Hello everyone and welcome to today's webinar on Best Practices to Becoming a Data-Driven Organization. This webinar is presented by Target and my name is Jared Cornelius and first it's a pleasure and privilege to be presenting today and I thank you kindly for listening in. I'm the Director of Pre-Sales Consulting at Target. That's Target with an IT, not an ET, not the big retail chain. And I've spent my career, probably nearly two decades, in data analytics and business intelligence. And if you're not familiar with the role of pre-sales or pre-sales consulting, pre-sales engineering, we essentially act as the consultative and technical resources for organizations that are researching, evaluating whether or not our products and services are a good fit to meet their desired outcomes. And I've had the opportunity to work with hundreds of different companies over my career across most different industries, all different sizes, and on their transformation to becoming a data-driven organization. And one of the things that never ceases to amaze me is how unique that transformation is for each organization. Even between two seemingly identical companies besides their names, their goals and objectives, the challenges faced, their personalities, the complexity or lack of complexity required out of the solution, and various other aspects make every situation unique. However, on the other hand, regardless of company size or whether it's a regional heavy equipment dealer or a global 100 financial institute, there are definite commonalities in the approach and journey that those who are successful share. And so my goal today is to share with you what those commonalities in the journey look like and provide some practical guidance and examples on how to become a data-driven organization. We do have a Q&A via the chat window there. So if any questions come up throughout today's presentation, please enter in your questions and I'll be happy to follow up at the end of the presentation. So if you're wondering who Target with an IT is, this is my one obligatory about us moment. And Target is a business intelligence and analytics software and services provider. We operate globally with thousands of customers worldwide and we also have a dedicated practice centered around the heavy equipment industry. Lots of deep expertise, tons of success stories and even purpose-built solutions. Target is also consistently a leader in the business intelligence industry per the pundits or the industry analysts. So as an example, BARC is a BI and analytics research firm who conducts the most comprehensive annual survey of business intelligence users in the market and reports on vendor ratings across different categories. In the most recent survey, Target was ranked as a leader across 16 of the categories including a 91% customer recommendation rate which is significantly higher than the industry average and the other predominant BI software vendors. So jumping into today's topic, here's our agenda. And before we get into the best practices of becoming a data-driven organization, we'll first examine what that even means. From there, we'll cover the critical planning activities and considerations that will act as both your roadmap and compass to keep you properly oriented along the way. We'll then look at three stages and how organizations commonly start and evolve their journey from better business insight through to really leveraging data as a strategic and competitive differentiator. And once we're all hyped up and we know what it takes to get there, I'm going to bring you back down to reality by sharing some common pitfalls that can often lead to maybe false starts or missed objectives, maybe poor user adoption or otherwise less than successful attempts at this. But we're going to end on a high note and we'll see some of the many real-life success stories of companies, probably much like yours, that are in the equipment distribution and rental business. Let's begin. Ah yes, business intelligence. You've heard me say this term several times already. I'm certain some of you are familiar with this term to varying degrees. And I'm also confident that it's new or perhaps a little vague to some of you. I've actually had people joke to me that business intelligence is an oxymoron, to which I can't help but think we must have worked at the same company in the past. But I know what you mean. So let's briefly define what it is, why we should bother with it, and when it becomes important to an organization. Then we'll get into the who and how aspects of BI next. A quick Google search on the definition of business intelligence, or BI for short, will take you to Gartner's IT Glossary. And if you're not familiar with Gartner, they're another leading research and business advisory company, probably the largest or at least most well-known analyst firm. And their definition is pretty solid. And I love how it begins by acknowledging the vagueness. It's an umbrella term that includes the applications, infrastructures and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance. While I totally think this definition is accurate and precise in its brevity, I still think it's a bit fluffy for the layperson like myself. So I took this opportunity to give it my own shot, which is basically technologies that help make data a strategic asset for a company. And sure, if we want to get technical or at least philosophical about it, BI is more than just technology. And we're going to surface some of those non-technology aspects today. But when people talk about BI and business intelligence, they're pretty much always talking about BI tools, software solutions that are implemented to get better visibility into data about the business in order so that they could take timely action on it. And now, the why business intelligence question is fairly obvious and there are countless valid answers here. But again, just sharing some of the key and common drivers that are among the reasons why companies leverage BI tools. It's so information is readily available and even accessible on demand without involving IT heroics for each and every question that somebody wants to ask about the business. Another driver is to provide a single version of the truth, right? One master hub of information that has data blended across diverse operational applications and data sources. The data is clean, it's consistent, it's validated and trusted and everyone's looking at the same results versus folks going off in their own silo to cobble data together, calculate their own versions of particular metrics, you know, only to find out that five others had done the same thing and got five different answers. The third why BI listed here is data democratization. The idea that the more transparency, the more people, in fact, everyone in an organization has access to the same information that it empowers individuals at all levels to take action and make better decisions. This could be a controversial motivation for BI and in certain industries or in certain aspects of any business, there are clear boundaries and lines to draw for who can access what data. But for today's purpose, we can keep this pretty simple. It's really about giving more people greater access to information so that they can be more effective. That could even mean just going from, you know, two to three executives having, you know, limited visibility to, you know, five to ten members of the management leadership team having access. The idea being is that without a doubt, the more folks with diverse expertise armed with the same information and sharing the same company vision, the stronger that company will be. And the last why on this list is really also a when, which is basically now. And, you know, we're in the midst of the data age, the data revolution, right? Data is the new oil, a lot of people say. And this fact is obvious if you're a tech company in Silicon Valley or, again, you're one of those Fortune 500 enterprises that may be drowning in massive volumes of data being generated from all kinds of systems and applications spread around your company. But, you know, for a heavy equipment dealer operating, you know, five, ten, twenty regional locations focused on stocking parts and selling, renting, servicing, you know, real tangible assets and machinery and equipment, you're not necessarily unindated with the Walmart volume transactions or inventory logistics. It can be hard to imagine how much impact data can have for you. Well, those that are leveraging data analytics are, at a minimum, operating more efficiently, at lower cost, and with more agility. And my guess is that there is at least one equipment dealer out there, could be relatively small today, who will radically change the game in the near future with an innovative data-driven approach. And this is happening really across all industries over the last couple of years, and it's going to continue well into the future. For those Shakespeare fans out there, we know this is an age-old question he wrote about in the opening line of Hamlet, which is, to BI or not to BI, right? And, you know, all joking aside, surprisingly, there are more dealerships that don't yet use a BI solution than there are those that do. And, you know, perhaps worse, as many may think they are employing BI, they're actually eons away from access to the insight they really need to truly make data-driven decisions. And, you know, probably, you know, small dealerships make up the obvious majority in terms of sheer numbers. There are still plenty of multi-million dollar companies that are stuck in this kind of pre-BI stage as well. And, you know, these dealerships are using the same sort of management information systems today as they did 20 years ago. Standard reports produced out of their operational applications, like the dealer management system, the ERP system, static analysis created in Excel. There's a plethora of problems with using these systems for measuring and monitoring key performance indicators. And, you know, users are likely consolidating reports from different data sources. It's time-consuming, very tedious, open to human error. There's usually one or two individuals, sometimes in the IT department, who can really only do this, and they've got other tasks, and they create backlogs trying to fulfill requests and get different aspects of the business the answers to the questions they're asking. So as such, it takes days to pull a report together. And that means that your metrics, what you're monitoring about the business, is only happening monthly or weekly at best. And really, at a minimum, many of these metrics should be measured daily and ideally monitored around the clock, seven days a week, right, 24 hours. If it takes, you know, multiple days to run a single report, it's simply not possible to make strategic business decisions. In other words, you're making decisions that are, you know, reactive versus proactive. And so who are these data-driven organizations? What are their characteristics? Well, I think the greatest way is just to say they're lean, mean, and disruptive machines. These are companies that have gone from those reactive decisions, having the ability to look into the past with reports, that hindsight, if you will, about what has happened, and they've progressed more into the what's happening right now, and ultimately evolve into how can we project and predict what's going to happen in the future so we can take preventative, proactive, or corrective actions now before we get to that end result. So it's an evolution that encompasses not just what's happened in the past, but also what do we anticipate and project with high confidence is going to happen in the future. As I mentioned, these are companies that are operating very efficiently and effectively, and they thrive on that. They're able to keep costs down. They're able to be more productive in their day-to-day activities, and they're using data, and data is at their fingertips, and they're spending less time worrying about the numbers, more time acting on it. These are also companies, we see a lot of these and make headlines in news, but they level the playing field in the industry, and they really change the way that industries really exist, right, in terms of how they're using data. The barriers to entry become smaller. Smaller companies are able to act much larger than they actually are and ultimately grow to be much larger than their competitors. These are also companies, many studies have been done on this. I was reading one this morning actually from the Harvard Business Review that discussed how companies that invest in analytics technologies going into bad economic climates or even during recessions are able to survive and thrive at a much higher rate than those who don't. These are also companies that are keeping their customers, keeping their partners, the various business relationships, very satisfied, right. They're delighting these people with their openness and the insight in how they operate. They really see this agility as a differentiator. They're able to pivot much faster. They're able to take corrective actions. They're able to gauge whether or not a choice they made is going in the right direction and continue down that path or change before it becomes an issue. Really over this time as they go through their journey, more and more this becomes part of their culture and data gets ingrained in their DNA again across the organization and it's just how they operate as a business. Now let's talk about some of the challenges, right. Because again, the famous president in the past said nothing worth doing is ever easy. That certainly can be the case with going about a data-driven initiative. What are some of these common difficulties that make leveraging data so hard? Well, you've got the data itself, right, and there's lots of challenges related to that just in terms of the number of sources, where the data comes from, the size and structure that it's in, do we have access to it or not, and where does that data reside? And how long do we keep it for? There's all kinds of technology implications that are related to this. And do you have the right resources in-house, the right skill sets to leverage some of these technologies, or do you have to train additional people, right? Beyond technology and even data, you get into just sort of the people aspect, people in process and business policies, right? A lot of these may need to change. There's typically a pressure of the status quo that gets in the way, right? It's in people's nature to resist change or to not see the value in needing to do things differently. And all of this can hinder an organization's ability to push forward and ultimately achieve successful deployments of business intelligence technologies. And unfortunately, many of these pressures never end, right? If you break through the initial barriers, you get the company on board, you've got the right skill sets, you're leveraging data effectively, a year from now, people are going to change, processes are going to change, business is going to grow, more data is going to be available. So it's an ongoing evolution. And it's not easy, but it's worth doing, right? And that's what becomes so critical, is just to really understand that for even the organizations that have been successful and are successful, the poster children, if you will, of data-driven companies, they face these same challenges, but they're aware of them and they're able to press forward and respond accordingly. So planning and preparation is perhaps the most critical stage before you get started down this journey. And it's really about knowing where to start and what the ultimate goals is going to be, right? What does success look like for your business? So there's a number of things that come into this. And of course, it's understanding what the business's goals are and initiatives are. It's knowing what areas will have the most impact and get you closer and closer to meeting those goals. And it's also making sure you're using the metrics or these KPIs that matter most for what your individual goals are. Because again, no two equipment dealers are the same. No two of any company are the same in terms of the details, the nuts and bolts of how they operate and what individual owners and leadership teams want to accomplish. And so metrics will change. And you want to be sure you're focused on the ones that count. You also have to know who your users are, because they're not all created equal. And again, this can be a change in culture for an organization. And that change needs to be encouraged and fostered and inspired, because there are always holdouts of individuals who don't like to evolve or are just happy with the way things are going now. And they end up being resistant to doing things a different way. So those are people that need to be focused in and brought along on this journey and ultimately converted to getting behind it. And then ultimately from the bottom up, top down, middle out, everybody needs to be aligned on the priorities and the goals and be on board to tackle this. So finding your target. As with most things in life, the way to successfully move forward is to first determine your end goal. And most business goals will ultimately aim at either increasing revenue or reducing costs. Sometimes goals are related to things like meeting safety standards or regulatory compliance. But I find it fascinating when I first am meeting with a company to try to understand what their objectives are, what they hope to accomplish with a BI project. I so often hear things like, we need a better reporting tool, right? Or we need a tool that's easy to use. And these are stated as if they're their business goals. Now, they're valid statements, but they are not good business goals. And they don't orientate the project on a clear path to achieving the desired business outcomes. So when preparing an analytic strategy, it's critical that the folks responsible understand what the company's top strategic initiatives are and map the goals to them. This basically helps connect the dots from all of the smaller discrete tasks that will need to be done through the KPIs and metrics that will measure progress towards your goals, and then ultimately how those goals will help execute on the vision of the company. With a good grasp on the business objectives, you also want to look at areas that can have the biggest impact. So this is going to help when it comes to prioritizing where to begin. Because all goals are important. Some may be more important than others. But you may find there are small steps that can result in a large impact, and that may alter different areas of where you start so you can get fast results and more bang for your buck. For all the good that data can do for a business, there's one major issue that it presents, and that's information overload. Many companies try to focus on too many different metrics without focusing on those that truly matter for what they are trying to accomplish. Now, a good foundation for identifying metrics that matter is understanding lagging versus leading indicators. And think of the data as being made up of cause and effect factors. It's really a difference between looking back and looking forward. Your lagging indicators are typically made up of accounting and historical measures, right? Financial ratios, revenue. You'll often see these things on, like, income statements, for example. On the other hand, leading indicators are also historic, but they look at internal processes or even external events that occur prior to, say, revenue coming in, right? For example, discounts and promotions or maybe conversion ratios or customer retention. Maybe it's fill rate in parts, right? These are indicators that you can focus on if you want to improve results. Lagging indicators are important for understanding current conditions and what's happened, but it's the leading indicators that often deliver more valuable insight and really let companies take the proactive steps instead of reactive action, which can save time and money. You'll also want to determine and distinguish between internal versus external indicators. Internal has to do with the processes of your business, right? It's the information that you most likely already own in some business system, right? And really talking about the data and how this data becomes available. Internal data, you likely already have. The external factors really has to do with aspects outside of the company that still affect your business. So this could be customer needs, maybe competitor pricing or other moves that competition is making, market rates on equipment, right? It could be seasonal changes. This data may not exist within your walls today, but it is out there. It does exist. And for the metrics that matter, you'll want to start tracking and registering this and keeping an eye on it or at least planning for it as part of your journey. And then once you've prepared sort of the string of events that will most likely impact your final goals, you'll have a great starting point for identifying which data sources to include in your business intelligence solution. Because that's really what we're talking about today, becoming a data-driven organization. This is really about starting at the top and then backtracking and following that chain back to where the data resides in order to effectively make those decisions. So those are going to be the data and the data sources you should worry about connecting in an analytical environment. So you've got to measure what matters and you also want to start with the sources that contain the right information. You also need to know your role or more specifically know your user's roles, right? The employee's use of BI varies widely across departments and functions. Every analysis, each dashboard report, it must answer questions in a way that makes the most sense to those who need to know the answers. And these are four common BI user personas that act as a guide. But of course there's always going to be individuals that fall between the different personas or even fit into more than one role. Kind of quickly covering these kind of common areas. The information consumers are often your executives, right? Owners, the managers. It could be line of business employees, sales employees. It could even be external, your suppliers, maybe your customers, your partners. They typically receive data in the form of reports and dashboards and they're really not actually concerned or even know about the BI solution, what BI software is responsible for generating the information. Other members of their department will probably be involved in defining and building the requisite dashboards and reports, which are then made available to the information consumers. And that's typically made available through online, maybe integrated into an existing portal. Information consumers will access this content on their mobile device or in an ideal state can even embed and integrate business intelligence information into an operational application like your ERP system, your dealer management system, or CRM system. Now business users are typically folks like your maybe some VPs or controllers, your COO, sales managers, right? These users, they've got access to the same information as the information consumers, but they demand a more feature-rich BI environment that gives them more flexibility when needed. So that could be occasionally adding new criteria or setting up alerts in the system so you can notify relevant people if data changes. And these users, they typically motivate their teams or their business units by sharing and collaborating on analysis. They're looking at benchmarks. They're looking to adjust information within the analysis so they can spot opportunities or threats. And then they need to rely on the right tool so they can keep their team and everybody well informed. Your business analysts, well they often have the word analyst in their title, right? Sometimes they're just called business analysts or marketing analysts or otherwise. They could be business developers or sometimes even data scientists. These users are really the power of power users and often they're seeking to test new hypotheses about their business. They want to analyze data beyond what's in the central data warehouse, if you will. They want to go get new sources of information and try and garner new insight that hasn't been discovered yet, right? These are people who are very comfortable within a BI environment and they also have the ability to handle external data outside the walls in addition to the internal company data. So these users are going to be ones that need to leverage all of the advanced functionality of the BI software and often without any reliance or even oversight from, you know, an IT department who's otherwise controlling access to, you know, information for information consumers and otherwise. And the last role here is the information designers. And these are typically roles in IT or an information officer. Some organizations have dedicated, you know, analytics or BI practices. It could be a BI manager type of a role. And this persona is really the one that designs reports, right, and designs dashboards and is a primary content creator for others to consume. And so these folks are the ones that need to be well trained in the BI software. They need to have a solid understanding of how to build the content that supports the business users. And it's also critical that they understand and know how different visualizations should be used to present data most effectively. There's an art, there's a little bit of art in that as well. But these users take more of a business development approach when they're defining and designing new dashboards and analysis and working together with the business users and the information consumers to get them the information that they need and empower them. And the last area of the planning and preparation stage is really all about prioritization. Really important thing to take here is you need to think of this as more of an evolution versus a revolution. Very often companies get excited about the prospects of data and analytics. They've got a hundred different goals, grand visions, and they want it all now. They want to open up access to all departments on day one, right, this big bang approach. Well those, unfortunately, are the projects that fail most often, right? They take too long to deliver. They don't meet the needs of everybody. And nobody ends up being really satisfied with the results. It's way more important, again, looking at the goals overall, understanding the metrics, the data sources that house the majority of the information and can cover most of the key metrics you need, and look for the smaller steps that can have the biggest amount of impact. So you can get quick wins, right? It may be starting with a single department or it may be starting with two or three key metrics that are most important to the business. If you can get those into the hands of a few users very quickly, that's the beginning of what builds momentum and you end up having this snowball effect where that first little win makes the next win even bigger and faster. And it's this domino effect that we see here and ultimately you're delivering on a large goal, but you've done it in very small chunks and everybody is on board and getting more and more motivated along the way because results continue to be met and delivered and you're progressing very clearly through this journey. So prioritization is a key aspect when you're looking at, where do we begin with this? Because we know we've got a lot we want to accomplish, but it's important that you don't try to bite it all off as the first step. Take smaller steps and it'll lead to way more success. Now let's take a look at what we see are common stages in this journey, right? And this is something that, again, regardless of the industry that you're in, regardless of the size of organization you are, really these are common across the business, right? And it's really, they're different stages and states that we see time and time again. And there's typically a progression from one to another, although there's always overlap and they're not clearly defined lines. These three stages are, number one, it's really just getting started, basic BI and analytics. I'll talk about what that means. The second stage is real-time data discovery and action. And the third is competitive analytics. And we'll see that throughout these three phases, the level of sophistication increases at each step along the way. So the first stage, basic BI and analytics, this is the operational level of BI. This is really when management has committed to that unified version of the truth. They've got approved data sources and they start to dig into internal data and reveal new kinds of insight. Typically at the beginning of this stage, analytics is often the tool for a few super users in IT or maybe finance department, maybe services is the priority, and so it's a few users within the services department. But as an organization becomes more mature in BI and analytics, they've got those first initial quick wins, standard reports start giving way to more specialized reports. And this is a continuous cycle of better insight leading to better questions and better answers, and that sort of flywheel gets set in motion. So typically those utilizing the solution at the beginning stage of BI are IT, finance, sales is another one. But the information is easy to disseminate throughout the organization. You might have dynamic storyboards that are up in maybe a service base, so those users who don't sit in front of a computer still have visibility into the metrics that matter to them. And this information is remaining consistent, up-to-date, and accessible on demand, again, versus waiting for that monthly report to come out a month after the previous month. So by the end of this stage, there's an increasing number of people in an increasing number of departments that are embracing BI and analytics. It's still at the operational level, but these fact-based processes are on the rise. Now stage two is really, we're getting hooked on BI, right? The organization's really growing. That culture of data is being ingrained, and the organization is growing in their analytics maturity and evolving more into this real-time data discovery and action phase. The company begins to expand its ambitions to even broader data sets, right, than they haven't been looking at yet. And this is where, again, some technical challenges might come into play here, but it's because the volume and structure or unstructured nature of data available for these analytical use cases increases, sometimes exponentially. So you're including sources of information that are far beyond what you might have today in your dealership ERP system. And as that data volume, the variety, the velocity increases, so does an organization's ability to make use of it. Much more opportunity to take advantage and leverage data strategically. But your analytic resources and skills are no longer, or should no longer be limited to just a few departments. Everyone from sales to rentals, services, should be leveraging and benefiting from this information. And that way, decision-making is spread throughout the company because BI is arming users with the information they need to make decisions and take action with confidence and trust in the data that they have. So user adoption is increasing in this phase. You might be taking advantage of features like embedded BI, where you're placing that content, that dashboard or report directly into an application or portal where employees are working in most, right? Maybe that's SharePoint, your company. Maybe that's your dealer management system, right? Actually, the ability to embed more analytical content, more insight right into the system that they work in daily, again, increases adoption and enables them to be leveraging that same information to be more efficient in what they're doing. Mobile BI also becomes more and more important here, too, because it's making it possible for users to access the information they need from any device at any time and really no matter where they're at. And I see this a lot, particularly in the equipment space, where you've got a lot of folks out in the field, right? And data is important for them to be doing their job effectively, but they're not at a computer. They're not carrying their laptop around, but they will have tablets or they will have their mobile phone. And so it's at their fingertips in order to access that data. And it's really in this stage where, again, beyond that basic BI, now increasingly the analytics is not just reactive, not just looking back on the past performance, but it's also proactive. So you're making more real-time operational adjustments, right? You're looking at sales trends and making sure if they're declining, you're not ordering as much inventory the next month. So you're keeping those costs down, right? You might have some automated notifications going out based on different analysis. So you're keeping people informed and abreast of key developments without them having to be glued to their screen looking at a report. Predictive analytics also can be leveraged here so you can learn from the past and provide educated guesses within a certain threshold of confidence about probable futures. And then, again, moving here into the third stage here, we're really talking about competitive analytics. So although I kind of propose this stage as the last of the three, four data-driven striving organizations, there's really nothing final about it. The leading edge of analytics is consistently moving ahead and changing the game it's playing in, right? And this is really the world that I live in. And that constantly pushing the boundaries, pushing the limits, enabling innovative capabilities that make accessing data and putting it to use and leveraging it effectively easier and easier and more and more maintainable and manageable for organizations. So really in this phase, companies that are operating at this level of maturity, they're all about using data disruptively, right? They employ maybe highly skilled analytics resources. Oftentimes those are very hard to find and companies can't find enough of them. But this is really when ad hoc analytics with external data sources really come into play. In other words, this is when companies really start cooking with gas and leveraging this term you may have heard of called big data, right? That could be because the true insight and real innovative aspects of looking at data may be in sources that are the volumes of data just too large, right? Social media type platforms, right? Large-scale information that can be tricky to mine, but lots of valuable insight can be gained inside of it. So today, BI users, they got tools that are readily available and tap into the power of external data sources. Equipment watch, weather data is becoming increasingly important over the last few decades. It could be CSV files, Rouse services, Dodge report, anything you might find out in data markets to be able to analyze their own internal data against and assess their company. So with much more data available, it really won't be long before a company's external data far exceeds their internal data and what's inside their ERP system. So really at this stage, you can consider the doors of insight blown wide open with these capabilities. Companies pulling in information, again, if we're talking about weather data to see how that weather has impacted your seasonal revenue, right? So many dealer companies have very clear seasonal definitions, right? Particularly in the north, a lot of equipment may be related to clearing snow and such. And as weather patterns change, that season shifts across the calendar year and it's important to be able to stay ahead of that and know when it's going to happen or how to realign maybe your fleet in order to take advantage of the off-season periods. You want to be able to map demographics to maybe plan the ideal place for a new location, right, a new dealership. You could even analyze market price fluctuations compared with your pricing so that you know if you're competitive or not. And savvy companies can even track the raw materials pricing to decide which products to launch now or maybe how best to engineer new equipment in the future. And more and more so really between stage two and stage three, personally I've been working with a lot of customers in the equipment dealership who are beginning to leverage very effectively telematics data, right? And there's so much insight and value to be gleaned when you're starting to blend actual equipment utilization, engine hours, right, with information maybe from your dealer management system, understanding service level agreements, maintenance dates, more accurate billing based on what the contract stated versus what a customer has actually run the equipment for, being able to be more strategic about when they drop off equipment, making sure that truck doesn't come back empty, if they can pick up other equipment on the way back home. Just really becoming, again, more effective, more efficient in their operations as well as more accurate in their billing and revenue streams. Some common pitfalls, right, and this is again tremendous value in leveraging data, but there are certainly a lot of places where the goal and the journey can fall down. And so I've really pulled together some of the ones that I've personally seen maybe too often, right? I mentioned some of these in brief, but the big one here again is this notion of you should view this journey as an evolution versus a revolution. Don't do the big bang approach. It's too much to bite off. Even taking an evolutionary approach where you're going to start small, that in itself requires work, requires the right folks, it requires the right planning and the right execution. So you want to make sure you're keeping it small, and you want to make sure that maybe if you're not in the leadership group or at the executive level, you want to make sure that they're aligned with your plan and the priorities and the order of events that you're going to take on, so that they're not expecting everything to be delivered at once and they know that more and more value is going to be delivered as time progresses. But starting with that small approach and taking the evolutionary approach really allows you to get that time to value and really get a return on your investment much faster, right? And even regain that initial outlay, because we're talking about technologies, whether it's software, infrastructure, whatever, that costs money, and you want to make sure you're going to get that money back, and how quickly will you get it back? Well, if you're starting small and with the most strategic and most impactful activities, you can recoup and start realizing that return much faster than if you try to tackle on a massive undertaking and deliver everything to all departments and meet everybody's needs at once. Another thing, this is more often when I'm working with maybe folks that are more like on the IT side, maybe a little bit more removed from the strategic initiatives of the company or the business goals, but they're the ones evaluating technologies. And they're looking and they're asking questions about features, right, cool capabilities. Can your chart do this, right? Can it do that? Not saying those aren't important or necessary, but it's important that you're focused on the business outcomes that you want to deliver. Because let's be real, right? A chart is a chart is a chart, whether it's in Excel or it's in a fancy BI tool, right? That chart isn't what's going to make or break. What you can click on or how you can color that particular chart isn't going to make or break your business goals. So you want to keep, again, the outcome of what you're trying to deliver as the driver. And as you progress, make sure that the capabilities of any technology you're choosing facilitate you getting to those outcomes. And if there's really cool stuff that, you know, one particular analyst really needs, but it's not delivered on the platform, you know, those are things that you might need to deprioritize. Or more and more companies are choosing sort of the best fit. You may have multiple tools based on their capabilities and based on the users who need them, right? So a BI platform may facilitate 80% to 90% of your users' needs and deliver on the outcomes that you're looking for. And there may be a subset of users, a handful, small handful, who need an additional, you know, deeper advanced analytics tool beyond what a BI system plays. That's perfectly fine, and it's becoming more and more the norm, actually. And also, you've got to understand, you know, this change must go beyond just the technology. This is really all about, again, the culture of the organization. There's, you know, again, time and time I work with folks at companies, many people within the same company who are all on board, right? It could be Susie in IT who's spending 50% of her day cranking out reports, and she's got a backlog that's going to take her, you know, weeks to get to and, you know, tons of people waiting on her. So she wants easier reporting and better capabilities. You might have a COO who, you know, can't – who needs information faster than, you know, once a month, right? Needs that, you know, faster than once a week. But if the whole organization isn't beyond it and everybody's not, you know, engaged and vested in a change and know the insight, it will fall down somewhere along the lines. And it's usually somewhere up the chain, right? Whoever's ultimately sponsoring this, if they're not on board, regardless of how many other people are pushing for it, it's just not going to happen. Again, insight for all, not just the few. This is really where I work with companies that are just really hesitant and just don't believe in giving more people access to the information, right? They kind of like to guard it themselves, and it's really maybe they find their value and purpose in having access to that data, and they don't want others to share in it with them. Well, those are very clearly approaches that more often than not lead to bad implementations, right? And that's really not the goal. The more people leveraging the same information, being on the same page, the better off and more successful a business analytics project is going to be. And the last one on here is really about choosing the right partner. And the alternative is, well, you could choose a bad partner or the wrong partner, or many people, you know, look at it as going it alone, right? So there's organizations that have really sharp people, really smart folks, maybe have done a number of data analytics projects in the past, and they say, we got this. We can do it on their own. And sometimes that is successful. More often than not, at least in the early stages, it's important to work closely with an organization that knows your business and offers the support and resources to get you going, get you at least pointed in the right direction and is there whenever you need it so that you can ensure continued success, you know, fast time delivery. And, you know, as you progress throughout this journey, can offer insight, guidance, share other industry tips, what others are doing with you so that you can ensure you are sort of at the cutting edge and keeping abreast with how the industry is leveraging data. That way you're open to being more innovative versus always in this catch-up mode or maintaining and drowning in your own project. So wrapping up here, just a few examples of what success looks like. And I picked these again. Maybe this was my last pitch. These are actual customer examples that we have here at Target. Again, there's a large portion of our business is dedicated to the heavy equipment industry distribution, rentals and sales and so forth. Companies that own and operate multiple locations with service bays and fleet vehicles and so forth. And among these customers, these are three common areas that are pretty consistent across all of them, right? Areas where ROI is achieved through their use of analytics and in most cases is achieved very quickly, right? So the first one here is department productivity. And I put in this quote from one of our customers, Best Line Equipment, and he noted that since implementing Target, and really, right, you can cross out Target and put in business intelligence solution here, assuming it's successful. But since implementing Target, they're averaging an 8% improvement in productivity per department per week. And he was able to extrapolate that out into a monetary value but asked us not to share what that monetary value was. But I can tell you it was impressive. And this was done in their first roughly four months of implementation. That productivity comes in a number of ways, too. Sort of, I guess in my mind, the most obvious area that productivity was gained was just in the reduction of hours pulling together information, right? His IT team at this, that's basically what they spent a huge amount of their time working on were just consistent ad hoc requests for different information. Having this tool was able to allow them to dramatically reduce the amount of hours they're spending on that and then ultimately dramatically cut even the number of requests they were getting because users are now more empowered with better information, better interactivity through Target's business intelligence suite. That productivity also comes at the management level beyond just the IT team and lowering kind of the amount of effort it takes to deliver information but the end users who need to find that information. They suffered from, as well, most companies get a breaking point or are faced with, they've got static reports in Excel sheets everywhere, right? And nobody over time, nobody knows where to go to find what piece of information. You bring up different reports, they're not the right time frame, they got slightly different answers. And so the management, the people that need that information are wasting time just even finding it, right? So there's improvement gains across the board when it just comes to delivering insight. In the middle here, very common area that people get a lot of return and value out of BI is around technician utilization. And this is where a customer of ours, Kirby Smith, was able to boost their tech utilization by 15% and they achieved 100% return on their investment in the first three to six months. What I love about this, they did this in a couple of different ways, but my favorite one was it's really, it goes back to the democratization of data. In their service base, simply put up in a screen basically live scrolling storyboards, dashboards of technician utilization, right? It's almost like, sort of gamified it among the technicians, right? So they can see where they rank among their peers and nobody wants to be at the bottom of that list, right? So not really changing much about process or cracking down on them. They just put up the information on a dashboard, everybody's looking at it, and everybody starts upping their game because they don't want to be on the bottom of the list. Boom, utilization improvement 15%. That's awesome. And then another final one, very key. This is where a lot of money is saved from the bottom line is on inventory turns, dead parts, slow moving parts, and so forth. Just countless examples, of course, on our website and elsewhere where you can find how companies such as Martin Deerline are using analytics and BI so they can identify aging skews, right? Repurpose them, send them back for maybe their warranties and rebates. And so that, their ability, their insight into those dead and slow parts gave them a seven-figure ROI improving their bottom line 5%. So those are just a few of many examples on really how organizations are getting real value and being data-driven and what it means for their business. So lastly, I just want to thank you very much for listening in today. I really appreciate your time. I hope you found this information insightful. This is my name again, Jared Cornelius. My email address is JACO at Target.com. I would be more than happy to take any of your questions. You can reach out to me directly. I encourage you to visit our website at Target.com. We have a number of e-books, again, dedicated specifically to the equipment industry, whether you're focused on rentals or you want to understand what are metrics that others in your business are leveraging, what do those look like, how to get access to them, lots of great content that you can explore there. We have a number of videos and webinar series as well. And again, like I mentioned, you can read more customer case studies of customers in different industries and lots that are in the equipment space as well. So thanks again, everyone. Have a great rest of your day and take care.

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