Dave Kelly 3:50
I’m looking forward to the conversation with Luke. Hey, Ro, before we pull Luke in I want to give big props to a past guests of ours go for it. Jim Keyes Hey, Jim, shout out to you man, former CEO of blockbuster and the great 711 Convenience Stores he’s adding to his accolades. He wrote a book recently called education is freedom he started the education is Freedom Foundation, DNI. Coincidentally, we kind of grew up in the same part of Massachusetts. So we had some fun banter side, side by side on that we learned he was a pilot, Super Down to Earth guy. I definitely recommend that if you want to learn a little bit more about education, being a leader and not being the victim of a poor environment. You know, don’t let that hold you back. It was an inspirational story, Jim, I loved everything that you said. conversation, and I recommend that people get out there and check out that episode. That was a lot of fun.
Rolando Rosas 4:45
No doubt, no doubt. Big props to Jim Keyes. Thank you very much. We appreciate it. Your insight. So let’s talk about our guest Luke Komiskey. hopefully I’m pronouncing that correct and if I’m not when you come on and Luke correct me so Luke is the founder and CEO of DataDrive a data consultancy providing managed analytic services with an ongoing partnership data drive helps growing mid sized organizations transform their desperate messy data into insights. And we know all about that Dave, working on Amazon. There’s a lot of data all over the place. And it can be messy after writing both the reporting platform and data team, which is actually the important thing because if you don’t have the guys and gals to look at that data and give you know what’s happening, we’re in big trouble now the data team for delivering faster informed decisions. Over the last seven years, Luke has grown data drive into a worldwide team of highly skilled data professionals serving over 150 organizations, including media agencies, public schools, manufacturers and other companies. Let’s welcome to the show. Luke Komiskey. Thanks for having me.
Dave Kelly 5:54
Hey, how are you?
Rolando Rosas 5:56
Thanks for coming on today. Where are you checking in from? I
Luke Komiskey 6:01
am calling in today from my home state now of Minneapolis, Minnesota.
Rolando Rosas 6:06
Yay. Big props for those who can endure the six to eight months of winter in Minnesota. My old home state I used to live there. I went to school went to college there my brother still calls Minnesota home. And boy, I don’t know how he does it. Because He’s way up there in Duluth, he can almost he can see Canada from his house.
Dave Kelly 6:26
I thought you’re gonna say Santa Claus
Rolando Rosas 6:29
to steal somebody’s thunder who could see places from their house could see Canada from his house. Not literally, but I guess figuratively. So Luke, thanks for coming on. We appreciate you coming on to drop some knowledge today. But you know, before we get started today, I definitely want to jump into a conversation. You heard Dave and I talking about, you know, data and AI, and what role it plays a or do we have a clip to get Luke’s reaction on so we’ll we’ll show you a clip and then we’ll talk about it on the other side. To kick things off.
Guest Speaker 1 6:56
Microsoft briefly took over apple on Thursday as the world’s most valuable company. Throughout Thursday’s trading on New York Stock Exchange, Microsoft surpassed apple and multiple points, including when its value reached at 2.8 7 trillion US dollars. And Apple shares fell by 1%. But Apple reclaimed the top spot as a trading day wore on. It is the first time that Microsoft had taken over Apple since 2021. Observers say the stock price search was led by the company’s cloud computing services and investment in AI sector mainly open AI, the developer of chat GPT.
Rolando Rosas 7:35
What do you think about what you just heard? Yeah,
Luke Komiskey 7:38
it’s it’s pretty incredible. I think it is. For me, I see that is a testament of Microsoft’s a forward looking and seeing what AI was going to become and has become for the evolution that’s going on. I think so many of the moves that they’ve made with being an early investor in open AI and even be involved in the drama that happened last fall with open AI. Drama man. Yeah, man is like probably at some of the most intense, intense like two, three days of like tech drama that you can ask for us. It was pretty incredible. But yeah, it’s it’s it’s amazing to see that I think the types of investments between Microsoft and even throwing a video in there is that this whole AI revolution is here to stay. And for me, I’m super bullish on what AI is going to provide for organizations and just completely change how companies operate. And selfishly. What I love about AI is that underpinning all of this is having a good data foundation and making sure that you’ve got sound data that’s actually going to give you meaningful insights versus blatant lies,
Rolando Rosas 8:38
no doubt. And what’s the danger here? I mean, the other side of it is that there are still companies on the sideline, both small and large. But we know that Amazon is throw so throwing money at the AI game billion dollars as well, Google, which we would have thought they were going to be the first one to launch a generative AI type of product. They got caught. I would say like when I say to my son, you got caught sleeping, they got caught sleeping, and this smaller company open AI with the money from Microsoft, leapfrog them in some regards. What’s the danger here for those companies that say no, we’re just going to stay on the sidelines and a little longer before we even touch it?
Luke Komiskey 9:22
Yeah, I think I think the big danger right now with like the AI evolution is that we are still like on this growing curve of like, what are the possible use cases we can use for a I am not totally appreciating maybe the hard work that needs to be done behind the scenes for this to actually work at scale. I think for me, like you see Chad GPT kind of hit the world that goes crazy from everyone, the world’s playing around with it. Last year, a lot of the big tech data vendors are starting to advertise all of these generative AI solutions that they’re going to be throwing into their tools. And I think we’re still just like on the up and up of the popularity curve. And I don’t think we fully have the ability to see like what are What is like aI going to look like after it gets really settled in and we understand more of the nuances of it, I view it a lot like how when the internet rolled out, people got excited about all the potential use cases for the internet. And then we kind of hit this like a trough of, alright, these are some of the short shortcomings of what the internet can provide for us. And then we eventually settled into one of those standard use cases that many different organizations can start to participate in. So there is an advantage for organizations to jump in early on this AI train. But it’s the Your mileage may vary of this is going to be a very fast moving platform. And you know, some of the investments you might make are going to be probably more bets, then most companies are used to
Rolando Rosas 10:37
Wow, no doubt. And as you say, I will use your words get things may get overhyped internet was overhyped. There are companies that are no longer with us from the.com age, and it was a boom bust. But and made some really bad bets early on. I’m sure that when we’re looking at, you know, where the bets worth, we placed the bet. So I want to jump into the bets for you or do we have anything with with bets that we can talk about with or roll with Luke here?
Dave Kelly 11:20
I don’t think that was
Rolando Rosas 11:22
the Packers fan. It shouldn’t have been go pack go.
Luke Komiskey 11:25
Yeah, 100%
Guest Speaker 2 11:27
tech behemoths alphabet and Microsoft with the focus largely on AI, what I suspect we’ll see with generative AI is that it will be a richer, more engaging experience for the users on one side and the advertisers on the other. And so I think there’s potential to grow the pie and continue to expand search. Ultimately, it’s about driving outcomes for all the participants in the search platform. And so while competition is going to remain fierce, I think what we’ll see from Google this evening, and over the next year, is that searches durable, and ultimately, if they can reinvent it, as they’ve shown that they can historically, I think the business and the broader search platform at Google will remain vibrant with good growth prospects.
Rolando Rosas 12:14
I know we’re playing we’re goofing on you a little bit here. Given that you’re a big Packers fan. You’re from Wisconsin, we slipped in a little skull skull skull for those that are from Minnesota. Yeah,
Luke Komiskey 12:25
it’s alright. It’s a citizenship requirement of Wisconsin to be a Packers fan for life.
Rolando Rosas 12:32
It’s a deadly rivalry, you know, Vikings and packers and the poor souls that live in Minnesota that are Packers fans, but I guess this year, they got the last laugh with a lot of love from Jordan love. Yes,
Luke Komiskey 12:46
yes. exceeded expectations. So pretty, pretty excited for the years ahead.
Rolando Rosas 12:50
in good hands, man, I guess the Vikings back to the drawing board who knows what happens with with our quarterback there, but I digress a little bit just to just to poke you a little bit since you’re a Packers fan. Appreciate it. But seriously, on the on the on the outcomes, this is where I want to I like picking the brains of folks that are in the data side. And that in some regards, may have some exposure to AI, you know, even if we get all the data we need. The the what I see is the the leap, the LEAP we really need to make for business decision makers and others that are taking decisions on data is then what kind of outcomes can we get from this data. So I’ll give you an example. We play heavily in the Amazon space. And we we buy a lot of ads from Amazon to push the tech products that we sell. The disconnect here is that a on one side, Amazon themselves gives you their recommendations, but their recommendations are what’s going to drive money for them impressions, showing product to customers. It doesn’t necessarily drive revenue and ROI. But for them it does. On the other hand, as as the business owner or a decision maker or or media agency that’s looking at this information for your clients, you want to make a decision that’s going to drive revenue, that’s going to be ROI positive, that’s going to drive sales, not just clicks. And right now, the data doesn’t give you insights into saying, hey, media agency, your client is seeing better revenue when they do this because we’ve found a pattern on what they’re doing or clients or, or customers are responding. This is where you really need to place your bets. Sort of like what AWS is doing with the NFL, in that there’s a 70% chance that you’re going to make a touchdown when you’re when you’re throwing it at the 10 yard line and you’re throwing it to saying I’m on raw or your Jordan love is throwing it from the right side of the field. There isn’t that data and outcomes probability yet available within this example, let’s say advertising
Luke Komiskey 15:00
Yeah, and I think I think the biggest challenge with like those types of use cases is that Amazon probably is only likely very privy to the results that it can see, or it’s trying to drive. So things like impressions and clicks. Oftentimes, like, if you’re trying to train an AI model, you also need the outcome data that Amazon may or may not actually be aware,
Rolando Rosas 15:19
they, because they own the E commerce platform in this case, so they know what click drove the buy, right? That’s the journey St. Jude
Luke Komiskey 15:27
does with Amazon. In that case, no, like the cost of goods sold to like the profit to the customer, or your customer.
Rolando Rosas 15:33
They they don’t know the cost of goods, but they look they have their own worth the cost of the ad to generate the sale? Because we don’t know the cost of the good. But the business owner can can can they have that information that the advertising agency get that information? And say, Yeah, this is what it costs, you not just run the ad, but this is what the cost of goods is in relation to the the sale that you got, because you had to run 10,000 impressions before you get that one sale? Yep. Yeah.
Luke Komiskey 16:00
And I think it’s kind of the two challenges that I think of with AI is, is one making sure you’ve got the whole end to end data, I think some of the big challenges that we see with the media agencies we work with as your AI, it outside of like a customer isn’t privy or maybe the agency is not privy to the entire journey. So like how many leads that led to how many sales that led to how much actual profitability It drove, it’s really hard to connect all of those individual touch points, because attribution is already tough. And even if you had attribution, the underlying AI model might not have all that information. But the the second challenge is just having enough data to actually make a decision, right? The NFL is, of course looking, you know, in the NFL example, they’re looking at tons of different or many years of information about like, what is the right play to have happen, like to have to occur yet. But there’s also the evolution of the game, or the strategies of today’s football is quite different that if you were to run it kind of back in the 1990s 98 1980s, it might be a different outcome. And those are some of the challenges having a big enough data set, and then also being aware of kind of like how far back does AI need to look to try to make the best recommendations of a changing market? Right? That’s, it’s super tough when you talk about marketplaces like Amazon, because all of those factors are constantly changing. So maybe it’s maybe their model is trying to play on the safe side of let’s let’s lead to impressions because we can look at more near term data that we get to play around with.
Dave Kelly 17:25
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Rolando Rosas 18:26
guys. Dave, thank you very much for letting us know about that. And we pick up the conversation again with Luke and about data. So you’re saying that attribution obviously is messy because we we like here on this podcast, we know that entrepreneurs and different people listen to us. And they will go and buy on our Amazon store, or they will pick up the phone and call us or and want to do a project or consultancy or whatever. But the attribution doesn’t link this podcast very well back to that phone call back to that Amazon store. And Amazon does have attribution. But it’s also again, it’s messy, because it may not know that this is where the journey started. Right? They may have heard about us and oh, yeah, we were on Amazon or yes, we have our own website. And the attribution picks up halfway, essentially halfway in the conversation of the data journey.
Luke Komiskey 19:18
Yeah, yeah. 100%. And, yeah, it’s because of that messy attribution. And also many times that customer journey is taking place between a lot of different datasets, a lot of different pieces of software. I mean, that that is really where the value of a centralized data system really starts to come into play for organizations that want to potentially leverage a large language model LLM in their own use case.
Rolando Rosas 19:43
Yeah. Let me ask you about that because I’m gonna stick with the the social media and media agency said for a moment, let’s say you’re a media agency, and you’re advising clients that are on multiple platforms, you know, you’ve got your Instagram, you have your Facebook, you’ve got this tick tock, you’re trying to leverage the data that’s available, obviously all have their own datasets, is it possible to take data from all those places, combine them and have AI, or data tell you, this is where you’re winning. Put some bets here, there’s a prior probability you’re going to win here, versus over there.
Luke Komiskey 20:21
Yeah, that’s absolutely where AI can really start to play a role. I mean, the first challenge for many organizations is bringing all of those various platforms together. And then it comes down to more of the data modeling of making sure that you know, each each social network, each type of advertising channel might have a different way that they’re either measuring impressions, or trying to break down by various cuts, like markets and dma’s. And so all of that goes into really the data modelling behind the scenes of you got to pull that data in from disparate social media channels and traditional media channels, but then also clean it up in a structured way. Because AI is one of those things where you can kind of view it as like a printer, you need to like feed the information, but it needs to be all well structured, complete. And this is where like those types of attributes around data like isn’t, isn’t necessarily a common thing within small companies, or just any size company is is there’s a lot of gaps in the data, there’s not processes being followed. There’s not even consistency between these social media sites to be able to feed the data into something like an AI model to be able to output some of those results that people are looking for. And I think that that is where like aI fundamentally is going to highlight the challenges we have had forever, just nobody wants to talk about with data governance and data quality.
Rolando Rosas 21:40
So we need some standards. And I know that it’s especially on the when you’re talking media companies on social media, social media, in particular, they they have their own definition of like when impression, YouTube versus Instagram versus Amazon, and that the standard should be. So there should be some standards so that you can make those comparisons if you’re an advertising company, or you’re a client of an advertising agency, and they come back with this report and say, Yeah, on tick tock, you’re making a million impressions on Amazon is 10 million. But it turns out that an impression on tick tock is half a second. And on on the other platform is a three second view, right?
Luke Komiskey 22:22
Yeah, yeah. And it’s those difference in definitions. And like this is where that combination of AI and human intuition is always going to have to coexist. I think AI can make recommendations. But then there’s also layering in good actionable insights that a human can look at to say, stats like cost per click cost per impressions, I can layer in my understanding that tick tock is measuring this completely different to make a more holistic recommendation for a client. So this is when I hear all these like doomsday conversations around AI is going to take all of our jobs. I view it as if you’re if you’re a marketer, it’s it’s the marketers that are going to be using AI are just going to overtake the jobs of marketers that aren’t leaning in on AI, it can make your job.
Rolando Rosas 23:05
All of us I can Luke is that to me. And and we haven’t done one of these in a while. But I like that because that sounds like a pro tip, or give us a pro tip, get ready for a pro tip. Look, tell me why AI is not going to completely take over all the jobs.
Luke Komiskey 23:24
Because fundamentally, AI is only going to be able to be as truthful as the data that’s being fed to it. And there will always be an element of human intuition that has to come into play. And so for me AI, using that marketers who are able to leverage and lean in on AI and make decisions with it in play, will be able to overtake marketers who are completely putting their head in the sand when it comes to AI applications.
Rolando Rosas 23:52
Well said, I heard Mike Margolis, pay this off, he was being interviewed by Chris doe. And he said, to add on that he said this, there’s three things that marketers, businesses, or people will always need, no matter how good AI get is the ability to read, write and speak. Because with those three things, you can command the AI, you can make it do better things than what you’re doing today. And they just doesn’t always get the language, the nuance, the understanding that a good storyteller can tell. And what’s the difference between a good story and a great story. And AI doesn’t yet have the ability to give you a great story every time you want it to give you a great story or write a story for me or something around content. It could do some of it, but that doesn’t mean it’s going to come out with a great result.
Dave Kelly 24:47
And Luke right, they were you know, a lot of people are saying you know, it’s this doomsday. It took your jobs, it’s gonna it’s gonna come and it’s gonna take everyone’s job and we had a guest on I think it was Kevin King. Who said, AI is not going to take your job, you might lose your job to someone that knows how to use AI really well, but it’s not going to come and just, you know, take your job. We had an article that we were looking at earlier today, and it was from Forbes, and it was about data analytics, you know? And will it be automated by AI in the next few years? Or it? Can you? Can you pull up that headline. And I think this was actually written. This was written in February of last year. So this is about this is about a year old here. So I’m not sure if you had seen or read this article, whatever come out. But how do you how do you see AI? Strengthening businesses like yours?
Luke Komiskey 25:48
Yeah, I think the big thing when it comes to AI applications, when it comes to data analysis, I think what I’m already seeing just from being consulting in the spaces that a lot of companies are trying to either write write code, or do analysis by, you know, feeding it, feeding their data into some kind of AI model to be able to analyze it. And again, I think much like the marketing discussion we had, it’s AI can absolutely play a role in finding trends that might have taken a human, many different iterations. If you think about pivot tables that they’re doing, to try to find those types of insights. It can it can handle and automate all of that. And I think that’s a benefit for everyone, right? Imagine doing pivot tables a million times faster than you’ve ever been able to before. But, but ultimately, there needs to be a story of, okay, this is what’s happening, this is why it’s happening. And this is what we need to do about it, that AI is just not there yet. And I just don’t I don’t see a world where it can completely replace that, at least not for the foreseeable future, where it can actually make recommendations that another human is going to have to trust to make that type of action.
Rolando Rosas 26:57
Yeah, and if things have gotten a lot better, in the year, that chat GPD was launched to today, you know, we’re a little over a year now into it. And the the leaps and bounds have been exponential. Now you can get images and thumbnails and all the rest. And now there’s some video being created by AI. We’ve been testing some of that it’s not great. You know, it’s okay. Not great. It could do some editing, it’s, it’s okay, again, okay, not great. It misses some things, including even on the images, you say, Hey, give me a, I want you to generate an image of a person sitting in a cafe drinking coffee, while they when they holding the cup. They’ve got six fingers, holding the cup, right? Or missing a couple of the other ones. So it’s some reasoning that’s really seems so obvious, right? But somehow it’s either missing the finger altogether, or adding too many.
Luke Komiskey 27:52
Yeah, yeah. And I’ve I don’t know if you guys have seen that product where now you can actually add a sixth finger on your hands. So if somebody were to take a picture of you, you can claim that it was aI generated, it’s like a fake six finger you can draw get. Oh, is that right? Yeah. So that’s, that’s a way to like maybe Cover yourself from you know, any kind of accusation of what you’re doing in real life. It’s just grab the six finger for now.
Rolando Rosas 28:15
There you go. Whoever’s got that he’s gonna do really well. Six finger person, six finger company. Hopefully, you’re out there mobile fine. Yeah. But yeah, it’s amazing where we’re going with this, I want to ask you to also clear up a word phrase that, you know, for me, just sounds, it can sound spooky, or it sounds just mysterious. What is machine learning?
Luke Komiskey 28:45
Yeah, machine learning is another another trendy Fei phrase that’s been around forever. But I would say over the last decade, that’s been been a big thing in the data space. And in a lot of ways, like artificial intelligence, machine learning. They’re all coming from the same family of phrases. But it’s the it’s the idea that a machine or a computer behind the scenes can be given inputs and start to understand what kind of different paths that I can take any different decisions that I could take? And what are what are the types of outcomes that are going to happen from it if you kind of view as, as life or any kind of decision as just a maze of yes or no, I did this type of action. Machine learning really simply is just doing the algorithms behind the scenes to help figure out what is the best path that I could take that can get me the best predictable outcome. It’s the same stuff that’s happening when AWS comes up on an NFL game and says, Yeah, you know, 80% of the time this this will turn into a good outcome for the team is because a machine is able to be able to take past experiences, and then run that through the algorithm to say, alright, this is the decision that I would make in machine learning takes it a step further so that a machine can learn and almost it can make that decision for you so that a human doesn’t need to be involved in saying Yes, sir. No.
Rolando Rosas 30:00
So really what I what the question that was I was getting back earlier, is businesses need more information from machine learning in the same way that the NFL or in the teams in the NFL are getting data to get predictive outcomes that 80% likely that this would be a touchdown, or 10%, or zero was near zero in near impossible that you’re going to score 10 seconds into a game with a Hail Mary. Yep. Yeah. And then,
Luke Komiskey 30:27
you know, to add to that, I think another challenge that I have as a data practitioner is even if you do all the machine learning in the world, if you don’t have a solid communicator that’s describing the black magic that’s happening behind the scenes to these business leaders, they’re not going to going to take that action, or they might trust their gut over the data. That’s always a common challenge of anything, when it comes to the business world as people will always overemphasize their experience versus what the data is telling. So even if you have a machine learning algorithm that is doing amazing things, the next challenge I always run into is just the pure adoption, or at least a high level data literacy to understand what the decision was and why it was made. So that you can feel confident taking the action that needs to happen in the real world.
Rolando Rosas 31:14
I guess we’re gonna be with this bias of humans versus machine machine learning for quite some time. Because I mean, up until last year, I would say most people never either understood or had any experience with an AI generated piece of information. It from firsthand, you know, company, like you said, machine learning has been around for a while AI has been in the background for a while Google’s had it for a while Facebook’s had it for a while, but the way it’s on the front end been unleashed. I think people’s experience and comfort level is going to take several years before they 100% rely on what’s going on behind the scenes. And that’s the other piece that I wanted to get your opinion on, when it comes to AI. Platforms like Amazon, are starting to use it in a way from a seller’s perspective. And from my point of view, as someone that’s selling goods and wares on Amazon sees that the platform has been changing, for example, you used to see all the reviews, four or 531, all of them, they’re still there. But they’re hidden under a search. And now what Amazon’s offering up is a summary of all if you have 1000 reviews in in a summary in a paragraph or two. And it doesn’t always seem to get it right. Or the proportionality of the written information is not commensurate with the reviews. So for if, for example, you have 1000 reviews and 97% are good reviews. It puts one good paragraph and one bad. And the implication there. It’s it’s 5050. So my question to you when it comes to the way Amazon is evolving with this data, and using it and deploying it? What’s the potential impact as they migrate away from pure SEO and text based heavy searches that are on there to a more preferences, AI based type of experience? What kind of impact would that have on businesses that are selling on these types of platforms? Yeah,
Luke Komiskey 33:24
I think I mean, I think the first big challenge that I hear is just the the, the I guess the Trump like the trust and what that data is actually going to output. It’s it’s having faith that if you’re aggregating this information, I think, humans naturally, this is why spreadsheets continue to exist and why Excel is the best BI tool in the world is I think everyone feels much more comfortable when they have all of the individual pieces of data underneath it all. And they might, they might not actually want to dig through it. But I think the biggest challenge that I think AI is going to have in any kind of application is when I’m trying to aggregate this up, what is the right way to describe it to somebody in a summary, but then also maybe understand the more nuances of the human language too. I think some things that I like even run to run into as I’m doing natural language processing for reviews is I come from the Midwest. So we are all about sarcasm. And so there’s a lot of words that we use are like, incredibly, they’re like this is sick, or this is gross. And it might actually mean the opposite of what it’s what it’s trying to say. And so I think some of the challenges that we’ll continue to see, especially when it comes to like review type data is understanding more of that human nuance of it. And having faith that if if a company like Amazon is going to aggregate that up to maybe make that an easy solution, what is the user interface that they’re going to create? That’s going to make it easy for me to maybe test that judgment against some of the granular data underneath? I think humans are going to always be yearning for that for some for some amount of time, because we can’t fully trust the machines to like aggregate this up right All right.
Rolando Rosas 35:00
And do you think the future though, you know, there’s a lot of discussion among the different folks that I know that are also selling on Amazon, and different Amazon sellers, and I’m talking about Amazon, because they they are, they’re pushing a lot of a lot more AI tools in the background that nobody sees. And I’m back end, and they’re rolling them out. And they don’t really tell you that they’re doing. But you can see that. And one of the fears is that, what we’ve known as marketers, as business people, for the last 10 to 15 years on how to be successful when it comes to these types of platforms, whether it’s ads, marketing, or selling products, that model may not work anymore. And so, from your side of things, you’re in the data, do you think that companies like Amazon, are just going to leverage it? Because it makes dollars and cents to them? And marketers and businesses are going to have to adjust? Or do you think they’re just going to go on a bit of very slow approach to allow everybody to get used to it, whatever? What’s your thoughts on that? Yeah,
Luke Komiskey 36:01
I think I think we’re gonna definitely see my prediction of this is that these companies like the Google and Amazon’s will lean in as much as they can to help promote the outcomes that they’re looking for. And I kind of say that as a guy that has been digging into SEO a lot with laying the Google algorithm over the last two, three years. And the challenges that we see nowadays is that it the, the algorithm and the AI recommendations of how their search is going to work just much the same like Amazon. They’re in the they’re in the business of making sure that people are spending advertising dollars. And so if they can, if they can create an AI search or AI recommendations that are going to help drive more advertising dollars, I mean, it’s hard to believe that a multi trillion multibillion dollar corporation wouldn’t go the route of wanting to promote outcomes that are going to increase their revenue. And this is kind of the nuanced gray area of AI, right is, is they’re not technically telling you the wrong data. They’re just using AI to help drive I think an outcome that’s going to be more beneficial for them. And I can’t really fault them for it. But I imagined that I was
Rolando Rosas 37:10
investing it right battle. Yeah, 100%. When when we look at this, and we’re talking to other business owners in the space, and other entrepreneurs as well, one of the things that I’m always asking is, what are we missing here? You know, we all I know what I know, are unknown. Maybe it was Donald Rumsfeld that said, we don’t know what we don’t know, or something to that effect. Right? And what is that? We don’t know? What are we missing right now? Are that when you talking to different businesses, and you’re consulting with them? What are we missing here?
Luke Komiskey 37:52
I see the biggest challenge of what I do with helping small midsize companies try to get a handle on the data that they’re working with. I think people are very excited and are nervous about how AI could play a role in helping create that competitive advantage for them. What I don’t think people appreciate is that there is a lot of foundational work that needs to be put in from the data perspective, that, fundamentally a lot of these businesses across America are still running on spreadsheets and humans doing a lot of their manual magic to make that work. And that’s not going to be able to scale in an AI driven world. And so my plea to business leaders as I helping educate on what they’re trying to do is a good AI strategy has to start with a sound data strategy, you can’t skip that step, because AI will always find a way to confidently lie with whatever data that you’re giving to it. And more than anything, I think AI has to be built on a foundational, good data strategy that also is bringing together all of these disparate applications into one central spot. And it’s a foundational piece that just cannot be skipped.
Dave Kelly 39:00
So Luke, when when you’re working with businesses, you know, what’s one thing that a business should never do when they’re starting to analyze data that they’re starting to dig into this? They’re coming to you? There’s a lot of advices a lot of things that they should do. But what’s something that they should certainly avoid, at least in the beginning?
Luke Komiskey 39:17
I think the things that they it’s more about inaction than anything, right? I struggle with I think, people who are trying to do that first step of trying to analyze their data, it’s a necessary step to maybe start in spreadsheet land and pull in what is interesting to them. I think the biggest challenge I have with business leaders is that they’re probably likely not dreaming big enough about what are the data questions that can actually move my business forward. And more than anything, is just an education of recognizing that things like automation, AI, a lot of the modern reporting platforms that we have out there can unlock unlimited potential within the data. And I think the biggest thing that people struggle with is just not dreaming big enough or seeing An opportunity to maybe even give back time to their leadership back back time to their teams to not be stuck in Excel spreadsheets that if your Sunday night is full of downloading CSVs to get ready for a leadership meeting the next day, that’s not a good use of of your team’s time to pull that into place. And, and dream a little bit bigger about what today’s technology can actually unlock for your business.
Rolando Rosas 40:21
Let’s pull the curtain back on that I love that jumping off point about not dreaming big enough. Because then you’re telling me I’m in a facility that’s dark, and somebody’s giving me the sample I love using this this that I’m I’m in, I’m in a huge warehouse, the lights are all off. So it’s dark, numb with a flashlight, I’m only seeing what the flashlight is seeing. Maybe I need a big huge spotlight, you know, and give me a bigger frame of reference of what’s in there. And what’s going on. Saving time, like you just pointed out is one way of doing what other things are possible today that a company can you know, start saving money in the short run like you guys work with them and say, boom, you’re not dreaming big enough downloading the files, those all could be automated for you. What else? Are we not dreaming big enough about?
Luke Komiskey 41:08
Yeah, the big the big theme of last year in particular. But it’s been an ongoing theme has been this concept of data monetization within companies. And this is this whole concept that your your organization is creating new data points and entering new data points on a on a daily basis at a growing pace. And what companies don’t recognize is that there’s actually monetary value with giving that data out to anyone in your community, whether that’s customers, vendors, partners, your community, that a lot of our customers that work with us are actually taking the data that their team is creating the actions that they’re doing the service that they’re providing, and be able to provide an external reporting platform that other people in your ecosystem would be willing to pay for that there’s a ton of value and well structured data. And can actually data can actually be literally used as a strategic revenue generating asset for the organization. And it can be, for me as a data professional that where data is often used as a more more from a cost center perspective, to be able to be a revenue generating line item is a game changer. And data monetization is I think, just like very early stage of what companies can even start to do
Rolando Rosas 42:18
with their data. Would you take, let’s say, like, like we do, we create a lot of content, we have a bunch of videos and articles and infographics and all kinds of other data. And I’m sure the in the in the Creator economy as well, this is a case and people are trying to figure out, how do I take what’s here? And things that I’ve already put out in the world and make it generate revenue for me. Walk me through that?
Luke Komiskey 42:48
Yeah, yeah. I mean, that’s from a data perspective, or just general
Rolando Rosas 42:51
data. What do I do? I mean, I’ve created a bunch of videos, I’ve got my processes, I’ve got employees that do some of this stuff. I’ve got teams, I’ve got other stuff higher Woodward, where I start, you know, where do I start? If I’m, let’s say, I’m a content creator, or my company creates content? You know, how do I go about monetizing that? That data? Yeah.
Luke Komiskey 43:11
I mean, I would think about just like who are who are people in your ecosystem that would be interested in that data? I think an interesting one that’s like in the content creation space that I hear a lot about is that there’s actually not much transparency around how certain platforms are performing for individual content creators, or even like podcast views is a pretty, pretty like hidden metric that would be interesting in aggregate to sell, if you had access to that, or if there’s some way that you could expose that data for you, as a content creator out to the world so that another data provider would easily pay for that to like, grab those individual data points from you. Yeah, there’s a lot of different like, there’s always different spins of partners within your ecosystem that would be willing to use that data more broadly. It’s why you see, insurance companies want to give you a discount on your insurance bill to put a little tracker device about where you’re driving, you’re saving 25% They’re making way more than that. And, and it’s it’s all about the value of that whatever data you’re doing, so that content creation is maybe a little bit tougher, because it’s more unique to you, but you do have your own individual stats that are interesting to somebody else. But in any other business. I mean, if you’re thinking about marketing, media agencies, like the type of return on investment that you’re getting within that is incredibly interesting to other marketing agencies, if you so choose to go that route. But more specifically to your customers probably want to know that if I spend $1 of marketing with you that I’m hopefully getting more than $1 of return back from
Rolando Rosas 44:40
Indeed, indeed, and that’s the challenge. And I know we talked a little bit about this about the predictive nature, not just the analysis, but predictions is I think, I think that’s where do you know of, of, of, of either models or that are LM models that are doing this or machine learning where you’re putting in place almost again, the NFL like or even CIA, because they do deep work a lot in probabilities, right? You know, what’s the probability because they’re spotting patterns, they know human behavior doesn’t change wildly. You know, people like to get in the morning, drink their coffee, go to their computer drive to work, there’s a pattern there that millions of people do every single day that dance from getting up in the morning, going to work and coming back. How can businesses unlock those patterns when it comes to marketing, advertising and other things that are, there are data but like you said, it’s messy, and it’s out there? Yeah,
Luke Komiskey 45:41
and I think this is kind of like the new battleground that I’m seeing in the data space is this concept of like a data marketplace that similar to an app marketplace, where you have a bunch of apps you can download for your phone, is having a marketplace where there’s curated datasets that are available that people are monetizing their data today? I think the the little dance we’re doing in the data space right now is not everyone can trust these MLMs to make good use of their data. So they don’t want their companies copy and paste in their database into chat GPT to like, give them some kind of example, because you don’t know where that data is going. And I think right now we’re in the in the phase of maturity of AI that I think people want to see what are what are the tools that I can use to make sure that my data stays safe within my walls, because people haven’t been as as willing to like, put their data out there. But of course, there are public data providers, and there are data marketplaces out there, that you can get things like weather data and traffic data and start to overlay that with your datasets to make interesting outcomes. A really cool use case that we worked with was a pizza chain, trying to a very, like delivery focused pizza chain, trying to understand how can we staff, our delivery drivers based off the likelihood of certain weather hitting, if you think about in the Minnesota market, it’s like, there’s a good likelihood that it’s going to snow on Super Bowl Sunday, that might change your decision on how many delivery drivers are very real cost for you how much you want to make available. So there’s like really just creative ways of like, you meet with a business leader who understands more of the nuances and the patterns of their data, but then you can overlay it with like the likelihood of thing of like weather events or whatever, whatever applies to your business. That is where it like, it’s really fun in my space to dream big
Rolando Rosas 47:20
be so cool. That’d be so cool. I look, let me just let’s make it 10,000 foot view here of this discussion. So I’m in an E commerce seller, and there’s millions of them in the country and around the world. What’s, what’s the impact of rain, lots of rain, and you know, given that we have such a big geography, right? You know, and ecommerce is national, right? It’s not just like, you know, people in Florida or New York, it’s impacted national. So if it rains, and half of the country is inundated with rain or the other, you know, we have a cold weather event, and now half of the country is blanketed in snow. What is the likelihood that more people are going to order? or less likely? Yes. Right. And what what should I do? The next step for that is, then, well, how should I price myself accordingly? Because you know, when there’s a hurricane in Florida, everybody’s buying hammers and nails and right. So you have to almost know that a it’s coming. It’s a great likelihood that to happen, and then prepare for hopefully, pricing yourself properly so that you don’t run out of stuff. Yes,
Luke Komiskey 48:26
yeah. And a big example of that is when a global pandemic hits and people are selling the facemask is there’s all sudden this huge demand that’s coming up. And you know, depending on what level you want to play with, as like, as you know, government mandates are coming on onboard, Could you could you price yourself and have inventory ready to take advantage of that demand coming because you can totally see that coming in. Every day, there’s little micro events in your own little industry that you know, whether it’s rain or snow, or traffic or natural disasters or celebrity deaths, or whatever a certain NFL team wins that you can take advantage of to get ahead and make a business decision ahead of that. And that is where AI can help you see those patterns, potentially, if you can input your knowledge into that along the way.
Rolando Rosas 49:13
I love it. I’m I’m excited about it. I know that for us. We’ve been in the E commerce game for 20 years. And there’s 100% patterns every single month. And it’s trying to unlock those patterns. Like I was telling you a little bit about it. People get up or people somehow even with within remote work, we’ve seen some shifts in you know, the day of the week, that’s become shorter. For example, give me one Wednesdays used to be longer in terms of how people ordered and you know, maybe they started their day and orders will start coming in at eight o’clock in the morning and go all the way to 8pm That’s shrunk. It’s condensed on Wednesdays, Fridays. People stop ordering soon sooner in the day, because they’re either leaving in the office earlier or not in at all on Fridays. And so those shifts in patterns have shifted a little bit of how we operate and try to target customers. But again, spotting those patterns can be interesting and having a way to to grab all that information and to hate Wednesdays, shorten your ad, your ad spend, maybe on Google, you know, day parted or schedule it between eight and three. And then on Fridays, on the summer, it’s even shorter. You know, so spend less on Fridays, because there are no odors coming in at 7pm at night anymore. Everybody’s out and partying during the barbecue, especially in Minnesota, where it’s summers are valuable. And so Fridays, you’re out by tune by noon, you’re done. Yes for that during
Luke Komiskey 50:52
during the three or four weekends that we get.
Rolando Rosas 50:53
Look, we want to we want to do something really fun now that we’re lightening it up a little bit, we have something that we call rapid fire or he hit me up with that rapid fire segment. All right, these are just phrases and words. And we just want to know what hits your brain when you hear this word or phrase that Dave and I are going to tell you there’s no right or wrong answer. Your answer is the right answer. Okay, so I’m going to kick it off with something really simple. Walmart versus Amazon. Value. All right. All right.
Dave Kelly 51:44
What is your favorite social media platform?
Rolando Rosas 51:49
Ooh, LinkedIn. Okay. All right. All right. Favorite piece of tech? Easy. Ai. I guess it would be no other. Yes.
Dave Kelly 52:07
First thing you reach for in the morning? Unfortunately, my phone, huh? Okay. A lot of us do. A
Rolando Rosas 52:15
Yeah, yeah, you’re not at fault. We we asked this question a lot. And that’s usually the case. That’s the winner amongst a lot of guests. Now, you may or may not have an answer for this, but whatever. Whatever you do, it’s fine. favorite podcasts? Hmm.
Luke Komiskey 52:35
I have a lot of podcasts I listened to one of my favorites is the Tropical MBA. What is that? What is it? It’s is a podcast that I think is a mix of more entrepreneurs that are probably I would call it like sub 10 million. But what I love about it is it’s got a digital nomad spin to it. And as a, as a formal former digital nomad doing building dashboards in Southeast Asia. It just helps me relive those days.
Rolando Rosas 53:03
Nice. Nice. I’m gonna have to check that out. This is why we asked this question. There’s again, you don’t know what you don’t know. And this is definitely one that sounds very interesting. Have you heard of Steven Bartlett, by the way? name sounds familiar Diary of a CEO. Okay, yes. super fascinating. So that’s what I would pass on to you. If you’re checking out new podcast diary of CEO. If you’re into the entrepreneurship and growing and learning and new things that are happening, he does a lot of have a little bit of everything when it comes to that. No, that’s amazing in his podcast.
Dave Kelly 53:40
All right, and the last one here, what’s a game changing book that you’ve read that’s influenced you to
Speaker 1 53:48
a book that recently came out that I’m sure is a popular option is called The Gap and the Gain. And it caught me at the right time in my own entrepreneurial journey. Because as the title would suggest, it is about how, as humans, we naturally measure the gap of where we are versus where we need to go. Instead of looking at gain thinking of where we’ve been, where we started and where we’re at now.
Rolando Rosas 54:14
And we have it if you’re listening on the audio side, we’ve we’re featuring, there we go. He’s holding it up in his hand, and Amazon also has that book. So you can go out and check that book out. It’s got pretty good reviews, it’s 4.7 on Amazon, published in 2021. It says it’s the gap in the game, the high achievers guide to happiness, confidence, and success. Love it. That sounds great. And this is enriching. That’s why we ask these questions. I want to learn more. Luke, if people wanted to reach out to you, you said you’re you’re on LinkedIn. What are some other ways that somebody can get in touch with you they want to know more about you or what you do? Yeah,
Speaker 1 54:52
absolutely. So you can definitely research more about what we do at data drive at godatadrive.com godatadrive.com. I’m also, as you mentioned, very active on LinkedIn and would love to connect via direct message. Or if you want to submit a Contact Us form on a website, I will be the one that probably emails you back. So I would love to connect with you there.
Rolando Rosas 55:12
Awesome. Awesome. Thank you, Luke, for joining us today. And sharing your valuable knowledge. I’m really appreciative because there’s a lot of stuff here that I’m going to take back to my team and let them know that maybe we need to look at a little bit more and dig in mitt. We’re not dreaming big enough. I love that phrase. You don’t mind if I steal that maybe trademarked or something? Yeah, please
Luke Komiskey 55:34
do attribute it to me though.
Rolando Rosas 55:36
I love that. That’s great. And if you’ve been nerding out with us about this episode today, I want to let you know that we have other great speakers like the great Kevin King that was mentioned earlier, and why AI thinks that people like to eat out of slow feed dog bowls. It is absolutely hysterical. But if you go in and check out the episode, which we did with Kevin King, wherever you consume your podcast, you’ll get not only get a kick out of it, but you’ll learn why Kevin King is the master at direct marketing and things like Amazon. So thank you for joining us today and we will see you the next time
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