Hey channel insiders, welcome back to channel insider partner POV. I'm your host Katie Bavoso and my guest today is Hans Mai, president and founder of data 41, a value added reseller whose evolution building out an independent software arm of the business includes several stories about how AI can truly help humans. Hans explains how data 41 found a special niche in the life sciences market and how his company plays a part in matching children with care and patients with the organ donations they need.
Welcome Hans. Thank you so much for joining me today. >> Yeah, thank you for having me. It's a it's a really nice opportunity uh have an opportunity to talk about what what we do here at Data 41. >> Yeah, I'm particularly excited. This is our second conversation in the last about five, seven days, something like that. And I've been thinking about this ever since we first virtually met because your story is incredible for so many reasons. So I want to start all the way back at the beginning in how the company was founded.
So I'm going to summarize a few key points because I know that you started in 2006 and essentially you wanted to start your own business as a way to basically call your own shots. It sounds like you were a real road warrior and you no longer wanted to do that for somebody else. You wanted to invest in yourself and your dreams. So, take me back to 2006 where your mind was and what you wanted data 41 to be at that time and what it's evolved into now. >> Yeah, it's it's it's quite an evolution.
I mean back in 2006 I I'd put together a uh a business plan for the better part of a year and looking at market opportunity and analytics and it wasn't called analytics back then was you know one of those hot topics and decided to kind of go down that path and it was a lot of personal pressure that kind of forced me into okay how do I actually analyze my career and how do I analyze what I want to do and you I had a young family at home three daughters a wife who's also professional a lot of stress on on her when I had to travel a not.
And so we just kind of came to this decision. Let's invest in ourselves. Let's you've always wanted to go out and run a company, you know, whether you got promoted into it or or you started one. Let's give it a shot and see what you can do. So I started in a spare bedroom in the house, 2006. Partnered up with a small software company in Westville, Massachusetts, and we're reseller partner, professional services, so on and so forth. Had one employee.
And then uh within about a little less than 12 months, they're acquired by a larger software company out of Burlington, Massachusetts. And so we go from being, you know, a a minnow in a small pond to a minnow in a in a fairly large lake. And then about 12 months after that, that company was acquired by IBM. And and then all of a sudden, we're a minnow in the ocean. And uh and that was the first, you know, two two and a half years of me starting the company.
So it's it's it's been quite the evolution. And you know when you put together a business plan everything is like perfectly laid out and perfectly drawn out and you're going to execute per perfectly and basically within the first month that business plan was obsolete and had to make adjustments and again a maturation that comes with it as well and so it's been a a pretty interesting journey a lot of fun a lot of high highs a lot of low lows but uh it's something I wouldn't trade for anything >> so talk to me about where you are with data 41 right now because I know that you were primarily a a var a a value added reseller, but you've definitely had a shift in the last two years.
Walk me through what your priorities are now and your ultimate goals as a business. >> Yeah, and you know, I probably have to kind of recap, you know, the uh the journey a little bit more as far as how we got here and and some of the things that happened along the way because I think it it really adds a lot of context as to why you have to change and if you just look at the industry and you look at some of the macroeconomic factors that come into play as well.
So you know when we were starting I started out as a reseller but you know we were more professional servicesoriented. So in order to grow a company especially topline revenue and uh with you know the software resale being a small part of your business you have to actually add headcount and you've got headcount you know very experienced people that actually provide consulting services for clients. And then, you know, once you you get to a point where you've you've you've got the backlog that is uh needed in order to produce the revenue to support the operating cost, you've got to add another headcount if you want to grow topline revenue.
And that that was really the model. And we had the software VAR aspect of it, uh the resale aspect of it, the gross margin you get from that as a fundamental piece of of the game as well. So, we go through two acquisitions that really changed the dynamics of who we are as a company. And you go from okay goodness you know we're you know working with a small software company and and we actually had influence to you know going through the uh the entire ecosystem change into IBM and not really having a voice or influence and okay how do we actually survive number one and then the second part is how do we actually thrive in this environment.
So you have to make adjustments all the while the industry is also changing. So the industry is going from okay great we started out you know focused on the office of finance you know for budgeting planning forecasting automating that kind of the golden era of of that type of solution and then those acquisitions take place and we have to expand our footprint. So we're still a VA still a reseller but with a widened solution set and then trying to add folks again in order to grow topline revenue so you can cover cost you know everything you know fundamental to a business.
So simultaneous to that big data starts coming in that's part of the conversation. So everybody's shifting their attention to what is big data and analytics so on and so forth. Then all of a sudden after that cloud is the next big topic coming in. And so you start okay well how do we adjust and adapt the company to be able to take advantage of this but also position ourselves to be able to you know for for some longevity. And then you know lo and behold here we are you know probably 2 3 years ago AI has really consumed the conversation right it's top of mind for everybody and what it's done is that it's offered us an opportunity to look at what the market wants what is is really hot and what is actually exciting about the market and what can fundamentally change the way people go about business. and we found an opportunity to be able through a project to take it, commercialize it and take part of our business and transform ourselves into an ISV.
So, an independent software vendor instead. So, becoming a software company. So, our old model is still in place. We're not abandoning that. We've got a long history and a lot of great clients and relationships. But this new model is based on AI and it's kind of given us a different way to look at the business and a different opportunity to produce revenue. But more importantly, what type of outcome you know can we produce you know for our clients based on their needs. >> So going through this conversation of evolution you mentioned a project where it kind of took you on that journey to becoming an ISB.
That was not necessarily the plan for you but you got there. I know that you hired your first AI engineer two years ago almost like to the day uh this month that we're recording in right now. Talk to me about the original plan building out an AI practice and what you were hoping to do with that and was ISV ever on the table for you or did that happen accidentally? Walk me through where that really turned into that independent software vendor aspect for you. >> What's interesting about it is, you know, looking at the our heritage business model, you know, being the bar, being the professional services organization and the challenges that come with how do you actually grow the company, right?
And it's through headcount. And it's it's wonderful when you have a great team like we do here, but it's also very challenging because we uh we focus on a very niche aspect of uh industry that's very very specialized. So it's you know not as if there's a whole plethora of people that we can go out and get if we need additional headcount. So it you know it creates some challenges and nuances that we have to overcome you know different periods of time throughout the year and near-term long-term so on and so forth.
So we decided many years ago that we wanted to do some type of IP development and we've had some starts and stops with other aspects of IP development uh and that we've created content we would license that content and once kind of as a head start to the solutions that we offered and uh that worked out for a while and you know the the industry moved so fast and all of a sudden that content was created by a lot of other people so the value of that actually kind of diminished over time so we had to go back to the drawing board what else can we do So when AI became top of mind and you had the frenzy of of of everything that AI can do and it's you know really the kind of the panacea we we install it and everything works magically and and uh you know makes everybody's lives better.
We knew better than that. The biggest challenge that we had is that because the frenzy was so chaotic and the folks and the engineers there needed in order to get something off the ground. There was a lot of competition for that. you have competitions with all the big names in the tech industry that that make the news every day. And I was very very fortunate that we were able to hire our first AI engineer on November 6th, 2023 and he came on board.
And what the catalyst to that hire was number one, we wanted to build an AI practice. But in order to do that, you either partner or you hire hire folks for that. So we had the opportunity to, you know, to make the hire. But about two weeks before that, I got a phone call from uh an old client in social services, McKinley Children's Center, and they reached out and says, "Hey, we would like to find a way to bring AI into our operation, and uh if you're willing, we we'd like to talk to you about, you know, how you might be able to help us do that." And that was the start.
So, that was the catalyst to hire our first AI engineer. And then, you know, several months later, we actually embarked upon a project for them. And uh and it it's it's been very fulfilling both in terms of you know trying to find a way to transform our business but at the same time producing an outcome that uh you know a lot of folks you know beyond just the financial outcomes that you would expect and productivity outcomes you know with with metrics and quantities associated with it it was an outcome that affected people and that has actually been a really really neat experience for us.
First of all, I have to say I think you're the king of pivoting and figuring things out because it's amazing is we talk about AI and iterating with AI, but when we talk about business, it has to be the same thing. And you've really found a great way to continue to iterate on what your business is is going to do based on what your clients need and what the market itself is demanding at the time. So to do that in such a short period of time is really impressive.
Now, you mentioned McKinley Children's Center, and there's some beautiful success stories there that I would love to talk about, but I also want to dig into kind of your pinnacle moment of where you realize life sciences is going to be the niche that we're going to be practicing in. Can you talk to me about the tissue bank that you partnered with to become what you are now essentially today at data 41? >> Yeah, absolutely. and and and again this is one of those where we were looking for an opportunity but this one you know to a certain extent more lucky than good.
Um, so we actually embarked upon a a sales cycle with a company that was looking to bring in Generative AI into their the front end of their their donor analysis process and uh basically we got a lead from IBM who's been a wonderful partner for for many years you know along with Anger Micro and we uh we went into a joint sales cycle with them and you know basically looked at the requirements looked at the outcome looked at you know what they were trying to accomplish and they decided to move forward with us and We embarked upon that that project and basically what it is is that they are a tissue processor.
So they have an array of different types of inventory they produce you know from donor tissue. So when the donor tissue comes in they have to look at uh the eligibility aspect of that particular donor and it's everything from it's a cataric donor. So basically they're looking everything from you know illnesses, diseases, uh exposure to different uh types of disease like malaria or hepatitis and then also looking at lifestyle and then from that uh criteria they can come up with with a set of guidelines on on whether or not that donor matches and is eligible for the tissue processing that they do specifically.
So there are a number of analysts that they use on the front end that comb through hundreds of pages of donor record documentation on a manual basis to ensure that they are they're an eligible donor. And as they go through that process as you would imagine depending on how long you've been doing skill set context is is really important here as well in terms of you know certain things that they look for and then obviously time. So they wanted to bring an AI solution that actually helped them with that.
So we actually developed that solution. So the front end they'd actually look at the donor eligibility and then from there you know once they determine okay we have that the uh eligibility and they match up then they have suitability in terms of what you know downstream production of uh you know products they can manufacture that will then be used for the patient. So it was really trying to streamline that. So as we we finished up that project, we had a dialogue with them and they told us is you know there's an entire industry of companies just like us that could benefit from this type of solution.
You should look at attending this conference called the AATB conference and trying to showcase your solution because you'll probably get you know a lot of demand from it. So we took their advice, made the investment and uh about a little less than a year and ago in October of 2024, we actually attended the ABV conference and sure enough right we had all kinds of interest come from that. So from there we decided to commercialize a product a product a software product and uh and and go out to market like as a software company and you know basically to focus on that part of the donor life cycle management you know aspect of that industry and we landed our first client back in May and uh and you know things have actually been very positive since and and we have a a really good opportunity not only to go in and provide automation you know through AI but If you look at the end product, the end goal is how do we compress the cycle of them bringing in the donor tissue to produce a a finished product that can get to the patient and try to do that without error more accurately and to do it faster because you know human lives are actually being affected by this.
We're not doing it, they are, but at least we're part of that process. >> I'm curious to know, did you ever hear about how your software was able to help somebody get what they needed faster from this tissue bank? That might be a question that you wish you knew. I know in healthcare they probably can't talk about it, but I'm curious. Did they ever talk to you about how it helped them be able to impact human lives in real time? >> Yeah, we actually haven't gotten that far.
I mean, the we're still fairly early in the relationship and the solution. And AI, as you know, it's it's one of those also there's a a maturation taking place. uh the technology is is still has a ways to go to be able to to have uh the data points in order to really compress that cycle and and one of the things I like to say is that AI is very u heavy on nomenclature but very light on jargon and when you get into a very specialized industry you need a lot of those data points with the jargon and so our our solution is is is growing and becoming more intelligent every day with more use.
However, uh one of the things that uh one of the philosophies that I think that we really really uh absorbed and have taken to heart um I had a conversation with CEO one of our our donor clients and he was talking about there's a psychological and emotional aspect you know kind of going to your point about that industry. So it's one thing to take in a donor and many times a family has to make the decision on whether or not you know someone become you know dece you know deceased member of their family becomes becomes a donor and he referenced that as a gift and he says one of the things that we have to ensure that we do with that gift is to respect the fact that somebody wants to be able to touch many other lives and we have to look at that and we have to ensure that we're accurate we are precise and that we utilize the tissue downstream in a manner that is going to be able to be productive for the recipients of of you know that final product so that we can go back to families and tell them these are the lives that you affected and here's how those lives were affected by the donation that you made by the gift that you made.
And we took that to heart because we're a small piece of their entire process, but we know that we can make an impact especially if we can do help them do this better. And it's just it's it's very fulfilling to be able to to offer a solution especially a technology solution because it's so conceptual to be able to have an impact on you know on society and community and and uh you know individual lives. >> That's incredible and I totally agree about calling it a gift and you really want to respect to get it from point A to point B the way that it was meant to and and as efficiently as possible.
So, it's one of those conversations, Hans. I'm sure you know because you go to all these conferences, you talk to so many people. We don't always get to see the solutions and the work that we do inside of this industry impact the real side of human life. So much happens outside of the help desk, but that is just an incredible story to hear. And I know that this this is this is one of many. I know you've also been working, as you said, with McKinley Children's Center.
And I would just love to give you another opportunity to talk about a success story there that data 41 has been able to help. >> Yeah, it's it it was really nice and it's one of these it's it's funny you know um magic happens through conversation and I had a conversation with uh you know our some folks from our our partner Micro and uh just kind of shared the story of what we're doing with AI. Everybody at the time is like do we have any partners that are actually doing AI?
They're providing solutions and and I said yeah I've got one you know let me tell you what we're doing. So, a few months go by and they approached us said, "Would it be possible to get McKinley to co-present with you at the Inger Micro One conference in 2024?" I said, "Well, let me let me see. We're kind of in the middle of of this endeavor right now. Let me see where they're at." So, McKenley actually helps at risk youths. They've been doing this for hundred years.
They've been around literally a hundred years. They started out as a foster school for for boys and then you know you know over time you talk about adapting to you know what's happening and and having to kind of remold yourself and ensure that you're relevant in order to survive but more importantly providing services back to the community. They've been doing this for over no 125 years though celebrated in in October of this year. >> My goodness. >> Yeah.
So a long time I mean for this type you know type of social service. So they have a long storyried history of what they've done for the community. And so whenever uh they reached out to me and says you know can you help us with AI the questions at that time were uh fairly prevalent you know my answer is you know backwards sure what would you like us to help you with well that's why we're calling calling you and I said okay well it's a little bit more in depth than that because this is very use case driven very data driven and outcome based and so they had to go through an exercise of you know looking at the the outcomes that they wanted to try try to derive from from AI high and they came back with about 10 or 12 use cases and then we prioritized those and decided to embark upon one of those.
So they have a number of uh clinicians you know better known as therapists or or social workers that take on case files for children. So they they meet these children and and the level of need varies. Some of them are are dealing with traumatic events that, you know, most people don't even encounter in their lifetime and they're having to to work with these kids. And many times these clinicians, you know, come on board and they're given a set of kids, especially if they're if the clinicians are new and they've got case files, you know, the donors.
Case files can be, you know, 100 to 300 pages on average up to a,000 pages. Same thing, mostly digitized documents that they have to comb through, look at the history of the child, look at, you know, what they're treating the child for, try to get an idea of the clinicians or the the case workers ahead of them, what they walk them through. So, trying to get a a really good history of the child and then develop a treatment plan. As you can imagine, they went to school, they're professionals, they're very, very empathetic, and they want to be able to make a difference.
And there's a lot of regulatory paperwork that comes with being being in in this job. So a lot of their time was being consumed by having to fill out paperwork. Number one, reading the paperwork to get idea of who the child was and then after the fact developing a treatment plan for the child and then having to then do additional paperwork so that it adhered to the policies and procedures of the different, you know, regulatory agencies that McKinley works with.
So what we did is we developed a solution that would basically summarize the the front end of the process to where it would review the hundreds, you know, to to thousands of pages of case files for the clinicians so that they can hit the high points and have a really good understanding of what treatment would be needed for the children in order to visit with them, come up with the treatment plan. And the goal was not only the, you know, put together a treatment plan, but to be able to produce a p positive outcome.
And uh so this was, you know, sent out to I I forgot what the sample size was a couple a few months ago. Uh a number of clinicians to be able to do this to get them in the mold of of using this. But, you know, a lot of their week would be consumed by paperwork. And they weren't trained to do paperwork. They're trained to actually interact with the kids. And so this was consuming more of their time than the actual facetime with the kids. So, we're trying to free up, you know, a lot of of what their work week looks like so they can focus on the kids.
So, the kids know that they've got somebody sitting in front of them that actually cares about them that is going to put forth all the effort needed to help them, you know, become positive members of society. And then, you know, the paperwork aspect of it is, you know, we can press the cycle time on that and make it you more of, you know, an afterthought rather than something that is too burdensome. >> I think that is absolutely so beautiful. We don't hear too often about the side of AI where it helps humans.
It's it's one of those headlines that does not make it to the top. This is that headline that I want to shout from the rooftops right now about how it's able to help these kids. That's incredible. Hans, you mentioned about McKinley coming to you and saying, "We would like help with AI." And you're like, "How?" And they went, "How would you recommend it?" How has that process with working with customers eased up over the last few years as you've done it more?
Do you are you able to go in now with a sort of uh template and saying, "Well, here's what you need to consider so that we know how to serve you." How does that conversation go? >> Yeah, it was it was a learning experience for us as well. Um, you know, we're we're walking in and said, "Okay, great. We we've got our our AI engineer on board now and uh we've been, you know, basically selling and and positioning analytic solutions, you know, from day one." So there were a lot of similarities with how we position analytic solutions and the process that we followed and you know how you you you kind of follow a similar process with AI.
We didn't know that at the time though. So when we engaged the conversation was almost like you know hit play, hit stop, hit rewind and then hit play again. And it was the same conversation because we were interacting with with clients and I think a lot of it's because of the chaos and confusion the industry is providing you AI is going to just be this magic thing that you you install you know like I said earlier and it just works. Well it doesn't work that way.
So the initial conversations were hey can you help us with AI? I said of course how would you like for us to help you? Well that's why we're calling you. And that happened over and over and over again. And so we had to basically change the way that we interact and say okay uh number one right let's let's look at what outcome you you want to get says okay well how do we determine the outcome and said uh look at the things that actually take a lot of time in your organization either something that's very manual or even something that's automated but there's multiple steps and we can actually you kind of help out with either one of those then prioritize those prioritize those on impact right what is going to have the highest impact in your organization if you can you know relieve yourself of some of that inefficiency.
Then the other thing is like look at what would it take as far as your investment in time and cost >> in order to you know be able to move a solution forward. So you know we we we kind of do a T-chart on this stuff and come up with a use case and then come to an agreement on that use case. Then we go in and do a prototype and says okay here's what we got based on the information that you have. And then the prototype would move into okay well how do we actually you know look at this from a solution standpoint.
So, a lot of the same steps, but the dialogue matured and and evolved, you know, over the the course of, you know, a year to two years. And I think that the the consumers actually got smarter as well, right? There's a lot more information out there now. There's uh more collaboration they have within their own networks of peers and who's doing what and we've also got uh you know more um I think defined use cases now than they were in the very beginning.
So they had that as a backdrop to kind of use as a reference point as well. >> Very good points there. Yeah, the user audience has definitely become a lot more educated just because it's been so ubiquitous across what we can use as far as just on our personal computers and cell phones and then what's being implemented at our jobs. So a great point there. Now speaking of that, obviously AI has advanced so much in the last 2 years, but it's not all the way there.
It is going to be something that continues to be iterated on. What roadblocks do you still run into with artificial intelligence and how are you hoping it continues to iterate in the future? What would you like to see it be able to do to get past those roadblocks? >> Yeah, there's it's so I think there's um a few things right there there's an emotional aspect of it in in that every single day you you you know read you know online articles AI is going to take our jobs.
So the people that we're interacting with are the ones that are most fearful that AI is going to take their job because all of a sudden they're helping develop a solution that's basically, you know, taking away some of the things that they do on a, you know, a day-to-day critical basis. So, so we have to overcome that psychological aspect of it that it's not necessarily going to take away jobs. It's going to take away certain functions within jobs that will actually enhance your value to the organization because you're focused on those things that matter most and you have more time to do it.
So that that's one one aspect of it. Then the other you know like I said a bit earlier if you look at AI in general very powerful but I view AI as a tool use you know chat GBT perplexity you know some of the other ones online right it's a new way to look for information and then you've got the different vendors with solutions that you can actually implement internally to be able to go out there and uh and get to that information. also a new way to retrieve information, a new way to consume information, a new way to compose information.
It's really hard to quantify how much that is affecting your organization positively and increasing productivity. It's it's assumed that it does and it's it and it's one of these that uh everyone's accepted the fact that it does, but it's hard to put a number to it. Agentic AI, I believe, is going to make a big difference. We just don't have enough of those yet. and then the interoperability between the different types of agents and what they do and and the conflicts when they overlap um is going to be pretty interesting.
But I think that's going to be one of those areas where we uh see a big jump in productivity to where it's really solving problems. And then the second part is is that as we're getting into these niche industries, the AI models aren't built or aren't built for those those industries in particular. So there's going to be a lot of training and a lot of data that has to be input into those models in order to build the intelligence to where they're making a difference.
And we're living through that right now in the tissue bank industry because each one of them is it's a very competitive industry and that they've got their own confidential processes that or not confidential but their their own uh discrete process that they use within their organization that separates them from another tissue bank that does the same thing. So there's not a whole lot of sharing of information. there's sharing of we're using this vendor, but there's not sharing of how we're using that this this particular vendor.
So, as we start training these models, we're doing it on more of a siloed basis. But if we could take that information and aggregate it, it would had have a dramatic effect on that industry, a more positive effect that could be uh utilized across the entire industry. But, you know, it it's it's going to take some maturation to get there as well. So, we're still in in the infancy of it. The models are getting smarter. the way that we're utilizing technology is is maturing and the way that we're actually, you know, composing a solution that produces an outcome, we're becoming more effective at it and and like I said, it's just it's moving at a very fast pace, but at the same time, right, there's a there's this this process of refinement and incre incremental gains that we're getting that is moving at at u I'd say more normal pace.
Well, speaking of pace, how are you continuing to iterate with artificial intelligence into 2026 at this point? We're almost to the end of the year. I know that you're you're constantly looking to also grow on the solutions and the outcomes that you've already been able to bring to market. What are some ways that data 41 is continuing to iterate with AI? >> Yeah, one of the most obvious ones just using internally. So, we we've had, you know, most of the products from technology companies now are infused with AI to some extent.
And so some of the solutions from our heritage EPM business has that infused now and is getting the team up to speed on it because we're viewed as the experts. So if that's another component of it, you know, we need to understand and be able to pontificate, you know, the value of AI within the solutions that we've developed through our heritage business. Uh the other part is you know looking ahead two years ago we were just trying to become a branded AI company experts in AI and we've had a lot of demand come our direction and we know that we can't be all things to all people and we're trying and so we we've got to be a little bit more focused on what part of the business and where do we actually want to play in AI so with the donor life cycle management working with the organ processors, our procurement organizations as well as the tissue banks.
That is an area that, you know, we're going all in. Uh we see a huge opportunity. We got some brand recognition now. And then looking at social services, that's another area. Uh although, you know, with the uh geopolitical environment right now, they they've been hit a bit, you know, funding and and you just trying to find their way and then looking at AI to come and alleviate some of that. But it it's it's a long process. So, we're looking at that as as another opportunity.
Obviously, you know, you look at it from a P&L and general ledger perspective, you know, how do we actually, you know, maximize what we're doing? But that particular industry, if you look at at what we could do to be able to affect the community, it has a whole different meaning. So, you know, it's one that we're looking at pretty hard as well because we get the contacts, we've got the use case, and it's one that I think we can actually go in there and and help make a difference, but it's more meaningful than just, you know, dollars and cents.
And uh so we're looking at that. And then you got custom AI projects and we're being very selective about you which ones we try to work on. Uh because we just you know we're running lean and there's a lot of demand and it's exciting and it's transforming our company but at the same time we can only do so much. >> No, it's important to pick your battles otherwise there there is such a thing as stretching yourself too thin and not being able to help anybody.
So you have to make sure you you can help effectively those who you do help. Speaking of help and speaking of context that you mentioned a couple of times during this interview, you've brought up IBM and you've brought up Ingram Micro and I would love to just get a sense from you of how that vendor and that distributor have helped you be able to continue to grow. Uh you talked about some deals being brought forward, but what has been the real get for you and having those relationships and being a part of those communities? >> Yeah, so you know IBM is one of those that we, you know, we kind of got thrown into in the beginning.
Uh fortunately for me early in my career I I worked for u u an IBM partner as well and it was software company but wasn't you know traditional bar but it was software company so I had an understanding of what that ecosystem looked like and you know how they operated so at least that wasn't foreign to me but it it took a lot of effort to kind of get in there but I will say this about them you know they they're going through an evolution as well just like most companies you know things are changing in a fast pace and they want to ensure that they're relevant their solutions are are meaningful but they've been a very very good partner.
They support the channel. They've been doing a long time and they've got a channel model that works. I mean, you know, it's not perfect and I don't think anybody's is, but they have a channel model that works. They support it. They make adjustments and we've had a lot of longevity and a lot of success. And we've we've got some marquee names. Can't mention uh in in in this interview, but I've got some marquee names, household names that we've been able to uh have the privilege of calling our clients because of our relationship with IBM.
Uh, Ingram on the other hand, you know, kind of came through and we didn't even know what a distributor was back then when we entered the IBM ecosystem and we had, I think, one of five we had to choose from. So, we selected Inger Micro because they're right here in our backyard in Irvine, California. And I would say that that partnership is probably more impactful, not not to diminish anything IBM's doing. And part of the reason is is that you know we started with them in 2009 and we've had also a wonderful relationship with them but they've got relationships with hundreds of vendors and you know another vendor that we work with is Microsoft and Ingram has been a great conduit to get to the right folks at Microsoft especially with AI and and helping out with that.
But if you look at the hundreds of vendors that they manage and the number of partners that they do business with and we had this happen a few weeks ago to where we need to establish a relationship with a software vendor that we have no contact with, we have no background with, we have no credibility with, but Ingram does and they are our conduit into that software company that offers credibility that gives us, you know, some footing that gives this welcome introduction to somebody that we don't know and there's going to be partnerships in the future we don't know we need today that they're going to be able to help with and kind of foster that relationship.
So they're really really important from that respect. And another uh aspect of it is you know just kind of touching real quick you know we've had a few different inflection points in the company having to adapt and and you know change direction a bit. AI is probably the biggest one but back in 2015 I joined Inger Micro's Trustex Alliance at the time it was is called you know the btn or venture techch networks and was rebranded in 2016 to Trustex Alliance.
And over the course of 10 to 11 years that I've been in that, that's probably been the biggest value ad that any offered me, you know, over and above, you know, just your normal day-to-day business. But that is a community of partners just like us. A lot of collaboration, a lot of networking, a lot of opportunities to do business together. And it's open up the doors to I'm part of a masterminds program. So an executive, you know, peer group that, you know, meets once a quarter and really goes into the business. uh gives access to executives gave us the forum without trust XX alliance I would not have had the opportunity nor would McKinley have the opportunity to be on main stage at the one conference last year right so it gave us an opportunity to talk about what we're doing kind of like now it gave McKinley an opportunity to talk about what they're doing to a completely new audience and all of that came through trustex alliance and it's offered an opportunity for me to get others engaged in the company to kind of bring them up and their toutelage and their mentorship because they're the next wave of leaders and this has been an absolutely awesome experience, you know, again over and over over and above just, you know, them being a great partner for business. >> I love to hear that and this is why I love the channel.
This is why I love to talk about it. So, thank you very much for those those amazing relationships that you've shared with us. I I want to make sure I give you the opportunity to talk about any other vendor partners or partners in general that you might be working with that also deserve a shout out since we've got the platform right now. Yeah, I mean Microsoft has has been great as well. You know, and again this is I think the initial aspect of it, you know, started with Micro being our advocate, you know, getting us to the right level within Microsoft said you really need to help out data 41, right?
They're doing some great things and getting us the exposure and credibility we needed when we didn't have those relationships. And they're really really becoming a bigger centerpiece in our AI push. And so it's, you know, we're just getting started. We're excited about it. They're excited about it. And we got a long way to go, but it's uh you don't get these opportunities very often in industry. The last time was the dotcom boom. And all of a sudden, now we've got AI.
You know, whether it's a bubble or not, the solutions actually matter and the solutions are making a difference. And and having a large vendor partner, you know, backing you up makes, you know, that journey that much easier. >> I 100% agree. And they do. You're right. They actually matter. They're making a difference in human lives. And I'm excited to see where that continues in the future. You know, Hans, as we start to wrap up here, I I am curious to know, looking back, like I said throughout this interview, you've evolved, you've pivoted, you've adapted to what the market is telling you to do and and directing you where to go.
You've really listened to your customers. With all of that almost two decades now uh uh knowledge in mind of running data 41, what would you say to the next person in line that is considering going out there and starting their own business, whether it's similar to yours or in the same channel industry or maybe something totally different? What advice do you have for somebody that's going out there to start their own business? >> Yeah, surround yourself with good people.
Um you know, there's only so much I mean there there's the the old phrase, if you're going to go fast, go by yourself. If you want to go far, go with a team. And and it it's really true. You know, we we've lived, like I said, the the challenges and trials and tribulations of of running a small company. They're immense. And then you throw in the financial crisis in 2008 and then co getting through that. You can't do that by yourself. You've got to have have a good team around you.
You've got to have good partners. You've got to have a great relationship with your clients. And you know at the uh the one conference you know several of the the presenters talked about it's not not AI it's the human connection. AI is just you know make that human connection you know kind of evolve a little faster but you've got to have that human connection right we're dealing with technology but we're infused in the technology that we're actually providing.
But it's those relationships that are going to define the companies that are going to be successful in the future or not. And so make sure you got a good team. Yeah. And I I've just got a stellar wonderful team. We've got folks I my number two employees been with me for almost the entire 20 years and and that's a blessing, right? You don't get that with everybody. We've got folks that have been here uh for 15 years, you know, half of their entire career, you know, coming out of college being with, you know, my small company.
So that's internal. And then you having, you know, good partners, IBM, Microsoft, Ingram, folks that are going to advocate and look out for your best interest, you know, by the way, yeah, we have to make money at the same time. Everybody gets That's just part of the business model, but you got to manage, you know, the top line, you got to manage the bottom line as well. And that's just comes, you know, it's fundamental and you have to do those things, but you got to have the people to help you help you get there and uh, you know, to help you smooth out the undulations of of, you know, when when things are tough and when things are going really well.
And I think that's the biggest difference is just surrounding yourself with really really good genuine people. >> I think that's phenomenal advice for so many people who are who are blazing their own trails. So, thank you very much for sharing that, Hans. And if those watching want to learn more about data 41, want to get to know what you do, what you're doing out there in the world of life sciences and beyond, where can they go to learn more? >> They can call us.
We've got, we'd love to hear from them. You know, we've got a website. We're actually kind of revamping our website now and I'm on LinkedIn and they can hit me up on LinkedIn. Always happy to uh meet new new people and expand the network and find out what we can do together. >> Love to hear it. Well, Hans, thank you so much for your time today. I'm very excited to follow along and be a fan now of data 41 and see where you go in this market. So, congratulations on all your success and I'm looking forward to following along. >> Yeah, thank you.
Yeah, appreciate the opportunity. This has been a lot of fun. >> Thanks so much to Hans for joining me today and thank you for watching or listening. You can check out every episode of Channel Insider Partner POV on channelinsider.com or watch us on YouTube at youtube.com/ channelinsider_news and trends. You can also listen to us as a podcast wherever you get your podcasts from. Don't forget to like, subscribe, and follow wherever possible so you never miss an episode.
Come connect with Channel Insider on social media, on LinkedIn, and X. Once again, I'm Katie Bavoso, and I'll see you next time.
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