From Data to Action: Leveraging Analytics in Asset Management

Good afternoon, everyone. Welcome to our webinar today. We're gonna, just, we've got lots of people still, kind of turning up and coming in, and we can see people arriving. So we're just gonna give it one or two more minutes, and then we'll start proper. Good afternoon, everyone, and welcome to our webinar. Before we, go any further, I would like to acknowledge the traditional custodians of the land on which we meet today and pay our respects to elders past, present, and emerging, we would also like to acknowledge and celebrate our first nations people around the world and encourage us all to continually learn from their history and teachings in caring for country. So, welcome. What we are going to do, today is we're going to explore how you can use your asset management systems to leverage data analytics and to optimize our infrastructure asset management. So, we're going to talk about how we might go about transforming raw data into actionable insights for better decision making and improved asset performance and reduced maintenance costs as well. So I'm gonna talk to you about, the data analytics, process and why it's important, through our real time data analytics and how we can leverage, that more kind of instant access to information. We're gonna talk about detecting early warning signs of deterioration. We're going to talk about automating some of the maintenance and scheduling tasks that we might have, through, our data and, to how we can make that slicker and quicker. And we're going to talk about integrating IoT sensors as well, which a lot of our organizations are going to do, and are looking to do and some of them are already doing. And then we're gonna talk about GIS and spatial analytics, and then I'm gonna hand over to Tess, for, some talk about benefits and cost savings. So who is Tess, you might ask. What I'm gonna do just ever so quickly is introduce us. I'm Robin. Hopefully, most of you have met me at some point or another. I am the, client services, principal consultant at the moment. And with me today, I've got Tessa, and, she's, one of our lead, consultants in client services as well. She's an excellent SME. And, hopefully, together, we're gonna present this subject to you, and we'll aim to also answer your questions as thoroughly as possible. Now I've introduced us, I'm just gonna do a tiny bit of housekeeping. We love all of the people that attend our webinars to answer what, to ask us as many questions as they'd like. There is, a q and a function on your screen so you can drop your questions into the q and a, section of the screen. We've allocated plenty of time at the end to to talk about questions and to answer them as much as we can. There is also, icons so that you can get to know Tess and I a bit more thoroughly. You can click the icons on your screen and read our bios, and decide how cringey they are that, we've written those out for you. And if you are keen to learn more, at any point during today, there's also an icon on your screen to contact us. You can request a demo. I will also just point out, David Lee was feeling a bit under the weather today, so he will not be presenting. So if you were looking forward to, seeing David Lee present today, we apologize. He will be here, another day and time. We'll we'll find a good subject for him to present on, and he can come and, come come and talk to everybody. So let's talk about the data analytics process then and why it's important. So when we say the words data analytics, what we're really talking about is a structured approach to, examining our data so that we can extract useful insights. We can identify trends, support decision making, optimize performance, and it's all about, for infrastructure asset managers, it's all about helping to manage our assets more efficiently over their life cycle. So we can approach this. It's kind of two stages. So we have this before data analysis stage and then after. So before we actually go into the data analysis, really, what we're what we should be looking to do is define our problem or our objective. We need to think about what we need to to solve. You know, for example, which roads need maintenance next year? Does that, align with our business goals, like reducing costs, improving service levels, or complying with regulations? Defining the problem is really the most important part of data analytics because otherwise, we can end up, you know, down those rabbit holes looking for data that we don't actually even need. Excuse me. And then we move on to data collection. You know, gathering our relevant data from various sources, asset asset condition reports, maintenance logs, GIS data, information from IoT sensors, finance data, all of that sort of thing that we can bring together when we want to, analyze our data and then, you know, gain some useful insights out of it. Then we've got our cleaning and preparation, that we need to do in order to, analyze our data. We need to make sure it's properly prepared first. Things that we would always aim to achieve with any set of data, really, where we remove errors, duplicates, inconsistencies, you know, standardizing all of those formats, filling in some of those missing values, and this step really ensures that our data is usable and trustworthy. Then we actually move on to the data analysis itself. Now you don't always need special tools for this, but you can utilize tools, like predictor, like dashboards, like reports, all of those sorts of things. You can achieve an awful lot with a spreadsheet if you're using one of our systems like confirm or, aesthetic or predictor, then you've got some really useful, really powerful tools there that you can use. And it's all about really, you know, using those statistical methods and models, doing some forecasting of your asset failure or you're estimating your optimal renewal times. All of those sorts of things can be done with, you know, data analysis. Once that's kind of done, then it's all about drawing out those, insights, interpreting the the the data and the insight generation where we're turning technical results into meaningful, insights and actions. You know? If we look at the data, do we suddenly realize all the bridges in zone three are likely to reach critical condition within twelve months? All of those sorts of things that we can draw out of that data. Now one of the really important things we need to look at when we're talking about any analytics that we're doing with our data is communication and reporting. You know? What's the most useful reports we can make? Who are we reporting to? Who is our audience? Tailor it to that audience. You know? Are they decision makers? Are they engineers so that we can have more technical data? Do we need to stick to the statistics because we're aiming at finance teams? Is it the public? Is it appropriate to use, you know, dashboards, maps, charts, paper reports, online things, all of those sorts of decisions. We need to figure out what the best way is to put out, results across. And then, of course, there's no point in doing any of those things if we're not gonna take decisions and then action plans from it. We can use those insights to inform our capital planning, prioritizing maintenance, optimizing budgets, and that really is the point where it stops being data and starts being value. And then the thing that I always like to put on the end of anything that we do or that we suggest, it's all about feeding that new data back into the process for continuous improvement, That monitoring and the feedback, you know, track the outcomes of the decisions, continuously improve the process. So I've popped a couple of poll questions in today. Tessa and I kind of had a chat, and we've popped a couple of poll questions in here. So what we would like to know is do you already conduct data analysis with, your asset management data? So I'll just give everyone a minute to click on one of those options. Alright. Last ten seconds, and then I'll have a look at the results, and we can all see what's going on. Okay. So this is looking really positive, actually. There's quite a high amount of you that are already doing, some data analysis with your, asset management data. So, there is, though, quite a high percentage also that are only doing this when it's needed on a specific asset class, and then a small number of you don't do it at all. So let's have a chat then about why it's important. So why should we be doing, you know, data analytics? Why is this something that we should be thinking about on a regular basis and perhaps not just when it's needed and maybe across a lot of our asset classes and not just one or two? So, really, it comes down to some some basic things. Okay? So better decision making. You know, data analytics enables evidence based decisions instead of guesswork. Now a lot of our experienced asset managers, you know, they can operate very well on a on a gut feeling, but what we wanna do is we wanna take out that guesswork, and we wanna be able to use our evidence based decision making instead. It can help prioritize the right assets at the right time with the right treatment very simply. From a cost efficiency point of view, you know, we can avoid over maintaining our good assets and under maintaining our bad ones if we've got a little bit of data analytics going on. It can support our long term financial planning and life cycle cost optimization, which is all in the back of everyone's heads, right, is I've got to figure out how to how to pay for these assets on the long term and improve services and performance as well. Those all important service levels and the performance of those assets is something we're all thinking about all the time. Ensuring our roads and our bridges and water systems stay functional and safe, identifying those performance gaps early. You know? Have we got a declining pavement quality? Is there something going on? And the really, really key things like, you know, providing data driven proof of compliance with our asset management frameworks like ISO or government mandates, that's gold. Right? We love being able to to not only say that we are in compliance with our asset management frameworks, but if we've got data driven proof of it, then we're good to go. And the sustainability, and risk reduction as well and sustainability not just of our climate and our environment, but the sustainability of our service, that's really important because it's not just about providing that service. It's about making sure that service is sustainable long term. We're modeling our long term scenarios. We're thinking about the sustainability of our environment as well, supporting climate resilience with our assets, the risk mitigation that we need to, deal with with our assets, and identifying the vulnerabilities to failure or environmental impact as well. And, again, my always my always there always point that I'm always gonna try and make that learning loop where every decision leads to better data and better future outcomes, that continuous improvement that we want to, that we want to tap into. So that's kind of why, we wanna think about data analytics, but what we wanna also be able to tap into is not just data analytics, but thinking about real time data analytics. So the process of collecting and analyzing and interpreting data, but as it is generated, so allowing for immediate insights and decision making, based on the most up to date information. So rather than waiting for data to be gathered in batches or or, you know, later on, essentially, can we analyze it in the moment as it happens? Now the key principles of it, you know, are that it is, in the moment. Okay? And those immediates and insights and decision making are all very well, but we have to figure out how we're gonna make them meaningful. And we need to think about, you know, if it's right for that particular action. So real time data analytics, when we talk about, in terms of our systems and we talk about it in terms of asset management, a lot of the time what we're talking about is automation. That's what we like to do. We like to talk about automation. We like to talk about relieving those manual actions with some data analytics, and it can be a very, very simple thing to set up. It does rely on, data streaming, technology. So, you know, have we got actions and functions that continuously feed data into the analysis system as it becomes available? Is it processed rapidly enough with minimal latency to provide those kind of near instantaneous insights? And the the time sensitive applications, you know, that's where it's particularly valuable in situations where quick responses, you know, are crucial to those changing conditions like weather events, critical asset outages, those kind of urgent, situations. So that's kind of when we think about real time data analytics. So I've got there we go. Yeah. Here we go. Sorry. My slide deck, probably updated a little bit after yours did, everyone. So sorry about that. A little bit little bit of a pause while I waited for my screen to update. So I've got a couple of slides here about detecting the early warning signs of asset deterioration. And I'm going to kind of tap back into some of this information and some of that real time data analytics again later on. It's kind of gonna all come together. So when we're thinking about detecting the early warning signs of asset deterioration, especially if we're doing anything real time, but this could be equally valid as a kind of a batch process that we might do after we've collected information as well. We can utilize lots and lots of different data sources, and bring them all into that one place ready for analysis. So we can think about condition assessments like visual inspections. We can think about sensor data, which can give us information about vibration and moisture and temperatures. We can think about the maintenance history of our assets. We can think about the past failures of our assets. You know, is certain assets failing failing often? Are they not failing often? We'll bring all of that evidence in together. We've got usage data as well that we can potentially use, like traffic volumes and water flows, and then the environmental data, you know, the weather, the exposure to salt, the exposure to chemicals, all of those things. And what we can do is we can bring them in to create a kind of a baseline to apply our analytics. So we have to think about bringing all of that in and then figuring out what normal looks like. Okay? So our condition ratings over time, our expected deterioration curves based on asset age and material and use, you know, that baseline is your benchmark, and then you can use that to figure out any deviations that could potentially be red flags on your asset. Now as I said before, this particular, screenshot I've got here, this is of our predictor, product, which is, one of our products that we can use for those kind of modeling and and analytics, but you don't necessarily, need a specific tool for some of these things. You can achieve an awful lot, asset management system, and the reporting, that is, already included in that. So if we have, established our baseline, then we're gonna move to that kind of predictive analytics, that kind of modeling, and, thinking about some of the techniques to identify the early sin signs of, deterioration, such as trend analysis. You know, we're monitoring those conditions over time to detect that accelerating decline. Like, for example, this bridge is degrading twenty percent faster than it's supposed to than expected. We can use anomaly detection, where we're thinking about those on you know, one of our sensors has, detected unusual vibration, which could signal a crack in our in our road. We can look at regression models and deterioration curves, thinking about forecasting future conditions based on our historical trends, and then we can think about risk scoring models as well where we combine that kind of condition usage age, environmental, factors to calculate those kind of risk scores and prioritize those high risk assets, you know, before they even fail. And all of that can be turned, you know, use that complex data, can be turned into actionable insight. We can use dashboards. I'm not gonna tread on Tess's toes here very much because she's gonna talk about dashboards a little bit later on. You know, we can use dashboards that show real time condition and deterioration rates. We can use, heat maps or GIS layers and to flag out hot spots. You know, you can have automated alerts from our systems where certain thresholds are crossed, and all of that really, is all about triggering early intervention. You know, we can schedule the present preventative maintenance instead of costly emergency repairs. We can update our renewal schedules to, you know, optimize those budgets. We can really just extend that life of that asset with, kind of targeted, treatments if we do it in a timely manner. So, you know, really, an example might be resealing a road when microcracking begins. Instead of waiting for the inevitable potholes, we could decide we've got an issue here. We're gonna go early. And all of that really, adds up into these benefits of early detection, you know, that time to act before the failure. We're gonna end up with ultimately lower life cycle costs if we put some of that complex data to action and get those insights out of it. We're gonna end up with reduced service disruptions. Our assets are going to be disrupted, and and they're gonna be out of commission for less time. Hopefully, we're gonna improve that safety. And, again, the sustainability not just of our environment, but of our service as well, that long term sustainability of our service is is asset management gold dust. And, really, the transparent decision making if we've got to have our decisions that are justifiable for our stakeholders, the more transparent we can make the, logic of those decisions, the better. And all of that adds up to the time and space to negotiate your budget, in order to be able to act. So I'm gonna move on to kind of the automation of our maintenance and scheduling, because this is some of that real time stuff that can can make a difference, not just to our assets overall, but to our everyday, work life. So our automation of maintenance and scheduling, I will talk about this a little bit more, in one of our other sections today, but I wanted to to, talk about it a little bit on its own right as well. So with regard to, you know, our reactive maintenance, when we're thinking about our, automation, we can have things like our defect reports and our inquiries. We can have those automatically analyzed and then triaged, and sent straight to the task owner for completion. You can decrease that admin overhead quite substantially with our reactive maintenance by using some of this automation, and it really does increase our operational efficiency. It gives us easier communication. And if you're utilizing automation when tasks are sent out, you can also utilize automation when they're updated and come back in, by using, you know, those dashboards and reports to monitor our tasks in in real time. You know, the task owners can utilize mobile device technology, and then that completion data can be picked up for near or real time visibility of tasks and results monitoring. And then our proactive maintenance, you know, you can use our automate oh, losing all of my words today. You can utilize our automation by having that kind of set and forget technology with our routine maintenance plans. Work can be automatically created and sent straight to our contractors based on, you know, that seasonal schedule that we've set up. Again, we can utilize dashboards and reporting to help adjust that schedule when we need to. I'm gonna talk a little bit more about maintenance scheduling when I talk about GIS and spatial analytics in a moment. So that's not all I have to say about this subject, but it's all I'm gonna say just for now. Because one of the things if we're talking about automation of reactive maintenance that I'd like to talk about is our, IoT sensors that we can potentially integrate, into the system. Now incorporating IoT sensors into our, asset world can be a real game changer when it comes to response times. It allows us that kind of instant analysis of data that we can react to it accordingly. The twenty years ago when I started working in asset management, an asset that tells you when it's broken was a pipe dream. Right? The whole game revolved around inspectors having regular physical interactions with our assets. Now this is a system, that I'm showing you here called Sensei, and the sensors, in this particular screenshot, the sensors have reported an issue with the system, and the asset requires maintenance or a defect. Now if I have my system configured correctly, I don't even need to see this part because I can have an API set up that sends this straight into my asset management system. And if we look at this example here, this is our one of our brightly asset management systems, and this, report of an issue came straight from Sensai. This was a live demo that we did a wee while ago, and we have sensors on an asset. It appears to have some corrosion that's outside the set limit. The sensor has raised an issue, and that issue has gone straight into, my asset management system and integration. So that data can now be analyzed by the system. And depending on what the problem is, it can either be added to my maintenance round for next week, for example, or in this particular case, it was processed straight the way through and sent to a mobile device of one of my maintenance officers to go and fix the problem. So I haven't had to have any, physical touch points with this particular issue. It's come straight into the system for me. It's been analyzed. It's been sent straight out. The whole thing was automated due to the data analytics capability that I've got in my system. Now Tessa's gonna talk about this a little bit more later, but, again, I don't wanna step on her toes. But whilst this issue is being sent where it needs to go, my real time dashboards and reports are automatically picking it up and monitoring it. So I don't have physical touch points with the issue, but I do have full visibility of this issue for both the people who are working on it and the people who need to manage it. So it's not just visibility, but this is real near near real time visibility. And it's all about, you know, that kind of single source of truth now that I've also created, because they were reported into the same system as any physical inspections we're doing. I can view all of the data in one place. And because I'm viewing all of the data in one place, I can utilize it for lots and lots of different purposes. And, again, once the issue is, solved, the completion notification isn't just in my system. It's sent back to the sensor system, and it checks the sensor is now reporting a normal status and then resumes standard operation. So it's a very, very, powerful, information that I can get and then utilize, from one simple integration with my with my sensor system. So I'm gonna talk about GIS and spatial analytics. Now David was gonna present this part for me. I'm gonna do it justice, but before we get there, I just wanted to ask a question. Do any of you use, IoT sensors on any of your assets? And, again, I will, count to a hundred as it were before we move on. I'll give everybody a chance to to choose an option. We've had a lot of responses to this question already so I'm just going to leave it open for about five or six more seconds. Okay. Let's have a look. So this is such a great response. So we actually have quite a lot of our customers are already using on at least one or two asset types. They're already using sensors, which is really great. And the people that would like to, absolutely brilliant. We're gonna we're gonna talk about this more, in later sessions. And anybody, who wants to have a chat with their account manager about this stuff, they're happy to to chat as well if you want a bit more information. And we can we can talk about the the potential integrations that you can achieve to really get the most out of any sensors that you're using. But, yeah, thank you everyone for answering our questions. We're really grateful. Let's, talk about GIS and spatial analytics. Now we've touched on this, a little bit earlier, but I wanted to give GIS its own section today because I don't think people really use GIS and spatial, analytics to its fullest, potential. So if you're using a best of breed system like confirm or aesthetic, then pretty much every entity you work with gets mapped. It gets an x and y coordinate at the absolute minimum. So customer service requests, defects, jobs, inspections, and then, obviously, the assets themselves. And very often, you know, almost routinely these days, there is a full integration between, your asset management system and your GIS system, And this allows us to tap into some really next level, shiny things. So one of the examples I've put on the screen today, you can see is a very simple hot spot mapping, of defects. And it can go something like that can go a long way to providing insight into those kind of repeating patterns and trouble spots. I know of a couple of councils that have managed to track, specific gritty graffiti artists using just a hot spot map, and they were able to to give that to the authorities with you know, which provided a bit more specific information. And they ended up with quite a few less rude words written on the sides of their assets. You know? And that's just a simple hot spot map. We haven't even tapped into the really good stuff yet because what we can do if we think about our GIS data in a more of a kind of a big data way, we can, overlay things like weather exposure on an asset condition map and use that to really enhance our predictive, deterioration. And, again, we can utilize any entity that has spatial data attached. So not just data within our asset management system or even just data within our GIS system. We can utilize external things like our population maps. We can mix that with our job information, you know, our CRM data, our defect data, our condition data, and then stick a town plan over the top with some weather data. And the list goes on, and using that spatial data like that to overlay different parts of of of our map and overlay different kinds of data on our map, we can go from a small amount of of analysis and insight to a really big amount of insight that we can gain. With regards to the scheduling part of this, you know, scheduling that working day and that working week for our teams, if we utilize our spatial data from within our system, we can make that scheduling really sing. Being able to see where each task is as you schedule it makes efficiency and optimization of your team's working day a very simple task. And if you bring in other tools like a little bit of automatic routing, then the real time visibility of their daily progress, you know, you can really start to move from a reactive state into a proactive state of working. You know, where spatial data analytics really comes into its own, is those moments when you need an urgent response. Being able to see, where your teams are on your map and what they're currently working on allows you to put the nearest team into play to make that urgent problem safe as fast as possible and get the right people to the right place at the right time to then make those repairs and keep everything moving. You know, it it it's all about just being able to see that real time visibility of your team's day and and that reaction time with a little bit of spatial analytics can be minimized. That reaction time could be made so fast. So what I'm going to do is I'm gonna hand over to Tess at this point. You've probably all heard enough of me right now, and she's gonna talk about the benefits and the cost savings, that can be, had with your, data analytics. Thanks, Robin. So in the way of benefits and cost savings, in the way of data analytics, everyone at the moment is feeling a pinch with construction and maintenance of assets becoming more and more expensive, and what used to go a long way doesn't go anywhere near as far anymore. The incorporation and use of data analytics can provide a number of benefits and ultimately have a positive impact on the, financial situation in the form of cost saving. Straight off the, top of the list there, we have better decision making. So the use of data and and analytics allows for evidence based decisions, which is better for the public sector as everyone is everything, sorry, is funded with rate payer or taxpayer money. And, ultimately, it allows for more efficient and effective use of your organization's resources. It also provides for and allows for justifiable decisions, which is always important in local government as ratepayers want to know where their rates are going. For example, when a proposed budget is out for community consultation, a ratepayer might query why a particular asset is being renewed or replaced and others aren't, that they might themselves deem more important, which I'm sure is a scenario that many of our attendees today are familiar with to a certain degree. By using data analytics, you could easily provide the community member with clear justification as to why the asset was selected for renewal and why the assets that they thought might have been more important weren't selected for renewal or whether they've been selected for renewal in a couple of years' time or not at all. Data analytics also allows us to support our long term financial planning and life cycle cost optimization. So the effective use of data analytics can help us inform and generate content for our long term financial plans and asset management plans as well. And this in turn can reduce administrative time, resources required, and the overall cost to produce the documentation that we need to provide to our all of fit members and wider stakeholders as well. A great example of a tool that can provide us with this while utilizing data analytics is NAMS plus, which is an online tool that's provided by and produced by Equia. Some of you may be aware of it already. But NAMS Plus, essentially, you have a series of data inputs, whether it's condition data for your assets, the depreciation values for your assets, and whether inputs for renewal plans, operational and capital budget forecast as well. You push all of that into NAMS plus, select some parameters, and then click go, and it essentially spits out a whole heap of visual aids that that you can like, graphs and the like that you can then put into your AMPs. And there's even an option where you can actually have the system generate an AMP for you with all of the, little, you know, introductory bits and pieces as well, and then there's only a small amount of content that you would need to write that to make it more specific to your organisation. Data analytics also allows us to ensure that assets are being managed, are fit for purpose. So the assets that you manage always need to be safe for use and fit for purpose as they're being used by the local community, and members of the public. So data analytics encourages regular collection of condition data of assets because if you don't actually have the data in the first place, then what are you gonna analyze to make your decisions around maintenance and renewals and disposals or acquiring new assets? Data analytics also allows for us to prioritize our capital works versus maintenance activities on our assets as well. So with the help of data analytics, users can prioritize work and decide what needs renewing based on actual data rather than renewing it based on time or because that's how we've always done it. An example of this would be the rope be a rope reseal based on length of time between renewal rather than inspecting the asset first, analysing that condition data that was gathered during the inspection, and then making an informed decision as to whether you do reseal the road or don't reseal the road if the condition of that road segment is of an adequate level. Users can also make informed decisions regarding what should be earmarked for renewal versus what doesn't need to be renewed, but might just need a little bit of maintenance and a little bit of love. So for example, you might have an unsealed road that has a concentrated area of potholes, and you might decide to just patch and do a small repair in that little section where the potholes are concentrated as opposed to reshooting that whole road, which would then eat into your CapEx budget. One of the tools that we can use once we've gathered all of our data to complete our data analytics as real time dashboards. Now Robin has touched on this already as part of this presentation, but I'll, provide a little bit more explanation as to what we can do and how we can get our dashboards to benefit us. So many asset management, software systems have the have the ability to create dashboards within the system itself and then provide, many advantages to the user when used effective when used effectively. First of which is to create, quick and clear communication of information at any given time. So these dashboards can provide a direct live feed of your data with the potential for that data to be directly exported from the dashboard itself. For example, a user may want to know how many maintenance activities, outstanding this week for a particular staff member. And with the correct dashboard configuration, they would be able to view the list and send it directly to the staff member responsible or just take a screenshot of the dashboard instead, email it to them, ping it to them on Teams, and then they'll have that information as well. These dashboards also provide us with near real time updates throughout the day. So this enables the user to have a clear visibility of their workload and the workload of the teams that they manage as well. If your dashboards are set up correctly and effectively, the user doesn't need to go into the depths of the system to find the information that they need or want. The information and data relevant to their role and responsibilities within the organization is presented to the user when they log in. And that last point ties in beautifully with the bespoke configuration possibilities that come with real time data dashboards. These bespoke configuration possibilities, in essence, you're able to configure to only see maintenance information and data, or it might be accounting and depreciation progression values that you wanna focus your dashboard on, or it could be specific asset register information and asset attribute data. A great example of this, Brightly software asset management systems, so both within a set of you can confirm, the dashboards are fully configurable to each individual user and further configurable to each module within the system. So if you're the works coordinator for water and sewer assets, you can configure your maintenance module dashboard to only show you work orders pertaining to water and sewer assets. And if the information's available within the system, you can display work, work order costings in your dashboard, resources assigned to works within your team, outstanding work items, and a whole lot more of information and data. And from a renewal planning perspective as well, you can configure dashboards to set up, sorry, to be set up to display assets that have poor condition scores so that you can quickly and easily decide what should be higher on the priority list for renewal planning. Another cool thing with the dashboards that you can, configure and then utilize is if you have an inspection done on a whole class of assets on in one particular week, you can have those results feed directly into a dashboard. And then when you start the following Monday, you can then prepare a works plan to address the defects that were identified the week prior just by looking at a dashboard. So now we have a couple of examples. So, this screenshot here is an example of a confirmed dashboard, so you can see we're looking at electrical testing and monitoring, and there's a couple of different visual aids and graphs that we can see here. So you can see that there have been a number of tests completed over a two year period, different feature changes, the quantity of those, and then outstanding items down the bottom as well that can be, you know, color coded in different ways. And then this is an example of what an aesthetic dashboard can look like. So as you can see, we have these, widgets at the top here, which are showing us total cost for a certain period of time. It's quite small on my screen. I wanna say that it says twenty twenty five. You've also got your planning costs, the total cost of all of your work orders, and then there's repair costs as well. And similarly to the confirm example that we just looked at before, there's a couple of different graphs that we can see as well. So there's quite a lot that you can do if you utilize the dashboards in your asset management, system, and it can be very, very helpful and a very good tool to have. So now I pass on to Robin. Thank you, Tess. So, we're kind of onto the question answer part of today. If you do have a question, we are still looking at those in the q and a section. So if you do have a question, please feel free to paste in there now. There are a few really good questions that have already come in, so I'm gonna, answer a few of those as best as I can. So the first one is, what data do I need to get started before I can start meaningful analysis? So, what I will say there is it doesn't say it take much to get started, but what I will say is, first of all, focus on the question that you're trying to answer. That's the only thing you really need. Once you've defined the problem that you're trying to solve, the rest will flow, and it will be very easy to see what you, do and don't have and what you need to collect and what you already have and and if it's gonna be good enough and if it's gonna be trustworthy enough and what you might need to do to it to make the insights that you need to have. So without that solid definition of of the question that you're trying to answer, you might easily find yourself. I think I already used the term rabbit hole today. That's my favorite term this week. You might find yourself down a rabbit hole trying to find, and complete data that you just don't need. So, yeah, start with why. Why why are we doing this? What are we trying to answer? What is the question? Everything else will flow from there. Okay. John wants to know where do you fit in the manufacturer's warranty and asset life? So both of our asset management systems, both confirm and aesthetic, enable you to to log, a manufacturing warranty date and your asset life, against an asset. And then you can use that to generate things like alerts, I e, automatic. Hey. I'm about to run out of warranty in a month. You best go and inspect me and make sure I'm I'm lasting the distance. And you can then use those warranty dates as part of your, kind of other analytics and your predictive, stuff as well. And the asset life is a very standard thing that we hold against all of our assets. And, again, that can flow through into, your, potentially a predictive tool or if you've got another external, long term planning kind of tool that you wanna use for your predictive analytics, it's very easily to extract data and, have all of those pieces of information flow into that as well. So, yeah, very standard fields that we hold against all of our assets. And if you need a hand finding them, let one of us know, and we'll help you out. Okay. Let's have a look through some of these other questions. Advanced analytics can be difficult to understand for nontechnical users. How does your system differ from others in making it easier to comprehend, and for others in the business to interpret? Okay. So there's a there's a couple of things we can do here. This is all about those kind of nontechnical stakeholders and making sure they can understand the findings. And, really, what it comes down to, I think, with our two systems that we utilize a lot is, it's those configurable reports and dashboards. So what we can do is have the report and the dashboard to configure it to show you the information in a visual way and to kind of take that detailed data out of the the factor, out of the the the equation. Right? So instead of going analytics data, you end up with the impact, not the method. Okay? So, we can take the jargon out of it by configuring those reports and dashboards in a more helpful way for nontechnical people, and it kind of assists you in framing the findings as a kind of a before and after journey. So you can tell the story. You know, before analytics, we replaced street lights on a fixed schedule even if they were working fine. Now we use this dashboard with the condition data, and we only replace what's needed, you know, saving us time and and money. I I love a dashboard because it's just so much better to use a visual and not, you know, something that looks like a spreadsheet. You can have your charts, your infographics, you know, your simple your simple, bar graphs and stuff that speak much louder than a raw table, to highlight those trends and the key numbers. And anything you can do to make it focused on dollars and risks and service, is always good. You know? This is gonna add up to a cost saving of I don't know. And let's be really optimistic. Two hundred k annually. This will prevent a major failure that costs millions. This will have fewer interruptions for our residents. If you can frame the data in that way by using those configurable reports and dashboards to kind of, have the data looking like results rather than data looking like data, that's anything you can do like that is is gonna help you to to get it across to those, those nontechnical users, and both of our systems can definitely, help you out there. Do you want me to answer some of the aesthetic specific questions that have popped up in the chat there, Anne? Yeah. Go for it. If there's one you like the look of, please feel free. Let me just scroll down. So we had one from Blacktown Council. Can we set up specific dashboards now or do we need another module? If you're referring to aesthetic, you will be able to set them up straight away. So within the aesthetic platform, all of your dashboards are fed through the, advanced search through the search module, through advanced search profiles. So that's something that is available to you straight away. It's not an additional mod module that you need to pay extra for or anything like that. If you do find that you, you know, you can't quite remember how to set up search profiles and how to set up the dashboards in turn, feel free to have a chat to, your if if you're dealing with a consultant already, have a chat with them, or you can touch base with your account manager and just see whether if it's a if it's a small task, we might be able to just do it straight off the bat. But if it's gonna be a larger exercise with some data analytics tied in there, to get the best information out of your system for you, then it might be a be a, broader exercise. Alright. There there is a question here there's a question here about the IoT sensors. So I just will we're we're we're, where are we? I was hoping to get an example, further examples of how Sensei works. Will there be a webinar about it? I'm not sure of any future planned webinars, but I can certainly, get someone to give you a call, and we can, show you a bit more about those IoT sensors and, Sensei. So I've got your details there, Kim, and I will put someone in touch with you. But I think we should probably pressure our, integration people to do a webinar. I don't know. We'll see what happens. I'll do my best. Okay. I think that's pretty much all of the time we have for questions at this point. If we didn't manage to get to your question, we will have a look through those, and we'll, do our best to answer a few more, and get back to people after after today. But thank you so much for everybody's questions today. They've been really good questions. What I really wanted to to to do here is just to point out that, you know, we've talked about, a lot of things that you can do, and we may have made it sound, you know, really fancy and complicated, and you need all of these things and blah blah blah. What I really wanna point out is that if you are using one of our systems like Confirm or Ascetic or really any best of breed asset management system, you probably have everything you already need to start doing some of this stuff. So please just give your account manager a call if you'd like to talk to one of us a bit more, and we can help you get started. Thanks everyone for being here today and we will see you all again soon with another webinar.