is quantitative more time series, which is either from the iot sensor or building management systems. So on and so forth, but the one thing question over here is the one that is in CMS. That's a lot of data available. Imagine a company that's been in it doing hard services for decades the amount each other they would have been on the systems is incredibly High because they've been running those Umesh Bhutoria: Planned and so on and so forth. But again as I said that quality is not what's there and why is it important? So there are two things one when you look at a lot of facilities, which do not have the data infrastructure there. Let's say there's a facility which was l A lot of data points on DNS has no iot Data point so on and so forth, but you could utilize if you're running a maintenance for 12 times a year. You still can have high quality data points collected at source while you're doing the plan maintenance, but that's not been happening and imagine if you've been doing five years, you would have still high quality 60 data points, which is not the ideal scenario, but then if you're on a scale of 1 to 10, you would still end up being at five six versus being a 10 right? So that's it. Why hasn't become a data quality issue is in conventionally again going back the FM company or only had to demonstrate that they've done the task, right? The maintenance has been done inspection has been done. But how effectively has it been done? Has it caused a positive difference to the performance of the asset? That was no way to be proven per se right. it could only up in the air like you could do it in a multiple manner, but largely that's what I feel is that because that focus was that I have 100 tasks and I need to demonstrate the hundred tasks have been done and that's it. And what's the easy way? You have a checklist you simply Mark then and check check and that's it. while that's all the problem again as I say all of these are problems today only because what everyone is wanting from asset owners to FM companies means that everything has to be data-led. And that is why we are talking about this problem. so you can't really do much when you have a take box where someone is just take that they've done something but you don't even know Why was it done? I always take this classic example go back to anything you need on the filter. That's one of the classic it says clean or change the filter as needed. Right? I mean according to be there actually listing action items there one is you got to make a decision whether you want to clean or change and initially to clean or change. You would probably have a measurement of the differential pressure and then you would say, okay because the differential pressure is this I mean, I'm just fighting that cleaning is going to be surprising. I don't need to sort of change but what you would find on the same event done. what have you done? we don't know and in case if you have changed have you taken the measurement of what the meaning of the time was before and after so you can really figure out the impact so on and so forth. Yeah, but this one's a not a rapid fire kind of a response to your question. But yeah a long one. Siddharth Sharma: No, that's quite alright, so how can organizations really up their game and ensure high quality data within their maintenance tools and systems. Umesh Bhutoria: This one's history case of I mean for incumbents the ones dear and FM company that I think. What they have is a technical debt, and that will remain with them now to come out of the debt is going to require huge investment. I don't know whether the P&L holders are even in a position to look at that. So the best way is to not add to the technical debt and say okay hence what we are not going to contribute to the technical debt that's there. Is there the data quality data, whatever is there is there we'll take care of it when we think we need to so the best recommendation I could do is handle it at source, which also would require change management honestly, so that's why but do it at source, which means you now hence for whenever you are inspections or your checklists having failed you will have more data to be taken or not more but the relevant data points must be taking the relevant context must be given and there's a very fine part that one needs to travel you need to make sure that the technicians are having to take a lot of data points. And hence it needs to be slightly more intelligent and recommended in nature where you're giving a recommendation and say It in this question. This is what I think needs to be taken and it's Dynamic. So the best place to be able to handle that at source. Does the CMMS they do the same as they allow them to do that. If and no changing as the MMS general is not moving from one Telecom operators. The other you are kind of at least wedded to that system for some time. So you will have to look Beyond and say Is there an application or is a process I could look outside of the CMMS but that complements so that's my recommendation. If you're an emerging FM company, I would straight away say that drop the CMMS that does not allow you to take data quality at source because you don't want to be using a CMMS or at least the core of your operations and data quality inherently isn't taking care of because emerging if some company can't afford to build that for themselves, which they probably would not be able to get out of in course, even if I'm companies still have a lot of capital at disposal so they can relatively easily get out of it. It will still have to invest. The other way to do it and it has to be at source, but the other way to look at it and connected it's a less popular belief by the way is every facility management company invariably slightly larger in operations has a concept called Command Center where there are a bunch of people who basically schedule an allocate tasks, coordinate on a lot of things that have to happen for early from The Last-Mile connectivity point of it think of it is from Logistics point of view. Yeah, a lot of shipments to be done your coordinating who's on the ship where so on and so forth. One way is to be able to look at saying can we automate a lot of those processes and free up the time from the people who've been doing this process and then give them the responsibility of saying okay up your game. We want you to be in the custodians of data quality, It's a core job that they should have been doing but normally in The Madness of everything that happens under a business is usual scenario. It goes for a cost. So I think those are two recommendations. I would have and later Source make sure that you free at the time of some control Command Center callings ask them to up the game and also basically ensure that they are react more like a data quality custodians then they do at this point in time. Siddharth Sharma: I have a lot to learn from this episode and I played back and listen to it again and I am sure the listeners will have to so that's it for me and my questions but just to wrap up this episode with a summary with the key takeaway for our listeners. Umesh Bhutoria: I think it would say the quick wrap up is going to be that the data quality in your CMMS is now extremely important. If you're not being start to pay attention, if you're an emerging FM company start to look at a CMMS that allows you to build that at source address that it source as a part of the offering and if you are an incumbent from company, you cannot allow your data quality to grow so figure out processes and steps to ensure that it is taken care of before it becomes an even issue, but it is an issue that needs to be addressed. Many realize it subconsciously, very few realize it consciously. And if there is someone in your team who realizes this consciously, please give that person the space to basically address this within the entire organization. So that would be my quick take away from the episode. Siddharth Sharma: Yeah, that was quick according to our rapid fire round. Umesh Bhutoria: Thanks for your time. Appreciate it. Looking forward to the next one. Siddharth Sharma: Same here. Bye. Umesh Bhutoria: Bye.