Analytics for Service Organizations with Tableau and DataSelf

So first of all, we are in times when we hear a lot about artificial intelligence, self driving cars, Internet of things. However, I still hear a lot from companies complaining that it's hard to get access to their data to do data analysis to make data driven decisions. So what we'll talk today is about analytics for service organizations. My name is Johnny Girardi, I'm Data Sales founder and CEO. And analytics is one of the components that quite often is missing to help service organizations to be more data driven. So today's webinar will cover pretty much that perspective. Our agenda will talk about the critical role of analytics, look some numbers in the industry, we'll talk about three critical service KPIs concepts, we'll talk about how to engage your team with analytics and we'll also do a little bit of product presentation in Q and A at the end of the session. If you have questions, Please use the GoToWebinar Panel Chat Panel at any point. Just submit your question at the end of the webinar. We'll address them as many as we can. All right, let's get started. So first of all we did some research to look at in the industry about metrics and trends when it comes to investment in analytics. And Aberdeen Group actually has some pretty good research around numbers for the industry. And at the bottom of these pages you'll see links and we will make this PowerPoint presentation file available later so you can actually check those resources. But one of the very interesting things in the industry we have found out is what organizations service organizations are investing to bring data to service excellence. So this chart shows how much they're investing in different type of technologies and interesting, interestingly is BI analytics is pretty much one of the top technology investments that best in class service organization are investing for bringing data to service excellence. Why are they using BI and analytics? So most of it is to do consistent benchmarking of service performance. I mean if you're going to really take your service to the next level, is really critical to look at your service performance from any different angle and see which of these trends can affect customer service, customer satisfaction, productivity, lower cost, increased revenue. So a lot of analysis has been invested into that bucket, which completely makes sense. Some of it is also invested into real time monitoring. So seeing what's happening on the fly right now. And also about the same percentage is real time visibility into service performance by senior management. So some interesting stats of how other organizations are investing in analytics and how they're using it. This is interesting as well. What kind of analytics teams these organizations are investing in and a smaller number of these organizations, they have a analytics team that works across all departments of the organization. Financials and sales and services, production, whatever it is. But if you look at the best in class service organizations, they actually have a dedicated team to look at services, just to analyze service exclusively. So it's not a generic department, but only a department focusing specifically on that. And I think part of the reason, based on what I read is services, it's a very sophisticated, I mean it's a very complex problem with so many variables and they change it so frequently that if you don't have a dedicated team that understands the analytics side, understand your data sources, understand the services initiatives, you know, the people, the processes, the technologies, you know, it's not as efficient. So having people that are dedicated in analytics for services is something that, you know, best in class organizations are doing apparently very successfully. Now this was also very interesting, I actually never thought about until I read this particular survey. But quite often it is heavily involved in helping service organizations, actually anytime organizations to get more out of their systems overall. But quite often it talks a very tech talk while the business departments talk a very business talk. And sometimes there is miscommunication between things. So setting the right priorities, sometimes it's hard to meet them. You know, it sometimes doesn't really understand the real business problem and find solutions that are easier in their perspective. So this part of the survey was very interesting telling that best in class organizations, they actually compensate IT team based on achievement of objectives related to better managing customer data. So if you know they are putting places ways to compensate IT based on the success of the teams that they support. So not so much about the success of IT per se is the success of the clients of the IT inside the organization. And this can change dramatically how it works because now they're much more interested in making their clients internally more successful than them just meeting their own internal goals. When you look at this perspective, at least on the BI side we see so many organizations investing bi analytics because the cost of the software is lower. They really are comfortable with some kind of platform. But those factors may not be the best for the users is best for the IT department when you compensate them based on the success of the users. Hopefully they can have this look in a more detailed perspective to really find the technologies, let's say the analytics technology that empower the users so they can do more, they can be more self sufficient. So I think this is interesting perspective that you should consider if you're not Already looking into compensating it based on the success of your of their clients, internal clients. Now what I think is most important for any service organization is to look at your own internal KPIs and start asking questions, you know, how are you doing? Is this effective or not? This is just a very simple example that we saw in this research and we asked some of our clients in the service industry is what are the top reasons for failed service visits? And these usually have a big impact in the business. First, because if you have a failed service visit, your costs will increase. But most importantly, this affects dramatically customer satisfaction. So ideally, if you have a service call, you want to be sure that you're going to be addressing hopefully in the first call or very quickly. So what are the main reasons why you have failed service visits? Well, in the industry, based according to Aberdeen part unavailability is one of the top reasons. So as usual, people go to do a service and there is a broken part that the person doesn't have any stock and they have to come back some other time. The second most frequent reason is the customer or the asset that needs to be served is not available for service. Both of these problems, it's very interesting to see how companies can address them. And I read a lot of different ways that you can improve it. Sometimes at first some of these problems look like it's very hard to solve. But when you break down the problem, different pieces in different regions and whatnot, usually with analytics you can understand in much more detail the reasons why whatever failed service business is happening in your business and hopefully fixing some of those problems to improve the efficiency of your services. Overall, analytics should help organizations to address to break down the problems in more little pieces, understand which part of the process is actually maybe causing bigger issues or more productivity overall. Now this page is pretty busy. I'm not going to go through this page, but These are popular KPIs, KPIs used for service organizations, you know, key performance indicators. You know, once you got the PowerPoint, you should take a look at this and see if you are missing some of them. These are the most popular ones. Now what's interesting is that quite often when people look at these metrics or when they're monitoring these metrics, they monitor the metric per se. Let's say busy times, okay, what hours of the day is busy, what days of the week are busy? So you're just looking at the metric per se. What is interesting actually that we've been learning with our clients and also reading around Is actually there are three critical components of pretty much every single of those main KPIs that people use. There are three critical components that affect those KPIs. One is people. I mean, this is a service organization. So usually everything you do has people involved. The second is processes. Are your processes being efficient to deliver whatever service that you're, you're addressing? And finally, do you have the right technology to help these people get the job done? So if you take every single of those KPIs, usually you can find different components where each one of these three components can affect dramatically the outcome of the KPI. And we'll talk a little bit more about that in a few minutes. But it's very important to keep that in mind. You know, it's pretty much about people processing technology. Now, even when you look in these three components, usually you also have to see these three critical variances over, you know, how these three critical variances are affecting those KPIs. Time is a big one. Is it changing over time? Should you be looking on an hourly basis? Should be looking on a daily basis, monthly basis, weekly basis, on a seasonal basis? Many organizations that when we start engaging with them, they have a very narrow perspective at looking at how times affects their KPIs. And usually once they start embracing analytics and they start to look very easily for the same KPIs in different times. You know, times of the day, of the week, of the month, of the quarter, of the year, the season, they start to see trends that repeat themselves over and over. And by seeing that, you can much more effectively predict your demand of labor, of parts, of processes, and much more quickly address those needs. Segmentation is also a big thing. Segmentation is about looking at your KPIs from different angles, maybe by different region or maybe by different kind of equipment that you service. There's so many different ways that you can slice and dice these KPIs. And if you do it properly with the right tool, quite often you can find some segmentation that influence significantly the efficiency of your services. Again, sometimes different regions, they will have very different KPIs, because let's say if you are in the air conditioning business and if you are servicing, let's say, Canada in the winter versus Florida winter, it's very different. And sometimes those things might be obvious, but until you look at the details, you can find some factors that dramatically can change the outcome of the service overall. The other thing is usually when we start engaging with companies, they're always looking at averages of the KPIs, which is very common, but also as you start to put outliers, and again, looking from a time perspective, segmentation standpoint, sometimes the outliers are very insightful because they can show you the very efficient or very inefficient things that are going on. And by learning those, sometimes you can see what's going on and fix the problems or learn from those very efficient cases and try to train the rest of the organization to take advantage of something that someone learned because it did something slightly different than everyone else. So looking averages in outliers is also very critical when you're looking at this whole KPIs. Now let's see some just talk some examples out of those several KPIs of how those components affect busy times. I'm going to talk about example of a client that at the beginning, you know, they were servicing actually air conditioning and heaters. And overall they have a fairly stable number of staff of service technicians throughout the year. But then in the summer they usually would have to hire several additional people because summertime the demand for services was much higher and they just couldn't take care of it. So usually have a hard time leading to summer and hiring people to help taking care of the higher demand. And at the beginning, when they start with the analytics that was just trying to cope with that kind of high demand, they were pretty much following already the AC manufacturers, preventive maintenance procedures. So everything was pretty much doing as they were supposed to. But then with analytics they started to look at much more closely, looking at the past of their services, segmenting different parts of the country, how things were working. And make a long story short, they started to find out that in their client base there were certain parts of the country where they started to do maintenance, additional maintenance, preventive maintenance in certain components. And they experimented different ways in different regions that were supposed to be similar. And eventually they realized that they started to do more maintenance in the low season of certain pieces that usually don't need much preventive maintenance. And actually by doing that, they were able to reduce the demand of services in the high season and make a long story short. So they did this research, they added some additional sensors in their equipment to try to pinpoint which of these machines would require this additional maintenance in the low season. So their overall costs actually grew with these additional processes. However, because of that, they actually lowered the demand in the high season in the summer. And even though the overall cost of this whole operation in a yearly basis was not much better than before, but the Client satisfaction went up significantly because now their clients have less issues throughout the summertime. And because of that they started to get more business because of the customer satisfaction. So it was an interesting case where initially the busy times was just like everyone else doing just busy time analysis. But by experimenting in different regions and understanding better the little details of the whole operation, eventually they increased customer satisfaction, the increased revenues in the cap cost at low. So it's interesting again that, you know, the people, technology and processes in looking, you know, different segmentation over time and outliers can help you really affect the performance of a single KPI. Such a busy times. Another example from another organization is the first time fix rate. So usually you're trying to have a very high KPI first time fix rate. And commonly at the beginning, you know, when people start, you know, addressing this metric, one of the very first things that people try to do is try to find, when you get a call from a client, a new call is try to find in the vicinities which of the technicians are available that have the parts and have the skills to do the job. That's, that's pretty common, you know, have the parts and have the skills based on what the dispatcher picked up during the call. Well, some of our, one of our clients that went through this process and you know, they were trying to, I mean, they went through this process and looking the same availability and parts and whatnot. And they had a large number of, you know, technicians in the field and they were usually using training in trying to do better with the parts to increase first time fix rate. But still at one point they start to just look at segmentation of their technicians, segmentation of the products they were servicing. And eventually they actually came up with a matrix of people and equipment, service and also type of jobs that came up with a matrix of, of that information that actually they were able to see that in a way a chunk of their technicians, they would be efficient regardless of the products, regardless of the problems, they would do the job fairly well. But a subset of the technicians, they were actually much more efficient with certain products in certain type of services and others would be in other segments. Sometimes some people were more efficient with more complex problems, some other people with simple problems. By making this matrix, they came up with a new framework where when a dispatcher would allocate a person for a job, they would try to find the best fit. What is the best person to take care of this job. Sometimes this person is not necessarily close as someone else, but this person would be so Much more efficient to do the job that is worth waiting a little bit more to get this person for that job. So by being able to break down the problem, much more detail to assess how the segmentation of the data can affect the performance of the service can dramatically improve KPIs. In this case, such as the first time fixed rate, if we have the time. I could go over a lot of these different metrics to give you different scenarios where using analytics to slice and dice and break down these KPIs in little details has helped organizations make their services so much more efficient. So it's amazing how putting smart people to look at the data in detail is amazing how much more effective you can make your service organization. All right, let's talk about engaging your team with analytics. The main questions are who should you engage with analytics, when should you do it, where should they have access to analytics and how you're going to do it. Quite often when we start with new analytics projects, people come up with these questions and this our approach that we usually recommend is not a one size fits all, but we say that once you need to start engaging with analytics, it's a journey. So let's start with baby steps. You need to start crawling, then walking, running, eventually going to be flying, but it's a journey. So let's start with the baby steps. And usually what we tell people is when you embrace analytics, it's welcome. You're going to be able to do a lot of things that before was very hard, was very slow. So you have visibility of business. And what you should do at the beginning is focus on people. They'll be really excited to be part of this new initiative. And typically these people are the data driven people, people who like to see data they're asking for and they will be very excited to help you figure it out. Because at the very beginning, you start with something that will probably going to be a little rough on the edges. Maybe it's not really doing the job, but using the right people, the smart people, the excited people, use a small number of people, you'll be able to figure it out and will be exciting, it will be efficient. Now typically when you do this, you have to try to find big impact as actionable metrics. Again at the beginning you may think that you know what you need to look at, but as you start to experiment and look at the metrics and compare and discuss, discuss with people and see how those metrics really you can have actions out of them. Again, it's a journey and sometimes you'll be surprised that some of the metrics that will really make a big impact, you're not even thinking about, you don't even know that they should matter at all. It's a very interesting and exciting process. But once you find big impact, actionable, actionable metrics with this small number of people, then is when you start promoting about it, talk about it with all the company, with all the team, specifically tell how this is helping your organization. Usually this is something that you can do sooner or later or start rewarding people that embrace those metrics and also start to have improvements. So promoting what you're doing with a small number of people in creation reward programs is usually a big component to help engage the folks that usually would say, not really, I don't care. It's very common to see many organizations, people that are not data driven and they're not going to be very excited about this extra thing that I have to do. But if you promote it, if you show the success that the other people are doing with it and having with it, and you attach reward programs, guess what? Slowly but surely you're going to have more and more of these other people that are not so data driven to embrace it because they are hearing good things about it, they're seeing other people be more successful, they want to be more successful, they usually want to get more money. And then you get more people to embrace it. So usually that helps a lot. And then, you know, as you start seeing this, you start to extend the analytics to more people and do it, do it in phases. Don't push it too hard, don't push it too soft, you know, kind of, you know, feel how things are embracing and keep on pushing, pushing it out, push it out to more and more things, get feedback. You know, sometimes, usually the beginning, the data driven people are gonna be the ones giving the most feedback. But as you push this to more people and as more people embrace, always be open to hear people, just write down, get their feedback by email, talking with them. And sometimes you learn certain things that sometimes from someone that you would never expect that the person would have a good suggestion. And right there you get a great suggestion that helps everyone else. So make people excited to share their feedback, hear them and try to incorporate the people feel that they are being heard and what they are suggesting is making things good for the whole company. And always attach this back to telling everyone, oh, this person gave this great suggestion. Sometimes give rewards for good suggestions because that will help everyone be excited to be part of the process. Finally, monitor User traction. If you have the right tools, you'll be able to see who is using your analytics. And sometimes that can be good for you to see why some people are using a lot and why some people are not using at all. So having a system that gives you visibility into that is also a critical component for this process, for this journey to be very successful in the long run. So what kind of analytics features are critical for all of these things I'm talking about to be a success and to be fast. So first of all, service organizations have usually very different environments for different users. Some users are in corporate sitting on their desktop, Some users are meeting customers in well controlled environments. Some people might be in very harsh environments where they have to access their information. So having a platform that can deliver information in different environments, it's very critical overall when it comes to analytics, being able to look at your data. The solution must be really easy, really fast and allow people to change things dynamically by themselves, even if they're not too technical from an IT standpoint, computer based, but it must be easy, it must be very intuitive, so very critical to have those components. And finally, most or pretty much all service organizations use different systems to take care of their jobs. There is usually a FSM system, a field service management system, project management systems, ERP systems, CRM systems, GPS based systems. In all of the system they have data from the whole process. And for an analytics standpoint you often need to have all of this data to together, to blend them together to see how they affect each other. So blending data from different systems is also a critical component of your analytics system to deliver the value that we're discussing here. So let's talk a little bit about data self now and how we actually have been helping service organizations with all of the things we talked so far. So we just talked about the main features of analytics to make it really successful. And the way we do that is we have a data warehouse in analytics platform to combine data from all the different data sources. You know, the ERP system, the fsm, the pmis, CRM, whatever your data sources are, we use data warehousing to bring the data together. We use SQL Server for that purpose and also we use our own extraction transformation loading tool. So blending the data is easy and fast. Then on the analytics platform we have chosen for six years now, Tableau, because it has proven to be one of the easiest analytics platform to empower users with different ways to consume data. They can consume data if they're in a desktop, using mobile devices, using web Browsers using email. So very easy, very intuitive and very fast. We've been building solutions like this for more than 12 years and we have more than 5,000 reports, dashboards and KPIs looking at metrics all over the organization. Not only services, but everything. Payables, receivables, financials, sales, inventory, purchasing services, CRM, information opportunities, tech support. There's a lot of metrics as a starting point, so we can dramatically help clients to figure out what will be making the biggest impact from an actionable standpoint, sooner than later. So that's a lot of the things out of the box. All right, so let's take a look at some examples of service dashboards and metrics in Data Self. I'm going to switch to the Data Self analytics server and I'm going to log in. Right now I'm playing the role of a services team manager. So when I log into the Data Self portal, it goes right into my landing page. In this case is a regional field service KPIs. Some KPIs that I use to manage my team and this regional field service KPIs are target for every regional manager. In my case that I'm playing this role is for my north Bay area here in San Francisco, how my teams are doing. If I were, let's say the South Bay area or the New York manager, I would see overall the same dashboard, but then it would be only focused on my specific team. And these things, how these dashboards will work for every service manager can be just by security. Let's say it is the same dashboard for everyone and then by looking at my credentials it only gives me my own information by default. Or it can be specific because sometimes different regions have different requirements or different KPIs and then this whole dashboard can be customized by different region. So Data Self provides this whole functionality of being based on credentials or based on functionality, whatever that you want. In this example, this dashboard has three main segments. This top left is about technician and it's showing first time fix rate as well as the average time to fix. And for all the technicians that are not meeting their first time fixed rate, they show as in this orange color, both the fixed rate as well as the average time. The bottom section is for team allocation and this is kind of showing kind of live. The team allocation throughout the north Bay area in this quadrant is showing the 90 day average response time and pretty much for the enterprise has been pretty stable because it's being awesome. You know, we pretty much have on site people all the Time. So it's being on site. Overall very good. But for the industrial and for the commercial, we're trying to lower the average response time. And overall, as you can tell in this sample dashboard, the organization is doing better than the last 90 days. The right section is this organization is heavily focused on servicing products they have installed. And then one of the main metrics is the uptime of the product. And if it's not meeting the target of 82%, it also shows in this brown color. So everything that is in this color here in the stop section and right session is because it's not meeting our goals. And then at the bottom we have the average time to fix and the first time fixed rate. So this is an example of a dashboard showing KPIs for an organization. In this dashboard it's pretty interactive. Let's say suppose as the service manager I want to see Derek. I click on Derek and actually I want to see, let's say maybe the realization of this technician or sorry, the top customers that he's servicing. So let me go to top customers. So when I click that now I go into this other dashboard is already filtered specific for the person that I selected in the first dashboard and is showing the top customers being serviced. And then a bunch of more specific information. So I have the customers, they are sorting a descending order by the contract sales amount. It shows how much we have delivered in sales and are we above or not the actual contract sale. This dashboard is for an organization where one of the main goals is to upsell contracts. So actually the technicians, one of their goals as they do their services is try to upsell more service for the organization. And that's why they are so focused on the variances. The same information I'm seeing the numbers, I can also see here on these charts where the bars are the actual the lines or what is the contract value is. And if I have upsold is in blue. If I have not upsold is in brown color. Now on the far right we have how profitable each of these clients are looking from a low target and high profitability range for the organization. At the bottom I have the details of all the contracts and also a projected six week job demand. Right now this is week 22 and red means, you know, usually these days are super busy black as you know, we have enough resources to take care of them. So this kind of gives me a projection of how I should allocate my resources to take care of the needs. Because for this business they are very seasonal and they can expect what kind of demand they should be serving based on the same season last year. Now this whole dashboard is also pretty interactive. Let's say if I look at this particular customer where I have a very low profitability and I have not been able to upsell pretty much to this customer, if I click on it on this dashboard automatically it drills down and it shows all the job numbers that I sold to this particular email client. This, the actual sales, the contract, the various, the GP and what's going on. From a projection standpoint, let's say that this organization decides to fire this client. You know, it's not so profitable. We're not upselling. This client is really hard to work with. Suppose we don't want to deal with them. You know, people can do that. So hey, I'm going to remove this client from my list here. I'm going to click here and say going to click and I'm going to exclude from my list. So right now I can exclude every single client or a bunch of clients. I can add more than back and right there it's out of the list. And now I got another one that was fit from the bottom and I can keep going to my analysis. So these dashboards are very interactive, very intuitive and very fast. And the idea is for you to come up with dashboards like this to your own team, to your managers, for your upper management, for your dispatchers, for your service people if they need to and make them interactive and make them easy to go from one point to another without learning how to use complex analytics. Now it's also very important that some of your people, they want to be able to change things beyond what's available in this framework. We call those people the power users. The power users are usually the more data driven people that want to explore what else can I learn from my data and to make things better. So I'm going to make an example how a power user can take a dashboard like this and change it completely based on their own curiosity of their data. So as a power user I can come, I have this edit button because I'm a power user, otherwise I would not have it. I click the edit button and I suppose I want to change this dashboard. I will make later a dashboard exclusively for the projection when I do more analysis on that. So for now I no longer need to see this component on this dashboard. Instead what I wanted to this dashboard is a new section on the bottom right that shows me by company. By the way, in my dashboard I have a multi company system for different organizations that Bring the data together in different systems. So I want to see by company the actual sales, the number of jobs and the average amount per job. And I want to put this information in this corner here. So a completely different report from scratch. I'm going to create a new report to show that information. And when I do so that I have in this edit mode, I have here the data panel that is plugged to your systems. And here I see customer information, job information, salesperson, service team. All the data that you need related to your sale to your service can be blended into this data panel. And here I have different metrics that you can see your data. You know we have actual contract information. So there's a bunch of metrics looking at the actuals build contract information, profitability metrics, ratios, rates of your KPIs, time metrics, variances, and a bunch of other things. Anyhow, so what I want to see is let's put first of all contract date. So when I see by contract date, I want to see the actual sales. So I'm going to see actual sales right there. I want to see job count. So I'm adding job count, actual sales and the average sales per job. Now, right now I don't have average sales per job in this part of these preconfigured metrics. How can I find that out? How can I come up with it? Do I need to call it? No, I can build it by myself. I just say I want to create right now a new metric. So a new calculated field I'm going to call average sales per job. And what it does is the sum of actual sale divided by. Oops. Actual sale divided by job count. That's it. I just created a new metric from scratch, just like kind of Excel. And now I have this average sales per job. I double click it. It goes to my report is right here. I want to see in dollar amount. So I'm going to say that this average sales per job. I want to change the format. Format number. I want to see as currency and I should see as thousands with only one decimal. That's it. Pretty cool. I only want to see for this year. So let's filter this report to only show for this year. I'm going to drag and drop contract date to my filters. And I want to say that this field is going to be a relative date based on years and showing only this year information. There it is. So right now I have sales, actual average sales and job count this year. And I want to see by company out of my four Organizations I can take company, I drag and drop my report. So right there, by company, by measure, I have all those metrics. And since it's always this year, I don't need to see the header. I'm going to remove the header. That's it. Just in about a minute or so I created from scratch this new metric. And now I want to attach this new sheet right there where I removed the prior projection. So right here I just edited, let's just resize it. I don't need to name to see the name of the sheet. So remove the title and that's it. In a couple of minutes I was able to change this dashboard to fulfill my own specific needs. I saved it, I closed it. This is only for myself and that's it. Now I have the new section and now it's all interactive. If I only want to see, let's say for this particular customer, when I click on it, I can see among my companies is the AB company that is selling for this customer. This is the actual which matches with this number. This is the average sales per job and this is how many jobs I have sold in this year. The point is by using a framework like dataself, you allow your people to easily and quickly the people who are curious and data driven. If you give them the right, you'll be able to make these changes, slice and dice their data to be more effective. So this is just a quick presentation of how Data Self can help service organizations with easy access anytime, anywhere. By the way, everything that I did here, you can do this on desktop, on web browsers and also on mobile devices. I mean you could take let's say a tablet and do this change just like I did in a tablet by using drag and dropping with your fingers. So Data Self enables that kind of functionality. Folks, we're running a little bit out of time here, so let me go back to my PowerPoint. So for all the things we talk here today, what Data Self can assist you? Well, we have the best in class enterprise grade BI analytics solution. We use data warehousing, we use tableau as the analytics tool. So all of these components can really help get the most value out of your data silos out of the box. We already have covered a lot of different ERP systems, CRM systems and other systems to do the job. And we have BI expertise to tackle those problems. So we're not only providing you with the products and templates, but expertise to get the job done. I hope you found this explanation in webinar informative. I'M going to open a Q and A and because we're a little behind schedule, I'm just going to take a couple of questions to answer. So let me check if we have questions. Questions from Fred what FSM do you support? Fred, thank you for questions. So you're asking what field service management system support? Pretty much if you have any recent fsm, we'll work with them. So we support. So pretty much anything out there we will support. Thanks for your question. Next question is from Chris how long does it take to get data self up and running? Chris good question. To get this data set up and running is usually a matter of hours, maybe days once we have access to your system. Typically for service organizations, the out of the box solution requires some additional or quite a lot of tweaks to fulfill the specific needs that you have. So we can get up and running. We can get a lot of out of the box reports in a matter of hours or days, but usually more around more standard KPIs like revenues, inventory payables, receivables, financials. Those are pretty straightforward overall for the services side per se. Quite often we have to look specifically how you're doing your current KPIs, look how our out of the box KPIs work and tweak them to be sure they address your specific needs. So yes, hours and days to get out of the box with a lot of metrics and probably no extra days or weeks to customize specifically the service side components of it. Chris, thanks for your question. I'm going to take one more question from Carol. We're having challenges consolidating services Reporting from Sage X3 and Dynamics 365. What is your recommendation? So Carol, I guess what you have is you have probably two organizations where one uses Sage X3 and one uses Dynamics 365. And I think you're trying to do consolidated reporting for services. What's our recommendation? So especially when you have systems like that, that you have two different systems that you need to bring data together, a data warehouse is a very critical component to address problems like that. So you need a data warehouse, you need to bring the data to data data warehouse, blend it, consolidate it. It's all data, it's all services data. You have customers, you have technicians, you have products to serve, dispatchers and whatnot. Even though different systems capture the data in different places in the data warehouse, like using data self data warehouse, we can blend this data together. And by the way, we already have a lot of templates for Sage X3 and for the Dynamics 365 so we can easily blend the data together and when you look in the analytics it's easy to see the data like is the same system like you know you're going to look at your analytics and it doesn't matter where the data comes from. X3 Dynamics it's just data blended together. The data warehouse so you do need a data warehouse. Very likely I would say for sure but you know probably talk with details. I'll send you an email later. We can talk this in a more, you know, one on one cases. I can understand more your needs and we'll take it from there. Folks, I'm running a little late today. I'm sorry about that. We. We'll have more questions here. I will be addressing those questions on a one on one basis. So let me just wrap this. Sorry. I hope you found this webinar informative. This webinar is being recorded. I will be sending you later today a copy of this webinar recording, so feel free to review it. If you have questions, comments, please be sure to contact me. You have my cell phone here, my email. I also would appreciate if you give a feedback about this webinar at the end when the session ends and in our YouTube channel when you get access to our webinar recording. If you like it, please be sure to subscribe to our YouTube channel and as well as like it. I hope you found this session informative. Thanks for your time and have a great Tuesday. Thank you.