Ep. #1058 - How Much Is Your Data Really Worth?

Matt DeCoursey 00:00
And we’re back! Back for another episode of Startup Hustle. Matt DeCoursey here to have another conversation I’m hoping helps your business grow. So if you have a business, if you really do anything online or offline anywhere, then there’s data that comes along with that. Data is such a hot topic these days. According to some people, it’s the most valuable thing on the planet right now. One thing I do know is if you don’t do anything with it, it isn’t worth squatting. So what we’re going to talk about today, and before I introduce today’s guest, a quick reminder that today’s episode of Startup Hustle is powered by FullScale.io. Hiring software developers is difficult. And Full Scale can help you build a software team quickly and affordably and has the platform to help you manage that team. Visit FullScale.io to learn more. With me today is Roger Ngo. Roger is the co-founder and CTO of DataAppraisal. You can go to DataAppraisal.ai. And I don’t want you to try to figure out how to spell “appraisal” on your browser. So the easiest thing to do is scroll down and click that link in the show notes. You can learn all about what they do straight out of my hometown of Kansas City. Roger, welcome to Startup Hustle.

Roger Ngo 01:10
Hey, Matt. Thank you for the opportunity. Happy to be here today.

Matt DeCoursey 01:13
And I’m looking forward to this conversation. It’s, you know, data is everywhere. And we’re gonna get into that. And before we talk about any of that, how about a little bit about your backstory. Both as a founder and, well, I don’t know what brought you here to us today, Roger.

Roger Ngo 01:28
Yeah. So, you know, I’m an electrical engineer with education and experience. And, you know, I joined my co-founder and CEO partner, Tam Tran, because we wanted to solve a problem by helping companies appraise data. And then turn around and monetize that. So really solving a problem that no one else has a solution for right now.

Matt DeCoursey 01:48
We first met you because you participated in the LaunchKC program here in town. And many people may have seen you through our social media. You are one of our famous participants in our elevator pitch video. Because, well, we have people help them give a pitch and then make you give your elevator pitch in an actual elevator. So if you saw that, Roger was in that. And if you haven’t seen it, go find it. It’s everywhere. Yeah. Was that a surprise that day?

Roger Ngo 02:16
It was a surprise. But you know, some good coaching was able to deliver a solid pitch there. Yeah. So thank you.

Matt DeCoursey 02:21
I think we did alright with that. That was fun. You know, that was? For those of you that aren’t aware, well, if you listen to the show regularly, you’re probably familiar with LaunchKC and get a launchkc.org. And that’s a division of the Kansas City Economic Development Corporation, KCEDC. Is anybody that can say that five times in a row without messing it up when surprised here. So it’s kind of hard, but maybe we could actually start there. Because with that, with that program, you were accelerated, per se, and to get your business started, and you have kind of a maybe we could go into your elevator pitch, because you gave that in front of a whole lot of people, including our mayor, who was very impressed with all of the participants that night.

Roger Ngo 03:08
So the elevator pitch? No, I mean, sure, sure. Yeah. So the elevator pitch is, did you know that you’re sitting on a goldmine right now? Over the course of the last couple of years, we’ve collected more data than the entire previous history of mankind. Hi, my name is Roger Ngo, and co-founder and CTO of DataAppraisal, and we help you appraise your data, turn around and monetize that and share that with other people. So there are a lot of opportunities out there to share this data for good. We’re focusing right now on the healthcare marketplace and healthcare data, and that’s going to help pharmaceutical companies help come up with solutions to different medical needs out there. Beautiful.

Matt DeCoursey 03:53
And you made that out, and you browse me a little bit. So there’s nothing better than coming by the Full Scale and Startup Hustle office, where we can put you on the spot. But yeah, as I mentioned in the intro, Roger, you know, data is such a hot topic, and it’s everywhere. And we haven’t talked about this a little bit. When you guys were here with a couple other entrepreneurs, getting ready to talk to a roomful of people about what you do. But I mean, the premise of DataAppraisals is that businesses don’t monetize their own data. I mean, what’s that’s the problem we’re trying to solve, right?

Roger Ngo 04:32
Yeah. So the example I always use is, you know, consider that you’re a widget manufacturer, and you’re really good at making the widgets. But that, you know, that’s what you focus on collecting data about that all like during the duration of that manufacturing process. And that’s the core business in the core strength. What we want to do is we want to assist that company in monetizing that data that they’re just sitting on that goldmine.

Matt DeCoursey 04:58
So, I mean, companies have limited opportunities for revenue.

Roger Ngo 05:01
And here’s an opportunity to expand that. And lead DataAppraisal, work that whole customer journey for you, we’ll get together with you, we’ll show you the value proposition, we’ll walk you through our appraisal process. And then we’ll turn around, and we’ll find customers for your data and bring in that residual revenue, right, let your data work for you, in addition to what you normally use it for, for internal process improvements.

Matt DeCoursey 05:31
So when we say data, I think there’s going to be a lot of people that are listening that are going, oh, yeah, I get that. And then some people are like, Well, what do you mean? So like, what are some examples of basic data that you use in the widgets example, which reminds me of the last business school that I dropped out of? It was always ACME Corp that made widgets. You know, that was like a very familiar thing. But you know, part of some of it in school, that example would have been like, there’s Person A, B, and C that works in the factory, and they earn, it’s managerial accounting, which I was terrible at. Because Person A, B, and C all made diff, they all made different pay rates, and they all produced at different velocities. And with that trickled down to all being different costs of manufacturing. And then, in some cases, you would find that this use case would be that employee C, who was highly paid, and also highly productive, actually wasn’t even profitable to keep around. And that would be like internal data. But I think some of the stuff that you’re talking about is more about data that you collect about users or someone else. And, like, what are some examples there?

Roger Ngo 06:41
Yeah, so let me use the example of a current customer that we have that kind of kicked off this whole journey. So we have a customer that spent over $80 million on marketing efforts. And they do that to market the products that they’re selling. However, they don’t realize that that data is very valuable to other companies and businesses. So, you know, part of our appraisal process is to take a look at the customers that they have, or the members in their system, and then to create a profile and add a valuation for that customer. And it’s based on multiple factors, right? So how much is this person spending? You know, how much money are they spending? Over? What time period? Are they spending that? So would you know, when you do that, that marketing data is very valuable to someone else? And if we can pinpoint and put a number on it, because this customer doesn’t know what their data’s worth these 80,000 customers or clients that they have? And they don’t understand that. And so we want to help them be able to sell that to others.

Matt DeCoursey 07:48
Okay. All right. So I mean, your business, it’s you, it’s you, your job, and your duty to pull the most useful revenue, or whatever out of it. I mean, is it true that everyone’s business has data that has value to it?

Roger Ngo 08:06
Everyone’s business has some type of value, but overall, their data has value, but not every piece of it does. And that’s where we step, and we help you say, hey, this data is valuable. This is one of your strengths, right? This data is not valuable. Maybe you should stop collecting it. So we help provide that overall assessment other than just saying this is what’s valued. Here are your strengths. Weaknesses, here are some opportunities that you can leverage.

Matt DeCoursey 08:32
There’s the most valuable part of that data, the stuff that’s related to buying activities.

Roger Ngo 08:37
It depends on who the customer is, right? So if it’s going to be used for a retail effort, or marketing, it could be for buying activities. But if it’s for clinical data that pharmaceutical companies could use to come up with medication. Yeah. So you know, that’s, that’s, that’s a different vertical.

Matt DeCoursey 08:54
Do they? I would imagine that from what you guys do. It’s challenging to have such a broad and well-rounded level of expertise. Do you have to work specifically with the clients to kind of figure out what actually matters at that business? And what isn’t? Is that a halt? Is that part of the process too?

Roger Ngo 09:16
Yeah, it is Matt. So what we’ve been doing is we’ve been focusing more on the healthcare vertical. So initially, we’re looking at retail, finance, and healthcare, but you probably just described 80% of, you know, businesses out there. So we decided to focus on healthcare, and even within healthcare, there are multiple EHR and electronic health record systems, and they’re all different, and each hospital may use different fields. So it’s really complex, but we’ve got a methodology in place where we look at the most important data set and value and tables and that piece of information.

Matt DeCoursey 09:52
So I’m not going to pretend to be a data scientist, so I know enough to maybe be dangerous, but I’ve learned that you have structured and unstructured data and unstructured data. So if you have a web, if you have a software, you know, web app or something like that, you probably will, whether you know it or not, you probably have a ton of information in a database somewhere. And sometimes, that is structured, and sometimes it isn’t. And when I say structured data would be the equivalent of looking at, like, a clean spreadsheet that had peaceful columns and rows that made sense, whereas unstructured data is a jumbled mess until it’s structured, do you help the companies also trying to get their shit together? When, like, Hey, this is a rat’s nest of stuff, we’re gonna need to untangle it or like, I would imagine a lot of people’s data is a mess.

Roger Ngo 10:47
Yeah, so we look at both, and that, you know, you mentioned that the structure data is clean and good looking? Well, you know, I would say that’s not always the case, either. So it’s more organized but not necessarily clean. And so, you know, we can provide suggestions and help with that also. But we also take a look at unstructured data as well. So we’ve got plans in place to go evaluate, you know, audio recordings and other natural language processing. So looking through semi-structured and unstructured data to really understand the sentiment of, let’s say, doctor’s notes, right, and pull out keywords like cancer and different prescriptions.

Matt DeCoursey 11:24
Well, that’s why NLP natural language processing is a big thing with AI. And, you look at the sentiment analysis. So what does that mean? So let’s use yours. Those are three different things. Right, and they have three different meanings. And, you know, so how do you train a computer to understand what the hell any of that man says, as a challenge, and, you know, chat GVT came out several months ago, and everyone’s all that, you know, I feel like that is going to open this floodgate of AI stuff that is, is interesting now that AI that everyone’s in love with is afraid of all runs off the data. And yeah, I’m looking at DataAppraisal.ai right now. And, you know, I’m looking at your process, and it talks about connecting data sources, step one. I mean, that’s got to be a challenge, man, because there’s everyone’s got a different thing in a different programming language. And, you know, there’s, you can go out and Google the top 50 programming languages, there’s 50 of them, there’s more than that. And they’re all going to connect in a different way. And, like, What’s the hardest part about just a simple connection?

Roger Ngo 12:39
Yeah, so I mean, it can be complex, and, you know, we’re trying to accommodate the user. So you know, we’ve connected with one healthcare system. And you know, they’re very specific in the requirements for doing a site to site VPN. So we had to get our house in order there and make that connection, make sure that it’s secure, because we’re dealing with HIPAA type information. So and, and that’s what we’re working to understand and execute as well. But yeah, so there’s so many different data sources, different databases, different types of databases, it could be an Excel spreadsheet, or CSV. So, you know, there are so many options out there.

Matt DeCoursey 13:18
And then you’ll pull it from one place, and it’s a different structure than the other place. And now you got to probably figure out how the Hatters don’t always match people, I think that’s probably the main thing, and you know, and then Alright, so, step two, here is you have enhanced and enriched yourself. Let’s talk a little bit about that. So like, how do you enhance data?

Roger Ngo 13:41
Yeah, so you know, I will provide suggestions to the data provider or the person that’s looking to get their data appraised, what type of opportunities there would be to enrich their data. So to make it more valuable, sometimes when you look at data, this part A is valuable, or B is valuable. But if you put Part A and Part B together, it makes it even more valuable. And so that’s what we’re trying to share, right? So if you’ve got some address deals that are there, but it’s not complete. Well, that means that whoever the buyer of the data is has to transform that data or fill in the blanks. So we can help complete the data and make it more succinct, clear, robust, and, you know, and make it more marketable from that perspective.

Matt DeCoursey 14:25
So I went, I did an experiment Full Scale, a few years ago, and honestly put it down because it was a lot to keep up with. But for us, you know, we’re trying to help people find software developers and with that there’s this massive sea of places that we could do business with. And so that, that side of things, so you say where do you get your leads from? Well, a good source for us is looking at people that are posting jobs for software developers, until you go out and you collect that data for over a year and you realize there are 85,000 Different companies that place the job for us. Software Developer, it was actually closer to 18 months. And then there was five or six different places where they would post these jobs, they all had different fields, they all had different, they all have different descriptions, and then you try to structure it and it looks like a checkerboard, you know, meaning like blank spaces, compared to ones that are filled. And then you know, all I’m trying to do is look at 85,000 companies, and I can’t contact 85,000, I could send out spam, that’s not effective. I could send a cold email to 85,000 people and probably largely get ignored by all of them, because that doesn’t work. Right? So we’re sitting there trying to figure out how we get down to the manageable amount. And the problem we had was that incomplete data, because when it comes from one plan, and you know, the act of filling that in, coupled with the amount of new data that keeps coming in? Well, that’s why we paused the experiment, because it was overwhelming, and so is, so we needed to enrich and enhance our data a little better. And that’s I mean, that really is why we stopped doing that. And you know, some of that was also then how do I rank them? We wrote algorithms and different things like that. But the hardest part that I had was making it actionable.

Roger Ngo 16:31
I think the thing I’ve learned most about data is that if it doesn’t produce an actionable something, it’s just data, you’re just looking at data just staring at it. It’s yeah, but it’s got to tell you what to do with it. Right.

Matt DeCoursey 16:36
So how does a business solve that problem?

Roger Ngo 16:41
So how do they make it actionable? So that’s a broad question.

Matt DeCoursey 16:43
But it is, but I mean, I got to pick it, pick a case, or something? And like, yeah, yeah.

Roger Ngo 16:51
So I think that goes back to where we help enrich the data. And so part of what we do also is we’ll go through, and we have a quality indicator for some of the data and we’ll evaluate whether or not we think that it’s real, or it’s fake, right? Because a lot of times people are collecting data. They’re just putting in random information. It could be fun numbers 5555555.

Matt DeCoursey 17:12
Well, that’s 675309, right?

Roger Ngo 17:14
It’s obviously fake. And maybe that record is worth nothing, right? And so if you can pare down the data to, you know, the top 20% and focus your energy there, that’s where you may get about 80% of your value, right through the Pareto analysis. So yeah, so we help our customers with that. So if we can, we do more than just value the entire dataset, we actually look at individual customers, and rows, and records, right. And so that provides some type of indication into the value that and so customers can focus their, you know, their efforts on that, as opposed to everything.

Matt DeCoursey 17:55
Real, like so many business owners are in that kind of, I’m guilty of this too, at FullScale.io. And once again, today’s episode, Startup Hustle is powered by FullScale.io. We love talking to Startup Hustle listeners. So reach out, we’ve got a lot of solutions, I’ll tell you, you’ll at least get some good advice. And we specialize on that with every call go to FullScale.io Click that button that says hire developers. And with that, that’s where we began to collect our data. And we used to have this like a big long sequence, you got down to it in less than two minutes, because I don’t need to know a whole lot about you to pair you up with people that have the skills that you need. But that kind of gut instinct of not I don’t. I don’t really know a lot of the data because I haven’t really analyzed it effectively. I’ll give you an example. So someone that fills out that form that does it with a Gmail, I can tell you right now that they are incredibly unlikely to become a client of ours, because if you’re using Gmail and you don’t even have a web domain or anything like that, you’re not really ready for what we do. Because we help companies scale a development team, which means you should probably have a company first. So we actually will downgrade those leads like they’re actually going into a completely separate path. And outcome B, and we have yet to prove that wrong. I think we’ve had maybe one out of our 50 clients use Gmail to sign up. So they’re just like I said, they’re not really ready. But I couldn’t tell you the percentage either way. And you know, now in your next step in here, you talk about training and predicting, you know, AI ml, are you using both? Yeah, the form of it?

Roger Ngo 19:33
Yep. Yep. So we’re using machine learning and we’re doing we’re, we’re doing a combination of predictive algorithms to, to, to look at our data. So I mean, kind of similar to what you’re doing Matt with the Gmail accounts. That’s that knowledge that you knew and then that’s how you Yeah, well, that’s a good feeling, but it’s based on your knowledge, but then you can insert that you have advanced knowledge that the machine learning algorithm didn’t. But it would understand over time that Hey, Matt, with all these GMO leads, and there was no result. And if you go to a company lead, then it’s good. So that’s where it can make recommendations based on here, look at all the companies. But you already knew that right?

Matt DeCoursey 20:14
But there are others from experience. And I’m not even sure we have a big enough sample size to get into AI and ML, you know, like as, as much as I’d love to tell you that 1000s of people are filling out that form. They’re not, you know, and some of that is? Well, some of it, I think, on some of the forms, I think a lot of business owners try to collect too much data, which usually ends up in not collecting any at all, because people get freaked out. They’re like, why are there 30? Fields? You know, so try to keep that sample. And then I don’t know the thing with AI and ML, I mean, it usually doesn’t operate on small sample spaces very well. Right in AI and ML have an insatiable appetite for comparisons. See, the more training data, the better. We have a guy that’s a company called Bellwether, Matt moody has been on the podcast a couple times. And they have a very specific niche. And they work by looking at trying to retain and help their clients retain their users. And that occurs from like, they understand, like, if someone were to call customer service two times and an X amount of period, that might be that key indicator that tells you that person’s about to cancel. So looking at that predictive outcome, there was the example he used was a phone company, which just has like, terabytes upon terabytes of data, like daily, hourly, everywhere, you know, and there’s an insatiable appetite for that. So when it comes to like, what do you what’s the basic requirements and needs to structure and train an AI or ML model?

Roger Ngo 21:57
So there’s some amount of expertise that’s involved and discussion with a customer to help understand what data fields are valuable. And so we try to kind of look at everything, all the data fields, and then we start to understand in a predictive model, which values are important. So that’s where we start kicking out fields that aren’t maybe introducing more to see if that prediction is more accurate.

Matt DeCoursey 22:24
Is that where the output reports come in? Yes, yeah.

Roger Ngo 22:27
So we’ll do an output report. That’s our appraisal, kind of like a Zillow Zestimate or a CARFAX that, you know, describes some of the details and attributes of that data set.

Matt DeCoursey 22:38
Okay, so now we begin to get data and once we go to data, data, appraisal data, I link in the show notes because there’s a lot of stuff in there, everything from the intrinsic value of information. And there’s a lot of accuracy that’s i vi business value of information BVI performance value of information, P VI. And there’s a lot of you guys probably even used the Greek symbols and stuff that I complained about never having used after Yukon class, that might have been part of the reason why I quit school. I was like, I don’t want to do this shit anymore. Lots of things come with that. Alright, so now you get through that process. How do you go about it, so that is you talk about maximizing the value of the data. And sometimes that involves brokering or being transactional with other companies that aren’t yours. So what is how that works?

Roger Ngo 23:31
Yeah, so, you know, we’ve got a strong advisory board, and many of them are in healthcare, vertical and sector. So, you know, they’ve given us insights on where we can go to approach consumers of the data. So we know that pharmaceutical companies are a big deal. They’re important. And we’re looking at many other opportunities as well. But yeah, with a focus on the health care sector, we’ve kind of focused our vision there. And it’s been very helpful.

Matt DeCoursey 23:59
Okay, so what about the people that can have a client DataAppraisal data? I do? I mean, I’m assuming I have some say and where that data could or could be sold?

Roger Ngo 24:11
Yeah, so absolutely. Still, you know, we have a governance aspect of it also. So just because you have data and it can be sold, and someone wants to buy it from you, as the seller of the data, or the one that’s going to support the collaboration of the data, you choose whether or not you want to share that data with this particular entity.

Matt DeCoursey 24:29
So how much is once again, overly broad? How much could my data be worth? I mean, are these things? Well, I realized that’s totally broad, but I have a big data set talking, you know, 100,000 lines, that a million lines or whatever, obviously, I would imagine that that comes down to the industry and whatever. I mean, are we talking about 10s of 1000s of dollars, millions of dollars, like hundreds of dollars?

Roger Ngo 24:57
Yes, so we have customers that are selling their data for As little as $4,000 on a quarterly basis, and you know, we’re familiar with other data transactions that are upwards of $10 million, right, of payroll type information that can be shared. So it really kind of varies.

Matt DeCoursey 25:16
Yeah, I’ll get into that. I mentioned that experiment with the job postings thing. And I had also looked into buying that info, which would have cost me a couple grand a month. Okay, for like the height. Yeah. And so like if I went, and it would have actually been delivered in a structured format, which would have allowed us to go through a less hassle of spending way more money collecting myself than I would have purchasing from a specific place.

Roger Ngo 25:45
Right, yeah, I mean, so time is money. But look at the person that’s selling the day, they’re not just selling it to you, they’re selling it to 1020 100 other good bags.

Matt DeCoursey 25:53
Yeah. Yeah. And that particular case that was specific to hiring, and I asked him, I said, Well, who uses it was actually more like, government and like research type people that analyze job markets, and I don’t know, they do a whole lot of stuff. But it’s, it’s, it never ceases to amaze me the things that people use technology and data for, like, you know, heard stories about hedge funds, using satellite maps and machine learning to see if more or less cars were in Walmart. And like being able to predict I don’t know, man, it’s, it’s, it’s pretty amazing what people do with that stuff. And let’s, let’s take a second because I don’t want to. I don’t want you to get out of here without talking about your experience with launch. Casey, what brought you to that? Like, how did you even find out about it? What did that process look like signing up? And what were the tryouts like?

Roger Ngo 26:47
Yeah, so you know, we were really excited when we heard about this program coming back, we were the first post pandemic, class or group of people that got that award. So that was really exciting. So you know, I’m in Kansas City, and I’ve been here for over 35 years. So to be given an award and a grant from an Economic Development Council, we can city, downtown Kansas City and Launch KC. You know, I’m extremely proud about that opportunity. And so and so it’ll be applied to that process. We went through a pitch in an interview, and successfully went to the next round. And I don’t know, man, I think there were over 140 applicants and six or seven companies got awarded the grant. So that was really exciting. And then, you know, as part of accepting the award, I think we came here, Startup Hustle, and you helped us with our pitches a bit. So that was an exciting opportunity, as well. So we had a lot of fun doing all of that. And the best part of this whole thing is being able to help grow our business, get some marketing out there, have the support of the community, so that we can help drive economic development here in Kansas City that you know, the city that I truly love.

Matt DeCoursey 28:08
Yeah, so Vonage que si. And I’m not sure if you even know the history of it. So they used to give grants like the one you got. So those are $50,000 grants. I think that’s what you guys did, right? Yes. And then they pivoted at one point, or a couple years ago, this is pre pandemic, and then even during, they pivoted to, it’s still doing the 50k amount, but they partnered, they had a corporate partner that would have also made a small investment in companies. So we and Full Scale and Startup Hustle have been involved in that for years. And so they, you know, at one point, they did clean tech, and they can help Tech was one of them. They did a number of different things. And now we’re back to the grande side of things and so where are we involved? Well, that’s part of the like, how we like to support Kansas City to help put these programs up and promote them. And then one of the things that is just fun, and that’s new, so you were the first class of the elevator pitch people, but part of that is when you go through an accelerator program, and congrats on making it because there’s a lot of competition for that. And by the way, for those of you listening, there’s something like this near you. These things go on all over the country. There are all kinds of accelerators, and this is economic development. And so where does the money come from, money comes from the government and also comes from donors and people like that. What’s the purpose of it because we want to start and spark new businesses and its economic development. But one of the things that comes with that is Demo Day. Demo Day is stressful for a lot of people because they’re gonna put you on a stage in front of what there’s probably 200 people there, the mayor, a bunch of other people and fasters supporters. Like, who knows, and they’re like you have one minute to effectively tell everyone why you’re awesome. And what you do. And one thing that we noticed over the years is that, like I said, that’s a source of stress for a lot of the participants. So having some expertise in the area, that’s part of where we’ve partnered with them more. So Startup Hustle at this point was, yeah, so Roger, and several other grant recipients came to our office, and we sat down, and they got to deal with me for an hour now. Which, depending on who you ask, is either a joy or a tragedy, in some regards, but say, you know, hey, let’s hear it. And what I found is that, I don’t know, I thought we, all of the participants in that came in with one approach and came out feeling better about it. I remember there was one, there was the professor. And I asked this lady, I said, Well, tell me what you do, Sir, give me your one minute pitch. And she got slightly under that. And I said, stop. I said, Hi, I didn’t understand anything. You just said, I’m not smart enough. And because she was talking way over my head, I don’t even know neuro linguistics, and I don’t even know you lost me right there. So I think the key thing from that whole training session was get out there and just lead with the need, tell people like immediately what and get their attention, and they’re then there, and then they’re actually on the hook, and they’re going to pay attention. So you did great. Participants did great. And I thought that was it’s fun to participate in that. So did it have anything else matriculated from the press or the promo or the introductions or any of that? Has that led to anything else?

Roger Ngo 31:53
Yeah. So you know, we’ve made all sorts of contacts. So you know, I want to elaborate on the Launch KC program a bit. So you get that $50,000 Grant. But in addition to that, we also got free office space for years. So we’re down at the Plex pod flash cube, with all the other land Launch KC award winners, we’ve been collaborating with them. We’re working with one right now. And you know, fingers crossed, we turn that group into a customer. So we’re making these connections and working closely with these people and all trying to help each other because there’s a mutual interest where we can help grow their business, and then they can help us grow our business as well.

Matt DeCoursey 32:29
What’s been the hardest part about that whole thing? Everyone has a different answer to that.

Roger Ngo 32:34
The most difficult part of this, all of it is like, well, I don’t want to throw you under the bus.

Matt DeCoursey 32:37
But before we came in and recorded it up, well, this is the first time I’ve ever been on a podcast. And sometimes I find that actually stuff like that is more stressful or difficult for people, then you’ll look back at this in a few years. And you’ll be like me, I’ve done it before. Yes. And they’re done.

Roger Ngo 32:59
Yeah. So you know, I’m a pretty technical person and an introvert at heart. But I also understand that it’s important to get uncomfortable, because when you get uncomfortable, that’s where you can expand your comfort zone. And the bigger your comfort zone is, the more that you can kind of give back to society and other people that you’re coaching and mentoring, just kind of like what you’ve done over time is, you’ve done well for yourself. And now you’re helping other people. That’s what I would like to do going forward as well. So I’m really enjoying all the potential the programming that LaunchKC’s has been preparing. We are going to have a LaunchKC day next week. Next Wednesday is the plan if actually, it won’t be next week because the fault in a day that this can be the Super Bowl cheese parade that she subrule parade we’re gonna have already occurred by the time this comes out.

Matt DeCoursey 33:46
So yeah, all right, congratulations. We hope you made it. And yeah, we have the Chiefs. I’m gonna be on a plane from Taiwan to experience severe SETI during the Super Bowl, because I planned really poorly this year. So people keep asking me if I’m going to the Super Bowl. I’m like, No, I’m not even gonna watch the game. I don’t know that. Well, maybe I’m gonna probably stream it online. Yeah, with airplane Wi Fi. Who knows? You know, so, yeah, that you know, I love these programs that you know, you mentioned Launch KC and all that stuff. I think that, you know, so like, when we do the elevator pitch thing. I mean, the thing that I found is just wraps. I’m gonna give you feedback. I think the most valuable thing that you can do for businesses and entrepreneurs in an early stage is not to be a yes person. Like, sometimes and and you know what, sometimes this doesn’t always come out the way that comes off the way that you intended. But sometimes you just have to tell people you’re like, Hey, okay, like well, like you mentioned the professor. She sent me a very endearing thank you note. After that, and after the presentation and said, you know, thank you so much because I’ve been trying to unwind this. She’s like, I’m a professor. And I can teach a whole class, but this isn’t really my thing. And some of that was like forcing some reps in there. You mentioned the uncomfortable part. Yeah, I’m gonna, I’ve been making people uncomfortable here. So yeah, but but with, but that’s, but that’s a good thing. And, you know, I think that that’s one. So here in Kansas City, we have this term called Midwest nice. That isn’t intended to be complimentary, in a lot of ways, because people will tell you, Oh, yeah, that’s great. And it’s not always what people need to hear.

Roger Ngo 35:37
Yeah. So I think candid feedback is one of the best gifts that you can give anyone and, and my partner, Tim and I, we, we give that to each other. And I go into it with the mindset of, you know, sometimes you get by the pleasantries, and you just get right to the point. And it’s no hard feelings, because we’re out there trying to solve a real business problem for people that want to monetize their data, bring in more money. That’s what we’re going to do, and no hard feelings. At the end of the day. We’re really good friends, we’re partners, we’re going to grow this business. And so part of that is just the execution part, you know, portion of it. That’s what we need to focus on.

Matt DeCoursey 36:11
I mean, that’s, that’s a key ingredient. I think that you have well to, if you’re going to give it you have to be able to receive it as well, which is I mean, here’s the thing, no one wants to hear their baby’s ugly. But sometimes it’s what you need to hear. Yeah. And I don’t walk around calling actual baby’s ugly. But if people come and ask for, for input or advice, I sometimes I actually will disclaim it. I’ll be like, Okay, remember, you asked, you know, and like, that’s the thing. And that’s, I have a couple of rules that go with that. Well, one, if I’d prefer that you don’t end up upset with me, because I tell you the truth. And second, I’m not gonna sit here and argue with you about why you’re right. And I’m wrong. That’s not the purpose of the feedback, right. So I’ve run in May I, I’ve been, so I’ve, I’m known amongst the people I know. Like, I’m the one that gets that call, like, they will start Hey, man, I’m calling because I know you’ll actually tell me, God, here we go again. But I think that that’s really valuable. And sometimes the best advice you can get from another entrepreneur is don’t proceed. Because or do it knowing that you’re likely going to run into these five or six obstacles I have people be like, Man, I didn’t even think about that stuff. And these are just that’s just, I don’t know, man, I’m turning into an old man. Maybe I’m like that with that. There’s I don’t know, I think the main thing, like why we do this podcast is one I enjoy these discussions. And two, I think that knowledge isn’t meant to be kept. It’s meant to be transferred. And there’s no tangible way to analyze that data. Roger, like, how many people have listened to this podcast over five years, and, you know, soon to be 5 million downloads. And thank you everyone for that. You know, some of that is like, I don’t know, there’s a ton of people out there worldwide that had have been Oh, and then they don’t do it. And they don’t fall down the money pit. Or they don’t do something stupid. We’re here to try to help you not be stupid. Yeah. Because we’ve been really stupid ourselves.

Roger Ngo 38:19
Matt, we don’t live long enough to make all these mistakes ourselves. So we try to learn from other people and go apply it. And there’s nothing better than all the entrepreneurs and the mentors that we’ve experienced here in Kansas City that have helped us along this journey.

Matt DeCoursey 38:33
Yeah, and that’s, and that’s the key. So you gotta, you know, there’s, there’s a whole lot to be said around that. All right. So I like to end my episode Startup Hustle when I’m having conversations with founders by doing what I call the founders freestyle. So I’ll give everyone a chance for your closing remarks. The number one comment I get when recording, especially here in the studio, is, wow, that went fast. And there’s usually something I don’t know. You get to say whatever you want. What would you like to say to everyone on the way out of today’s episode?

Roger Ngo 39:03
So, first of all, that did go fast, Matt. And so most importantly, so as a person out there or as a business that is collecting data for your own purposes, consider using DataAppraisal to help value your data because right now, that asset is just sitting there collecting dust using it for your own purposes. But there are people out there that are interested in your data, and they can use it for some very good things. So let’s not hold that close to the vest. Just like Matt likes to share his lessons learned with other people that share our data to kind of help make the world a better place.

Matt DeCoursey 39:39
Yeah, I think there is a negative connotation that comes with data and privacy in some regards because, quite honestly, it has been abused by a lot of people in the past, and there are a lot of protections in there to change stuff like that. And, if data isn’t valuable, well, here’s the thing. It’s been used to shape elections. Frickin valuable. And I think that if, you know, sir, a lot of people watch Shark Tank, and Mark Cuban is so well known, if you don’t know your data, you don’t know your business. That’s true. That’s true. There are a lot of things that you can do. If anything, just kind of analyze it yourself. There’s a lot of tools out there that, you know, can, you know, can make, I’m talking like some of the simplistic stuff. Like I talked to people that don’t have Google Analytics on their websites. I’m like, that’s like, that’s the first thing. First thing, right? It’s the sum of that, and you know, and then when you get into more, that’s the first step and progressing towards having valuable data that companies like data analytics can DataAppraisal.ai can help you with, and once again, there’s a link in the show notes. So hey, wrap your arms around the data, people, because it can be useful. Roger, I’m gonna catch up with you down the road.

Roger Ngo 40:51
Okay. Good.