For legacy architectures, infrastructures, and technology, the business logic needs to get extracted, cleaned up, and organized in a more relevant and future-looking architecture. Mike Gearhardt, CEO of 5280 Software, helps small businesses, startups and medium-sized companies in the Denver area in maturing their technology. Mike is also the CTO of Fathym which focuses in the IoT space by helping industrial companies learn from their data and predict things they need in order to attain maximum efficiency.

Mike Gearhardt On IoT Data Extraction And Learning Solutions Applied

We’re fortunate to be with Mike Gearhardt. He’s the CEO of 5280 Software and he’s currently the CTO of Fathym. We’re going to dig into the IoT space and a couple of other topics, so this is going to be quite fun. Mike, I appreciate you taking the time.

Thank you for having me.

For the folks out there going like, “What the heck is 5280? What is Fathym?” tell me a little bit about the businesses and who you serve.

I founded 5280 Software to focus on what I had been doing for the last decade of my career, which is helping companies take legacy architectures and infrastructures in technology and get the business logic extracted, cleaned up and in a much more relevant and future-looking architecture. For us, we do a lot of work in the ski industry because we’ve seen a lot of problems that they’ve had there. We also do a lot of work with small businesses, startups, and medium-sized businesses in the Denver area who need help in maturing their technology. That’s how we originally met with Fathym. I was on the basis of helping them take the legacy of startup workflows that they have had for the last years and the pivots in their business and helping them mature their technology stack and in exploring that relationship, it eventually turned into something more. Then taking the CTO role here, we’re focused on helping companies with legacy architecture situation. Fathym does a little bit of the same thing but focused in the IoT space and helping industrial companies get the data and devices and everything that they have exposed in a way that lets them learn from their data and predict the things that they need to be a more efficient and operational business.

For the lay person, what is a legacy architecture example?

When I say legacy, for me, it doesn’t necessarily mean old. It’s something that just had a lot of different hands on it. When I think legacy, I’m thinking more of my legacy as a business. I, over the last number of years as a business executive, have learned a lot of things about how to operate in our space, how to do different things and interact with our customers, but it’s been built by a dozen different people with a dozen different attitudes and has become something that handcuffs you and prevents you from innovating in your industry, and being somebody who’s bringing new tools to your customers because you can’t because the way your code is put together. When I talk about legacy architecture, it’s about the people who have all this valuable knowledge locked away in a system that they can’t access.

Fathym Data Extraction: Legacy doesn’t necessarily mean old. It’s something that just had a lot of different hands on it.

For you, you had a rather unique path to the skills that you possess. Walk us through that deal.

For me, technology started as a kid. My Dad was an EE and somewhere in his career realized, “I think that software is the future, not necessarily hardware.” He made the move and that proved well in his career as he made it through several different acquisitions in that line of work, living through on software. Seeing him make that transition and having these books start to show up in our house for C++, Objective-C, and Visual Basic and all these different programming languages, I was at an age thirteen or fourteen where I wanted more knowledge. I was in a mindset of learning and so I started picking those books up and said, “If I do that I can make the computer do this.” Through middle school and high school, I programmed little calculator games for the TI83. I had three little programs for the computer and eventually thought that I should be in EE even though I was doing all this software stuff all through high school. I figured I follow in my dad’s footsteps and spend a semester in college realizing that, at best, I would have liked to be an electrician and not so much an EE.

From there, I struggled to find what it was that I wanted to do. I tried construction management in my second year of school. I was doing general business studies and got a job. Jeff Yeager, a good friend of mine now and actually my accountant, changed careers in his life, gave me an opportunity to work for his company and be the tech resource supporting a small ecommerce website. From there I tried for a year to do full-time school, full-time work, but realize that, “This is what I want to do. Why try to do both of these?” and just dove in and worked my way through a few small startups in the Fort Collins, North Colorado area building e-commerce software. I got my name on a patent working for a company called Deal For It where we built a flash-based flex-based action script-based technology that did walk-on video negotiations for eBay sellers. It would come on and you’d make an offer and the video would come out and tell you, “That’s a terrible offer. Let me take that to the boss and come back with counter offers,” and try to negotiate and create a fun experience. From there, over the next few years, I grew out of just being the developer and embraced the leadership that I wanted to give in the area, progressed up to architecture positions and into the roles that I’m in now as a CEO and CTO of 5280 and Fathym.

You’ve had 5280 for a number of years.

Three years since September of 2014.

There’s that process and many go through it. You make up your mind that you’re going to start your company. What was that thought process and discussion when you go home? Were you married at the time?

It was not as surprising for her. She’s been an entrepreneur or wanted to be an entrepreneur for a long time in my life. In the end, for me, it boiled down to a decision about being happy and quality of life. An opportunity came knocking from some of the relationships I had in ski from previous work. Going home to my wife, she was six, seven months pregnant at the time, we were about to close on our first house, two months from then, I came home and said, “I’ve got an opportunity to start our own thing, to provide ourselves.” At the time for that contract, it was only a three-month guarantee of work. It wasn’t like we had closed a full year, two years-worth of contract or anything, but the opportunity was there. She understood that I wasn’t exactly happy any longer where we were working before.

That, mixed with not letting that opportunity slip, made it for us a little bit easier than maybe it is for some people. There were certainly struggles but I took the leap and started the business. You learn a lot about the value of a paycheck. The knowledge that every two weeks, every four weeks, I’ve got this money coming in and when you’re running your own business and you’re closing these contracts, that you’re not getting the money as smoothly. There were times when it was pretty low and it’s like, “I guess we’re not buying groceries right now. The check comes in in four days and we’ll be okay.” If you’re looking to take that leap but you can do ahead of time, to prepare yourself would be good. For us, it’s about not letting those opportunities in life pass you by because they don’t always come back around.

It's about not letting those opportunities in life pass you by because they don't always come back around.

Click To Tweet

We could probably go miles down that road if they don’t always come back around. With the entrepreneur, there’s a certain level of courage that comes. You’ve got to have faith in yourself and you’ve got to be willing to risk because there’s always that opportunity to sit as an employee with the theory that you’re at less risk. That’s more theory. If you’re not invaluable, then you’re just an expense and they’ll let you go. You got 5280 up and running, and for the folks going. “I’m still a bit lost. I don’t know what’s your ideal client or prototypical client look like?” what problems do they have and what do you guys do to solve their problem?

The ski industry, for me, is the perfect example. For fifteen plus years, they’ve been handcuffed by the point of sale systems that they chose typically fourteen, fifteen years ago. These point of sale systems are embedded across everything that they do, which was the way that we built technology back when these systems were built. It scans your lift tickets, it sells your lift tickets, it sells you food on the resort, it books your hotel room, it gives you access to the gates. These systems are integrated at all levels. The problem for that industry is that the investment by those companies hasn’t been there to keep their technology relevant so that the ski industry can leverage new technology to create new innovative customer experiences that help them address a bit of the customer-base issue that they’re having.

What that’s led to is a situation where it has spun up a nice ecosystem of vendors who are trying their hardest to provide these innovative solutions. There are people that are passionate about ski. Flaik, one of the customers of ours, do ski school software. They have a little device that’s IoT. They attach it to the student on the mountain and it tracks where they are. The instructors use that to know if a kid has been lost. The parents use it at the end of the day to get a report on the fun that their kid had. Flaik gets to use this data in a lot of cool ways where they find themselves with an ability to make it an integrated resort experience. They sit on the outside of the business and in the end, while they’re creating very unique customer value, they’re adding expense on the back end by creating processes of manual reconciliation.

Nobody’s afraid of that. It’s just a part of the situation they’re in. That’s where we look to step in. Let’s take these people who have an inability to connect and innovate and let’s help this industry create the right pieces that allow resorts to maintain security and control over their data and partnerships, but allow them to let people like Flaik and SKIDATA and these other vendors in the space be more connected so that everybody gets more value. The customer gets more value, the resorts get more intelligence because Flaik is developing intelligence tools around ski school, and the resorts no longer have to try and build those tools themselves.

Many of us heard IoT. We had this vision of the refrigerator and the thermostat and a couple of other things. If you can, expand a little bit on IoT from your perspective.

I touched on before the old mentality of software was I need to build something that does everything. IoT is the advent of sharing of data, the open exchange of data between organizations. It’s nothing new. It’s been going on for a very long time, but more in the industrial space as people focus on how do we take hardware devices and connect them up to the cloud so that we can take these embedded systems and gain more value out of them and connect them to the world. For me, initially, what IoT was is that industrial focus. The reason we’ve all heard more about it recently is we start to see that bleed into our consumer electronics. They have Alexa and Google Home apps and our smart refrigerators or our coffee machines that we can control from a cell phone and things like that.

In the end, that’s what IoT is. It’s about devices. It’s about things being able to communicate with each other. In the industry, there’s a struggle to find ways to bridge the different gaps of how people are trying to achieve that. That’s part of what we look to solve, not that we’re going to create the standard, but we look at it from a perspective of we don’t want to create the standard. We want to be able to work with all of these different things happening because that is what IoT is, the open exchange of data so that we can all do more together.

You see it on your phone. Will you allow so and so to track where you are? That’s a generation piece, that’s first generation thought. The question is what if there’s 4,200 people doing the same thing, same place, same time of day, what data provides you? We were talking a bit about the ski space and the data. What do you see the data collection doing for the consumer and the provider?

There are definitely consumer benefits to IoT and having this data. The ski industry is unique. Sometimes when we think IoT, we think about the new connected devices that are being built, but a lot of companies have had IoT engagements going on in their resorts. When you think about a resort, you’re scanning lift tickets, so you’ve got devices everywhere, you’ve got gates. For years, they’ve just been collecting that data into a database and not gaining valuable insights into what they can do with that data, both to improve operational efficiencies and to improve the guest experience. When we look at IoT behind firing the ski resort industry, that’s where we like to book to start. It’s like with our partner Flaik, as we look to expose ways to know where people are on the mountain so that you can share, “They’re near this restaurant because they just went up this lift.”

Fathym Data Extraction: By using IoT and understanding where people are, what their likes and interests are, you can drive an effective experience that’s catered to those users.

We can share with them a coupon to try and drive those sales into other places and the consumer experience is better because they are receiving those discounts for the experience that they’re on. We also find, outside of the monetary experience, people are actually looking for physical experiences. By using the right information that’s already locked away in their legacy, you might be able to push people to other experiences on the mountain that help them understand there’s more to do up here than just ski. The big part of helping the ski resorts is helping people see the other opportunities. By using IoT and understanding where people are, what their likes and interests are, you can drive an effective experience that’s catered to those users.

I think of some of my experiences where I thought I was going down the proper slope and I found out that I wasn’t. Then you get to learn some new skills. In my case, I grew up on the slopes a lot, but I think about the ability, like it will say, “Traffic jam on our phone at this location. Choose an alternate route.” I could see and some of the crowded mountain spaces where there’s a couple of runs that people aren’t on.

Those are the key things for your users who are always on the mountain. They can see that, “I shouldn’t go there, I’m going to hop over two peaks and I’m going to go ride here because nobody’s back in that bowl.” Those are the absolute values that the consumer can start to get from those things. For the resort, their mentality is how do we shift this kluge of people in this backed up lift over to these other places so that they are getting a better experience and their experience isn’t waiting in lines at lifts? It’s like, “You’re a blue skier. We see you do mostly blue runs. If you get to this part of the mountain, nobody’s over there and you’re going to have a better experience.” That’s where the operational side can start to learn things about efficiently working on the mountain.

I could see that some of the areas where you go this particular run got more pressure than typical. We’re going to have to take and grim differently if they’ve got some level of automation on their snow guns. The end beneficiary is more than one. It’s family memory, it’s the ability of the resort to say, “There’s a reason why we price our lift tickets this way because we deliver this memorable experience and we’re interested in your memorable experience.” We talked a bit about machine learning. On these data sets, how do you see machine learning and “artificial intelligence” massaging or working with data going?

It’s a unique situation with every company you work with because everybody’s trying to learn something different from their data. A lot of times customers don’t know what it is that they are looking to learn from their data and so we have to back into solutions that allow us to do that. From a Fathym perspective, it’s the product that we build, and offer is a streamlined way to start managing these very robust IoT solutions. A good example is Fathym owns a subsidiary called Weathercloud.

These are the devices that you affix to vehicles.

What Fathym does is provide the workflow and infrastructure to facilitate companies who don’t have that knowledge. When Fathym purchased Weathercloud as a wholly owned subsidiary, it was about taking our Fathym technology and having a production use case for it. We connect these devices via IoT up to the cloud via an ingest process that we then, from the Fathym side, take control of. Our product is focused on creating a point-and-click user interface that allows people to take these different streams of data to change and manipulate them to apply business rules to them. A good example in the Weathercloud world, if you’re taking a temperature and that temperature is over 160 degrees, you can pretty well guarantee it’s a bad piece of data because it shouldn’t be pulling temperatures over 160 degrees.

We do a lot of data cleansing, preparation to get the data into the state it needs to be in to execute the machine learning that’s possible. The number one problem in machine learning is getting the right data into the right form to support at high performance the things that you want to do. A first step of where we need to go with machine learning is understanding these good workflows to help companies not have to hire a team of men and the tech people that we have, but to be able to have the tools necessary to get their data into the state it needs to be so that they can connect with the right data scientists. Fathym is never going to have all the right data scientists to support every piece of machine learning and predictive analytics that we want to do. It’s about...