Learning Without Scars
As a third-generation educator, it is easy to say that teaching and training are in the blood for Ron Slee. From his beginnings as a coach, through his time at McGill University, Ron developed a foundation for the work he does today. From working within dealerships, to operating a consulting company, creating a training business and running twenty groups, Ron has been directly involved in this Industry since 1969. Ron has been known as the industry expert for years, and has brought this expertise to bear through his training programs. Today, Ron provides specialized, job function based internet based subject specific classes, job function skills assessments, as well virtual seminars and webinars. These courses are designed for manufacturers and their dealers, as well as independent businesses in the construction equipment, light industrial, on-highway, engine, and agricultural industries through Learning Without Scars (www.LearningWithoutScars.com). This platform is a continuation of the work begun by Quest, Learning Centers which was established in 1996. This training is aimed at improving dealer parts and service operations through qualified people that are knowledgeable in using operational metrics and current market and operational best practice methods.
Learning Without Scars
Revolutionizing Business and Life with Data Analytics
Ever wondered how data analytics can revolutionize your business and even daily life? Join us as we sit down with Luke Kominsky, a seasoned data analytics expert who takes us through his journey from the corporate world to exploring the globe, and back to the warmth of family life. Luke’s passion for data shines as he explains the crucial role of data in modern business decision-making and the challenges of managing and integrating vast data sets. He highlights the need for educating business leaders on data utilization and the ongoing battle between data governance and self-service analytics.
Through an engaging discussion, Luke draws fascinating parallels between data-driven disruptions in business, like Amazon's impact on Walmart, and the potential future of car dealerships. We explore generational differences in tech adoption, revealing how younger professionals are spearheading innovation. Luke doesn’t shy away from the grunt work involved in data integration and quality control, giving us a realistic peek behind the scenes of data analytics. He also touches on the lag between technological advancements and their implementation, noting how many businesses still rely on outdated systems like Excel despite the availability of advanced tools.
Looking to the future, we dive into the skills gap forecasted for the workforce by 2030 and the slow adoption of technologies like augmented reality. Luke emphasizes the importance of continuous learning and employee development, drawing from examples within the education system where data analytics is transforming student performance tracking. We also reflect on the importance of mental clarity and personal roles, underscoring the value of a supportive family life. Wrapping up, we express our gratitude to our listeners and look forward to bringing more insightful conversations to light. Join us for a thought-provoking episode that’s bound to spark your curiosity and inspire action.
Visit us at LearningWithoutScars.org for more training solutions for Equipment Dealerships - Construction, Mining, Agriculture, Cranes, Trucks and Trailers.
We provide comprehensive online learning programs for employees starting with an individualized skills assessment to a personalized employee development program designed for their skill level.
Hello and welcome to another Candid Conversation. We're joined today by an interesting man who's followed an interesting path. His name is Luke Luke Kaminsky, and he and his wife decided, before they had children, to leave corporate America and wander the world, which, first of all, makes him different and somebody that's appealing to me. And then the two of them came back and got going and started a family, and here we are. Luke's primary specialty is data analytics, so I'm just going to ask Luke to introduce himself and tell us a little bit about what makes him passionate about data analytics. So, luke, welcome aboard and the ball's in your court, sir.
Speaker 2:Well, thank you. Thank you for the kind introduction and thank you for having me. Yeah, as you mentioned, I've had the opportunity to do quite a bit of travel, but the before and after that part of my journey has been all within everything data analytics. I you know, going through the education system, I was always into mathematics, but I knew that I wanted to apply my mathematical background in some way, shape or form to something more than just equations on a piece of paper.
Speaker 2:When I was leaving college, it was kind of that choice between going to develop the next mobile app that was going to be something like Angry Birds or go into a new trendy topic of the area of data analytics. And what I loved about working with data is that it is a perfect mix of working with some really cool technology behind the scenes and that tech in the space is constantly evolving. Ai is completely changing how we think about it. But then you also get to couple it with the opportunity to work with other fellow business owners and leaders within the businesses to help them think about how they run their organization and take a lot of their gut intuition and try to put it into what can be quantitatively measured with analytics and helping somebody uncover something they didn't know about their business after decades of running. It is a really cool feeling and a rush that I'm always searching for in everything I do, continuing to consult in this space.
Speaker 1:It's kind of amazing, isn't it, that people that run businesses basically just continue to do what everybody else does and really don't understand the business at all.
Speaker 2:Yeah, and it's amazing how far somebody can get by just doing the things that work, I mean as simplest form. That is what you need to do when you first get started. But there's a point whether it's a changing environment for your business or just scaling the organization is you as a one individual can't wrangle everything in. And that's where data plays such a critical role and what I have to evangelize the companies of leveraging that data so that they can make informed decisions or have other people make the same decisions they would have made with their intuition.
Speaker 1:What's the biggest shortfall or problem with data analytics today?
Speaker 2:The biggest challenge right now is there's so much data being created.
Speaker 2:There's still kind of two parts of this.
Speaker 2:There's a lot of data being created, but it isn't easy to mash that all together to this.
Speaker 2:There's a lot of data being created, but it isn't easy to mash that all together. So just the act of getting the data into one central spot that can be trusted, governed and provide insights that's a whole area of data engineering that there's a lot of work and it's not terribly invigorating exciting work that gets people excited. It's not flashy dashboards, it's just the hard work of building pipelines. And then I think the other part of that challenge is the lack of education around what actually has to go into these analytics deployments that people business owners get often sold on fancy dashboards and out-of-box solutions. But there's a little bit of a misunderstanding of the level of investment, the level of work that has to go in to make potentially incredibly messy data useful for their teams to be able to make decisions. And I feel like most of my role as a consultant is playing a role of educator, of telling people what they can even do with their data and how to fix, help, get them unstuck out of their own problems they've created over the years.
Speaker 1:I have a similar background to you, although it's a completely different generation, taking computer science in the 60s, when we wired unit record equipment and all the rest of that happy nonsense. One of the things that intrigues me is databases became in vogue in the 60s and here we are 60 years later and we still don't have good data in databases. What the hell's going on?
Speaker 2:I think we are running into the problem of there are more ways than ever to create new pieces of data, but we've never really stopped to take stock of how do we put quality stuff in right. It's just, there's a lot of new data points out there, but there isn't much going. We're not keeping pace with how we can actually make use of it and we're not keeping pace with how we can actually make use of it.
Speaker 1:One of the things that bothers me about data and you're right, there's a bunch of data. Very little of it is accurate, and part of the reason for it, in my world, is my belief that there has to be an owner of every data element in the database. That owner is a complete cop. Nobody can touch it, nobody can change it, nobody can report out of it without their approval. Is that a good idea, bad idea, or am I smoking dope?
Speaker 2:Somewhere in between. Yeah, I mean that's. The tricky part is you need people who are accountable to data quality, data governance, and it's not a role that most people are willingly raising their hands to control. And this is the balance we find ourselves in.
Speaker 2:My time in the data industry, most of the 2010s was really around this concept of self-service analytics. We're just going to open up all the data and people can find their own answers what, what a beautiful way to get it and control and governance out of the way so we can actually move faster as a business. But then we've run into now all these problems of like people lose trust in the insights and it's bad quality data when we removed all of those controls and so like to your point of how do we create data owners that provide a level of governance without creating a dictatorship around access to that data is, I think, where we're continuing to find, like trying to find that sweet spot of it. Right, we can't we can't stifle innovation for a business that wants to move really fast, but we can't completely ignore the natural reasons why like it and all these controls need to be in place.
Speaker 1:there needs to be a balance in there somewhere so then we have the stroud strike thing where the systems come down because we don't control data yeah, yeah, or put in proper controls of like how do you slowly roll out big changes that could affect everyone, right?
Speaker 2:it's's like that is a case in point of why IT needs to exist, and it's unfortunate like being an IT professional like you only ever get called out in the news when things go terribly wrong. Is that appropriate? Is that appropriate for IT teams to only be called out when they do things wrong?
Speaker 1:Yeah.
Speaker 2:I think it's kind of the nature of the role, right. If you kind of exist to be a guardrail, I don't think you go into IT because you're looking to get high praise for continuing to govern your data so well. It just kind of comes with the territory.
Speaker 1:It's an interesting game. I took over a data processing shop in about 1973. And I was a general manager in an operating department and the president of the company said meet me on Monday morning at eight o'clock at such and such an office. I said, ok, what's that about? He said that's your new job. I said, really I'm busy. He said no, you're not, I'll see you Monday morning. And this is the guy that fired me five or six times. So I go up there.
Speaker 1:I'm from an operating department and they needed a translator, somebody who knew the operations of the business and who could translate that into language and jargon that the systems people, the data processing people, the analysts, the programmers could understand. And we did that by having operating managers responsible for the systems people working inside the systems department. And then I went to the other side of Canada and I was kind of the backup guy in a 53-store operation and they didn't like traditional IBMs, traditional computing. So they went data general, created their own database, their own teleprocessing monitor, all the rest of that stuff, that stuff. I'm running the parts business 150 million, 53 stores across 3,000 miles east-west and 2,000 miles north-south. And the system went down in September for three months and we had to run manually for three months, holy camoly. And then I came to the States and ran a software company the largest one in the construction equipment world had 450 dealers around the country in Canada, and I was a client prior to that and my problem was anytime there was a new release. And it's still a little bit of a problem, but not anywhere near what it used to be 20, 30 years ago.
Speaker 1:So the team said, okay, it's a Friday afternoon. He said we're ready to install, to send out a new update. We're going to send it out next week. I said terrific, when are you going to test it? Oh, we've already tested it, I said terrific. So how about tonight, six o'clock, friday night? I want you to put it on our computers and I'll come in Monday morning. I want you to give me an update. They couldn't even start the computer. So we had that meeting on Monday morning and they told me the story and I said did you fix it? Oh, yeah, we found it, we fixed it. I said, okay, I'll tell you what. We're going to do it again this weekend and if ever that happens again, none of you are going to have a job, and it never happened again. The dilemma is there has to be a consequence for bad behavior, and that's kind of my dilemma with data analytics. So I start from a different place. What is data analytics? What the hell is it? Is this metrics, or is it truly data analytics?
Speaker 2:There's a book for you. Yeah, and when you say truly data analytics, tell me more about what that truly data analytics means to you beyond metrics.
Speaker 1:The average transaction value of a certain business is dependent on what that's data analytics. Most people call data analytics standards of performance, metrics, dashboards, targets, that kind of thing what I call what it looks like when it's right. So, as an example, customer retention is probably the single most important measure of the financial success of an organization. What causes customer retention? That's data analytics. Is that your data analytics or is that different than what you've been thinking about it?
Speaker 2:Yeah, no, I think it's all part of it. It's where data is actually applied to inform or create a decision that wouldn't have otherwise been created. I mean, that's where data analytics actually sits right.
Speaker 1:Okay, perfect, perfect. So let's look at retention. There's a direct correlation between customer retention and distance from the serving store. Does that mean I should have stores that as soon as that retention number starts slipping in today's world? I think my retention within 50 miles is I'm going to keep customers 90% of the time. 50 to 100 miles, it goes to 80%, 100 and plus it goes to 60. Do I put in more stores?
Speaker 2:I mean that's certainly one scenario you could run. There's also other angles you can think about. Are those the customers you even want to be winning? Have we proven that from a past experiment, when we put a store in a different spot, that it's actually led to improved retention of those people or raised order value? I mean, there's a lot of different factors in. Are these the customers we actually want to go after and then run it up against the very real expense of opening up a store right? There's a lot of different components just to that piece. I love you so what are my alternatives?
Speaker 1:You mentioned segmentation. What do my demographics look like? In data analytics we've been kicked pretty hard in the last let me call it 30 years and let me put the spot in there at 1995, and bring in three things, I guess. One is internet-based shopping, One is cell phones, One is cell phones and one is commercialization of internet-based businesses. Amazon became the largest retailer in the world, Apple basically completely transformed communications and now we have no control over either. Is that good or bad? Hmm, and the pregnant pause. I need to fill the air so I'll give you a chance to think.
Speaker 1:You know, it's a really strange world. Everything goes back to two people in a commercial application. Somebody's going to buy something, somebody's going to sell something. It starts with Adam and Eve, the apple, and so, as we come forward, here comes Amazon and Bezos. And he picked books. Would anybody have believed that he would go from books to this? So there's one. Another is the Sony Walkman. That kind of led the parade on music. So we take one guy with books, another guy with music, and they come up with the Walkman and then everybody started copying them, which kind of introduced the world to Kaizen, Because every time people caught up to Sony. They put the next one out there. It started as tape, it went to disc, it went to all the rest of those steps and then it got completely transformed with. Here comes the cell phone, and Apple replaced that whole deal, and I'm using Apple as the illustration. There's a whole bunch of others.
Speaker 1:Amazon comes across, they become the largest retailer in the world. Prior to them it was Sam Walton and Walmart. Sam had a particular advantage in that he said to anybody who wanted to put product in his store he would have them be the exclusive supplier, but he wasn't going to pay them until the product was sold. In other words, you're going to put inventory in my stores and when I move it at the cash register I'll pay you. So I had the lowest operating cost of anybody and he killed everybody.
Speaker 1:Until here comes Amazon, so translate that into data analytics. Here comes Amazon, so translate that into data analytics. Data analytics is a vehicle to me that's going to change how we do business Completely. So take the car business. Are dealers going to exist in 10 years' time or is it going to be direct from the manufacturer? Things change and data analytics is, I believe, leading the parade or causing the change. And we've got generational things in here. Guys my age typically are protecting the status quo. We're change resistant, we don't want to do that kind of stuff Under 40, they say get out of my damn way, I need to change this. And those two are having trouble coexisting today. Fair comment or not.
Speaker 2:Yeah, I mean, I definitely I agree with the trouble coexisting. There's a lot of the like. Everything you've described are basically just like innovation loops, right, Like these are. These are things you can't really forecast out. There have always been the rise and fall of titans and and monopolies or monopolistic type behaviors, but I think where, where I kind of like philosophically stand with it is like there's this innovation cycle that is always going to breed new innovations and like the benefit to smaller outfits is that you can move faster with these innovation waves as they roll along, right, like data analytics, reporting, dashboards. They've they've had their run, and now we move into this like AI innovation.
Speaker 2:Now, what is that? What is the final outcome of this AI wave going to become? Does chat GPT and open AI like become the winner in this race, or is this just the next evolution of? Do we even need a website to go buy our books from anymore, or could we just chat it into its existence with some kind of virtual assistant, right? All of these will just continue to evolve. Every single business model you can kind of rattle off, but we're not even quite sure what that final state is going to look like.
Speaker 1:Exactly, exactly. And it becomes really difficult Again if we go back to transaction-based businesses, where everything revolves around a transaction, and if data analytics is required to maintain a position, to protect a position, if not grow a position, then we have to be very clever in the algorithms we create to evaluate and analyze. And that seems to be the new area. You know, think about games, gaming. That's really exciting, it's sexy. Young people, here we go and off you go. Data analytics and commercial applications ain't sexy, it's grunt work, like you said earlier. I mean, it's really dirty grunt work. There's no shortcut in this stuff. What?
Speaker 2:in the hell are you doing in that, All of all of the grunt work? I think I think what we like, what we are there's like the necessary work of that needs to be brought into. Like foundationally set up any kind of data analytics outfits, right, Like different types of questions. I mean, what I love is like when you think about just your example of opening up new stores, right, Part of that is just helping people ask maybe different questions or more data, informed questions or different avenues for it, and kind of see how different trends within how people operate businesses they can think about it differently.
Speaker 2:But fundamentally underneath that, data engineering, data integration, getting data into a spot where you can actually make informed decision, like where that data comes from, how that data is stored even the concept of a transaction within a business might feel very different now that we may be just moving more towards like a chat, log-based interaction with the world, All of that work still has to be organized in some way, shape or form and like that is the dirty work that we're doing behind the scenes. Yes, from a marketing perspective, we advertise the metrics and the dashboards and the flashy stuff. I mean this is what people can grasp onto as a thing I can touch and hold. But the vast majority of the work behind the scenes is working through the nuances of data quality, where the data is coming from, and automating that into something centrally trustworthy data source.
Speaker 1:How far behind do you think we are in application today to the technological advances? Five years, 10 years, 20 years, 40 years?
Speaker 2:Yeah, I mean I've never had to think about measuring in terms of years, but yeah, I mean I feel like probably closer to the five-year, 10-year mark of what you can actually do if you've got a full-fledged data system with all the bells and whistles of what is actually coming out today versus what the realities of most businesses are. Most businesses are. It's probably close to the range of 10 years in the past. Right, most of the organizations that I consult with are still running on Excel, whether they want to admit that or not. Their primary systems are people typing in numbers in the right spot and it crumbles very quickly when those people are no longer in those positions.
Speaker 1:And you know again all of this transformation stuff. You know this kind of discussion. How often do you have this kind of discussion?
Speaker 2:The deeper type of philosophical purpose of data analytics? Not often enough.
Speaker 1:Okay, I'll give you a couple examples. There was a YouTube film put out by BMW called BMW Augmented Reality. It was a technician walking up to the front of a car whose hood was open, reached over to the top of his toolbox and picked up a pair of glasses, put them on, clicked a button on the earpiece and the engine compartment lit up and the instructions of what he was asked to do came through his earpiece Step one, remove such and such. And the picture changed, showing what it was and it was moving away. That's now 31 years old. If you and I got in a plane and we went to visit a hundred dealers across whatever geography you want to look at, how many do you think we'd find that are doing that?
Speaker 2:Probably none.
Speaker 1:So that's a 30 year gap? Yep, so that's a 30-year gap, Yep. So Excel. Prior to Excel this is probably before your time, but prior to Excel, it was QuattroPro. Prior to QuattroPro, it was another one. Prior to Word, it was Paperclip, the very original one. Why are we still using these things?
Speaker 2:Yeah, I think using a keyboard. Yeah, because change is hard, that's what it is. Big time.
Speaker 1:Big time. It's one of the things that I used to do in a classroom. I said okay, you think change is hard at work? Go home and tell your wife you're going to sleep on the other side of the bed and tell me what the couch is like tomorrow. I like that, it's true. It's all kinds of things. So data analytics is critical to the success going forward. But then here's another one that becomes interesting forward. But then here's another one that becomes interesting One of our contributors.
Speaker 1:We have about 65, 70 people that write blogs and do different things for us. I call them thought leaders, experienced executives or revolutionary reformers. One of them says to me by 2030, 50% of the workforce will not have the skills to be employable. His name is Ed Gordon. He's got two PhDs. He used to teach at Northwestern. So let's say he's wrong. By 10 years, it's 2040, not 2030.
Speaker 1:How does America survive when 50% of the working population, working age population, is paid for by the government? We've spent hundreds of trillions of dollars on technology, nothing on sociology. So you know data analytics. I love the subject. It's something that's very seriously top of mind for me as an educator. Something that's very seriously top of mind for me as an educator, lifelong learning has become something that's almost absolutely mandatory.
Speaker 1:Yet adult education is something that the educators of the world don't really think about. They think about 25 years old and down in a school, take a degree, get associates, whatever the heck it is. I'm talking about 18-year-old to 70-year-old who are working full-time, typically married, have children. They're tired and companies don't want to spend a lot of money training them. They look at them as tools in a toolbox. I'll hire a need, I'll train you for our application, our system, and then you're on your own. If I need another tool, I'll get rid of you and bring somebody else in, rather than developing you Again. Data analytics is right at the root of all of that. How do we get people? And, just like you said, change is tough, learning is hard.
Speaker 2:Staying current is probably the most difficult thing to do. Yeah, yeah, and unfortunately, I mean, the way I kind of think about it is it's you kind of find it now, with the job market too, there's never been more incentive for people who may be without a job to have to find some way to skill up or be relevant or whatever you want to call it. But, like I, I I imagine that this problem has always like persisted since the beginning of the industrial age. Right, I mean, everyone kind of gets to the point of aging out of, maybe, what they were originally vocationally caught to, taught to do, and it's either you have whatever, either circumstance or drive to change, change that to retool, or maybe, I mean, this is fundamentally the reason why organizations still hold on to their excel spreadsheets. Right, it's just easier to hold on to what's known. It's comfortable, it's comfortable, it's a good way to put it.
Speaker 1:And that really it's the rocking chair type of syndrome. Some of us are cursed that we're never satisfied. You can always do it better. And I say cursed because it never ends. So let me split population into 10%. We don't need to worry about. They're going to survive. They're curious, they're ambition, they're driven, they work. 70% are struggling. If anything goes awry in their life, they're screwed. And work is a four-letter word to them. They don't think they have a lot of choice. They've never had much choice. And then we got 20% in the middle, that don-letter word to them. They don't think they have a lot of choice. They've never had much choice. And then we got 20% in the middle that don't know where to go. And my concern is nobody's giving them a model of choices, which data analytics should be able to do. So the trick for me is what do you want to do and why do you want to do it? What are you passionate about? And I don't think we think about that much.
Speaker 1:When I'm hiring you or every year, when we have a performance review, which I think management or leadership does a terrible job at, I should be asking you how can I help you get better? Because I want people to think about getting better. I want people to have that Japanese syndrome of Kaizen Every day we'll do something a little bit better. Yep, we don't have that in North America.
Speaker 2:No, no. And as a people manager too, I have found that it is surprising Like I always enjoy working with people that can not articulate what they're trying to do, but at least have some semblance of like. I have found that it is surprise Like I always enjoy working with people that can, I mean, not articulate what they're trying to do, but at least have some semblance of like. I have a direction, right, I know I want to go North. I don't know what lies that direction, but and it has been amazing to me that through all my time managing people, that there's probably the majority of people are actually like looking for guidance on answering that own question of like what, what do I want to do? Right, it's almost like the I don't know what kind of North American or Western culture is more around. Uh, the company will tell you what you need to do, and then I think that is like really dangerous and scary for a lot of people to like have to think outside of that box.
Speaker 1:It's how you're raised. We're taught to be obedient. Our parents look left and right, don't put your hand on the stove. We're trying to protect you. Then you go to school. Teachers have to.
Speaker 1:Here's cursive writing. We don't teach that anymore. But here's arithmetic adding, subtracting, all the rest. And then we get out into the workforce and here's your job. I'm going to show you how to do it, then I'm going to watch you do it, I'll give you some help and then I want you to get better at it. Do it faster, make fewer mistakes and everything's cool. Nobody asks you to or gives you the opportunity to say why the hell do you do it that way? And there's a flaw that analytics is going to expose and not everybody's in for that game, and I don't know how societies handle that. Honestly, look, it's a real, serious conundrum. Robotics is going to replace almost every manual skill that is there. That's why Ed Gordon's 50% of the workforce is probably going to be true. A friend of mine does. He's got a couple of PhDs from MIT. He has about 17 patents pending where he moves the cursor on your screen with his eyeball.
Speaker 1:We've had voice recognition in operating rooms since the 90s. Why do I need a keyboard. And to your point, why do I need Amazon? Just talk to the phone and the story have the phone do it. Point, why do I need amazon? Just talk to the phone and the story have the phone do it. Yep, and they'll pick, whether it's amazon or walmart or nvidia or whatever that you know, they'll because we'll give them an algorithm or somebody will give them an algorithm. We'll get there. Yep, very, very different place, isn't it?
Speaker 2:it is it is and it's, there's a, there's a lot like we just don't, like, we can't even solve for right. It's the automation, like we don't know how far that goes, say, have more of just evolved or pivoted, like whatever word you want to use, but the the nature of how humans fit into the broader picture, it just is different. Nobody knows the answers until we just like start seeing how they change and evolve. Right, we don't need phone operators anymore, pulling plugs, uh, but I'm sure they have to be like retooled or reskilled or have the wherewithal to like find, find the next role beyond being the telephone operator. It's, it's, we've evolved you know it's.
Speaker 1:It actually goes back to serfdom. You know where you got farmers that are being paid. Let's say we own a vineyard. There's no way that we have enough family members or employees to be able to keep on the payroll, except for harvest, and so we bring in bulk labor to do a specific job and then they're gone. Don't need them anymore. Agriculture 50% of the price of the food that you pay for at the grocery store comes from transportation. Why don't we have the farm right beside the damn store? 50% of the electricity is lost over the transmission lines. Why haven't we solved that problem? And again, all of it leads back to data, and we're only starting to come to the realization. Your generation is probably going to be the one to get there. Mine was introduced to it, but we didn't know what the hell to do about it, do you?
Speaker 2:Not yet Got to let it simmer for a little bit.
Speaker 2:Uh, yeah, I mean I that's the exciting part is like it's getting easier to like identify, identify these trends. I I think I think what's cool about this conversation with you is like the the deeper societal problems of like the advancements that we're trying to do. I think so much of what how we try to apply data analytics and like my day-to-day role right Is to like figure out how do we min max on maximize profits, minimize expenses, right. But there's a lot of uh. I think like part of what you're talking about, too is the foundational challenges of like co-locating farms and like uh eliminating loss and like uh eliminating loss and electricity, right, those are those are fascinating problems that can absolutely be solved by data. But it's the level of an investment from people's interest in trying to solve that, but also the monetary investment to like get the answer. I mean you have to have some kind of uh driven maybe like a mission driven person, or a lot of investment coming from somebody who is passionate about solving that to like get to the core, core piece of that.
Speaker 1:And the interesting thing about that is, the people that haven't figured out are the ones that have maybe 90% of the income. Yeah, and they do not want to change anything. Baby, they'll buy you. That's why we still have gasoline or diesel engines rather than natural gas. Yep, that's the arbitrage, right? Yeah yeah. So here comes the World Economic Forum. Fantastic, we get all of these smart people that know better than you and I how we should live our lives. Yep, what industries do you serve mostly?
Speaker 2:So we serve a lot, but some of the ones that we have some of our most success within is probably the interesting one I'll start with is school districts.
Speaker 1:Perfect, and what?
Speaker 2:do you do with school districts, academies, private schools, charter schools?
Speaker 2:Basically, what happens within district, much like most organizations, is that they have way too many applications that they're trying to manage for student performance, student academics, right, you've got behavior, events, attendance, grades, all of that. But it is incredibly difficult to make a holistic picture about how a student and the student body at large is performing as they move through that educational system. A lot of different components right there. There's a lot of human interactions of like how you're instructing these students, but for a school district, one of the toughest resources is time. And now you're asking busy district staff, you're asking busy teachers to instruct humans on the best way that they can lead to a successful student outcome a going the wrong direction or be able to focus in attention on a specific class or a specific student to help instruct them, to help lead to a more successful outcome for them. So we're building all the custom integrations behind the scenes to make a holistic picture for the student population so that people can make smarter decisions on how that school district is being run.
Speaker 1:How big is your geographic reach? How far away are the school districts you're working with?
Speaker 2:Yeah, we work across all of the United States, so, coast to coast, we have school districts in all sorts of different states that we can work with. Fundamentally, we connect with 80-90% of the applications that these school districts are using. The interesting part of school districts, unlike the for-profit enterprises I usually partner with, is they're all trying to solve the same problem and they have no reason not to share in that wealth of information with their quote unquote competitors. Right, everyone is trying to achieve a better student experience and I think that's what's so fascinating when I can contrast it against our other industries that we work with is there's a lot of like information sharing and best practices that we're solving the integration challenge once that we can share with all, which is super fun.
Speaker 1:So charter schools and traditional public education are cooperating, collaborating or competing.
Speaker 2:Depends on which context, but in the, in the context of Luke is a comment we never use.
Speaker 1:The word. Depends with people. My age.
Speaker 1:Oh, fair enough, I just I wanted to put it. You know, it's really an interesting conundrum the Napierville, illinois, north of Chicago school district. Years ago they published a book. They were part of a book that proved that physical education in the morning and after lunch had dramatic impact on the learning capacity of the students for the first hour, two hours of time. Yet as I go across the country and look at school districts, physical education has disappeared, except for after hours school sports. Yeah, why the hell have we done that?
Speaker 2:Yeah, I can't quite explain that. I don't know if we're trying to pack more into the agenda or whatever. These are the things that are going to increasingly start to bug me, as I'm less than a month away from my oldest joining kindergarten. So I'm re-entering education with a whole new perspective.
Speaker 1:Okay, first question is are you familiar with the Khan Academy? No K, h, h.
Speaker 2:N. Oh, okay yeah, khan Academy Okay.
Speaker 1:Yes, get your kids right now Started. Uh, and what's? He goes from preschool to grade 12, all the way through.
Speaker 1:Hmm, my daughter's got a master's in education. She's the curriculum director for her school district for AVID, and if the school your kids go to doesn't have AVID, you want to get with it. That stands for advancement via individual determination, yep, and you're going to have a lot of fun and pay attention to what the kids do, and they do not get summer holidays. They're also going to be taking classes all summer, starting at five, because you're going to find they enjoy it. Yeah. So culturally, let's take a different switch.
Speaker 1:You know I spent a lot of time working in Asia you mentioned. Asia is a very exciting place to travel. They're fanatical about their kids being educated. The kids work 10 hours a day and they go to school on Saturday. Our kids go to school five hours a day, five days a week and that's the end of it. Don't fall into that trap. Every single thing and education is a beautiful illustration of we've dumbed down education to the point of the lowest common denominator. The highest SAT scores occurred in what year, do you know? I don't 1963. And the SAT exam has been redesigned three times since in order to hold the score where it is, in other words, downwards. So now it's at the point that the university are saying, well, what the heck do I want to use that for it doesn't matter to anything. Yeah, every single element of society. Data analytics has the solution. Should we find the people that are interested in discovering them?
Speaker 2:Solving it, yep, yep. So you're going to have fun, my friend. That I know already.
Speaker 1:So that's why we call these things candid conversations. I bet you didn't expect this discussion, did you?
Speaker 2:I did not. I did not. This is super fun.
Speaker 1:Good, so that means you're interested in doing another one.
Speaker 2:Yeah, why not?
Speaker 1:These are the questions. I think we should be more thinking about 850 word blog that we can publish that will go beside this to start people down the path of thinking about data analytics and how data is so important in their lives. Similar path that's going on, luke, is in cybersecurity. People don't understand how vulnerable they are, nor do they understand how important data is. Yeah, and so we're transforming business, like COVID did with working from home, which is going to completely revolutionize what centered downtowns look like, what businesses look like, what office buildings look like if they exist in the future. It's kind of exciting time to be. I'm sorry I'm not going to be around long enough to enjoy all of it.
Speaker 2:And and uh, just a random one-off have you ever traveled to Singapore in your travels? I've taught in Singapore many times. Okay, yeah, just uh. Uh, I just got back from singapore for my second time, uh, april earlier this year, and, like, as you're talking about, uh, like, like, rethinking how downtowns are built and how communities come together. I'm not saying that their model is, uh, the model, but it's just fascinating to compare and contrast. Somebody who's trying to maybe take a stab with a blank slate right, they get to play sim city in the real world. Uh, maybe take a stab with a blank slate right, they get to play SimCity in the real world, but take a stab at rethinking how we organize cities.
Speaker 1:Well, the family that created Singapore broke away from Indonesia, Yep, and the current leadership is the first time since the founding father created Singapore that it's not a family member running the place. It's going to be, and if you go back there again, have the pepper crab down at the harbor with a nice bottle of really cold white wine. It's some of the best. I did that. Yeah, of course it's fantastic. That's one of the dilemmas that we have in in life and that people don't have enough um time I'm going to say that to do what you and your wife did traveling, because it opens your mind a little bit, doesn't it?
Speaker 2:Oh, absolutely, completely, completely changed how I view everything in my world. Yeah, perspective.
Speaker 1:Perspective. Okay, so let's let's put a ribbon around this. What? Give me some kind of a a close from here? What? What did you get out of this and what should we get out of it?
Speaker 2:Yeah, great question. So, uh, man, I I really loved how the conversation today, uh, I mean, I think the thing for me I'm going to even go higher, higher level here is your questions about how data analytics are going to start playing a much bigger macro impact on how people think about jobs and how we just exist in this changing world is something I wasn't expecting coming into this conversation and it's got me thinking down a totally different path. So that's probably my biggest takeaway from this conversation. Is that a good thing? It is? It is. Yeah, I mean, I jump from meeting to meeting and activity to activity, so maybe I think people in general could use a little bit more thinking time. Yep, that's what we seem to be missing.
Speaker 1:Yeah, I agree with that completely, yeah and maybe, maybe that's my.
Speaker 2:My one, like uh, leave off with the audience is uh yeah, go go for a walk.
Speaker 1:Think about something it's a good way to do it too go for a walk. You know, there's there's a I don't remember the, the way to phrase this or present it properly but you need to get to a place where your head is completely clean. There's nothing there which is when you can then think adequately. But look, I hope everybody listening has got something from this. You're going to play a very important role in your life, and I'm sure your wife and your children are going to play a very important role in your life. I'm sure your wife and your children are going to have a hell of a good time. Thank you for being here and I look forward to having you with me again at another Candidate Conversation To the audience. Thank you, mahalo. We'll see you the next time.
