The Truth About Data with Mario Laposse

This is Making Better Decisions. I’m your host, Ryan Sullivan. Decisions are where rubber meets the road for organizations. Each week, we’ll be learning from people who are on the front lines of turning raw data into better outcomes for their organizations. This show is sponsored by Canopy Analytic, helping companies make better decisions using data.

Ryan: today’s guest has over a decade of experience developing strategic initiatives and delivering results in the energy sector. His blend of technical expertise, leadership skills, and entrepreneurial spirit positions him as an asset in any organization. Striving for innovation and growth. Worked as a strategy advisor for a major energy company here in the US and is now working on research and development strategy abroad.

Ryan: Please welcome Mario Lappas. Yeah.

Mario: Thank you, Ryan. Always a pleasure and really [00:01:00] appreciate you having me here today.

Ryan: I couldn’t be more excited to have you. So I want to get right into the meat and potatoes here. Cause I know that you have a brain full of interesting stuff that I think everyone will learn from. So what is one thing you wish more people knew about using data to make better decisions? Yeah,

Mario: it comes down to three things. The first one is really understanding that data is not a panacea. It’s not the cure all. There’s a big misconception that if you have this mound, lake, mountain of data, answers will naturally come out. And so really breaking that.

Mario: Myth of like, it’s not, it doesn’t fix everything. And even if you have, it doesn’t mean you have to, so you have to be very intentional about what you’re looking for or what you want. The second thing is making sure you’re asking the right questions. And it’s really starting with the questions and the hypothesis of what is [00:02:00] it that you’re really looking for?

Mario: A lot of the times what I’ve seen is a lot of leaders come out and they’re just like, give me the answer to this one question, or give me the, I just need this one graph, I need this one answer. And a lot of the times it’s. Well, what are you really trying to answer? What are you, what’s your intention?

Mario: Where are you trying to go? And I’d say like 80 percent of the time, what they were looking for wasn’t even going to get them there. But it just felt like it was just something that at the moment, that flash point, they were like, Oh, this, this will do it. So let’s just, let’s just put on a graph. And then you ended up kind of like, bring me a rock.

Mario: It doesn’t go well. the other thing I want people to know is asking themselves, like, am I just trying to back up into an answer I already know? So you’re building a lot of that bias where it’s like, you know, I already know I want to, I want something red, so I’m only going to look at only red things, only give me the red data on red cars that are on the road.

Mario: And so what tends to happen is that. Then you’re just kind of forcing the data into an [00:03:00] answer. And it’s not, that’s not really what generates you insights. What you really want is you want to ask, you want to give yourself a little bit of freedom to explore things. And then really say, okay, well, here’s what I believe to be true that can let me have a decision, and then you’re really kind of either proving that condition to be true, or you’re proving something else that kind of sends you back, you’re like, well, if that, that didn’t hold, okay, let’s, let’s go back to the drum, let’s explore, let’s analyze. Because a lot of folks don’t have the time, they literally just come up, like, just give me the thing that supports my preconceived conclusion versus let me do the analysis, let me actually go test things and then kind of iterate a little bit. Now, there’s a lot of reasons why, you know, people kind of do that.

Mario: It’s, it’s fast, you know, it’s like, Hey, I just, I just need a quick graph. But there’s this misconception that like, everything’s either readily available. I either have all the data in the world or I have nothing. At certain [00:04:00] levels, you know, some people see how, how the sausage gets made. Right. Some people really realize like, Hey, you asked for this one little bar graph.

Mario: It’s really hard. Like we’re going to throw bodies at this thing. But as they, you know, as the folks get like a little bit more removed from that, they’re like, Oh, it’s just a graph. How long can it take? It’s like, Oh man, you’d be surprised. Right. And I think that’s, those are, those are the big things, right?

Mario: It’s that. Um, if, if you have people of various levels, like, really have that alignment as to, like, how much, you know, what is the right level of information and data we need? When is enough enough? Um, you know, when, how can we really ask the right questions? What are we trying to get at? And what’s our intention with this?

Mario: Then you really kind of have that, that alignment, right. Versus like where you see a lot of folks kind of turn into like, you know, I called it

organizational Roombas where people literally are just like, they bump into [00:05:00] one thing. Here’s a graph. Then they go back. No, that’s not it. Okay. They go bump into it.

Mario: Here’s another graph. Oh, that’s not it. And so it’s just like, you know, like not knowing the intentions of what people truly want. Just, just lays out, like, imagine like hundreds of people just kind of going and just bumping into things and turning it, it’s not, it’s not a great feeling as a leader to see that happen, but it’s a, it’s a worse feeling at the working level, where it’s just like, man, this is tough.

Mario: So that those, those are a couple of, I know one of them are more than three things, but those are the things that I hope people really have a better sense of when they’re dealing with data and trying to make better decisions, trying to make things actionable.

Ryan: yeah. Yeah. There’s, there’s a lot there that we can circle back to, but I think The follow up that I’m most interested in is something that, you know, I try to strike a balance with in, in my business is that lots of times, you know, [00:06:00] the, the nature of the game, whether it’s, you know, working within a company or working as a consultant or, you know, whatever it is, is, you know, to a certain degree, right.

Ryan: If somebody, you know, is asking for an analysis or a report, or they have a business question and they’re trying to dig into the bottom, you know, of it, you know, you. You have to just kind of go out and build what’s necessary. So there’s kind of one portion of, you know, the, the data Wrangler’s life. That’s all about just going out and answering questions.

Ryan: And, you know, you’re kind of very much in a, you know, Kind of ticket punching, order taking phase. And I think that that’s necessary. Like I do think organizations making investments in this type of stuff, you know, there are skill sets that are needed and, you know, specializational labor, all of these things, it makes sense to have people that take those requests and execute them efficiently.

Ryan: But that’s a very different process from what you talked about, which [00:07:00] is generating insights. I, which I see as being totally disjoint from answering already existing questions. If I’m going in and I’m looking for cool new things or inefficiencies or cost savings or any number of different, you know, um, Insights, as you said, that’s very different.

Ryan: How do you think about the balance between the kind of necessities of centralized reporting and division of labor versus focusing on generating novel insights for people that may not have asked for them?

Mario: That’s, that’s a good question. The big thing for me is one is having your, you know, your current status. Hey, how am I doing? What’s my, what’s my bill of health for the organization? Are we delivering on our commitments? Are we delivering organizationally on the metrics we said? [00:08:00] So separating reporting is letting you know where things are today.

Mario: How are things going now, in some cases, if you know, it’s obvious enough, or you have selected the right KPIs or you’re at the right level, you’ll know that, okay, I need to change things. Maybe you’re now you’re, you’re. backlog is growing. So you’re like, okay, well, I just need to grow people. But a lot of the times the status reports just give you the red flag, like something’s not right, or something’s going extremely well.

Mario: That’s where the analysis starts to come in, where you really have to then ask, like, okay, well, why is that? What’s going on? A lot of the times it’s not, you don’t have the cadence. You may not have the data. You may not have. Just even the intake of the information and data there. So somebody has to go have those conversations.

Mario: Somebody has to go, because reporting just is the flag. And then the analysis is really what enables decision. Now, a lot of what happens is that [00:09:00] either because, you know, just things have been experienced, you know, like just having seen it before, You’ll have folks in different parts of your career act differently, right?

Mario: Young, you know, maybe you’re a little bit younger or it’s a new or something new. You’re like, I need to better understand this. I can’t just kind of shoot from the hip and get going. While you have like seasoned executives, um, and I remember there was a, a director one time, he had a project and he, he, you know, he would get the proxy reports, but because he already had 30 years experience, which is a lot of, You know, cuts, bruises, and like, and that’s, that’s also just that experience is his data, he knew exactly what to do.

Mario: Okay. I need to go do this X, Y, C, Y. Well, you know, cause I know this, this stuff happened, but the challenge though, is that your start, you’ve, you know, you’ve seen this, depending on what industry, this organizational divide of just like different generations. So that experience, you know, hasn’t gone into the next group.

Mario: [00:10:00] And so really being able to do the analysis the right way, being able to really like build that skillset, build that confidence, create that space on top of the reporting, reporting is extremely important. Don’t get me wrong, but you need to be able to create and flex that muscle. So then you can really enable, you know, people that may not have 30 years, maybe they have 15 years, but then you’re building, you’re, you’re enabling them to really be able to make the right decision at the right time, which is, I think what a lot of folks don’t realize is that.

Mario: You know, everyone sees the top executives, the VP has been, you know, 30, 40 years. They know what to do immediately. Everyone thinks they should shoot from the hip, you know, 15 years is a long time to build that wisdom and that knowledge. And if you don’t have that, you have to find ways to enable those decisions, enable those new situations as well.

Mario: When you don’t have those years.

Ryan: Yeah, I like that a lot. You know, I think one of the things that’s apparent in all of the [00:11:00] answers is that the to my question so far has been, you know, strategic thinking. And thinking about how, you know, strategy gets crafted and then rolled out throughout the organization and the experience and the reporting and the follow ups and all that stuff that’s a part of it.

Ryan: So, you know, I know that I would say a decent portion of, of your career has been focused on strategy, evaluation, generation, implementation. Tell me a little bit about You know, the intersection of business strategy and data strategy and how you see those interacting.

Mario: Yeah. So, so I think the first thing for context is like how folks currently define strategy. And you know, everyone’s like, Oh, there’s tactical and there’s big, right? They’re like, Oh, you’re just either doing things or you’re setting a plan. And I think the big misnomer is that [00:12:00] strategy a lot of the times just gets boiled down to a business plan.

Mario: They’re like, Hey, I’m just going to do these things. And then there’s a roadmap. And then, you know, like, we’re just going to do these initiatives and boom. But really what you’re doing with strategies, you’re narrowing down. Strategic choices for your organization. Right. And going back to the whole thing, it’s like, you know, organizational Roombas running around, um, which, you know, at first everyone like chuckles a little bit, like, Oh, you know, it’s like, if you’ve been in a large corporation, you feel it and you know it, right.

Mario: Somebody comes out, management comes out and is like, here’s a big memo. We’re going to, we’re a big PowerPoint. We’re like, you know, three, four things that are critical to business. Here you go. And what happens is that people don’t realize that what if, you know, the, the compulsive strategy is like the information that helped inform it, the strategic choices your organization made, and then the plan to go execute and make those happen.

Mario: And really what it’s doing is it’s helping narrow down the focus for the organization and to make decisions. [00:13:00] Like, it’s like, here’s how we want you guys. Here’s how we want you guys to do things. Here’s how we want you to point the cannon. And the key thing is in how do you then. As an organization, translate that down to employees in each division and say, here’s what the, here’s what this means to you. Now, from a data perspective, what the, what has been happening is everyone just kind of says like, okay, well, we’re going to separate, here’s, you know, our business strategy, here’s our data strategy, digital strategy. And because they’re not conjoined, they end up fighting each other, right? And again, it’s because they’re like, well, it’s a roadmap, man.

Mario: Like we’re just going to go do this. It’s like, no, if you’re, if you define it as these are the choices our organization has made, and here’s how we as an organization or as an employee are going to make consistent with that, then you start seeing that from a data and a digital side, they have to be one and the same, they have to be integrated.

Mario: Right. It’s more about the question of, well, here’s the top three [00:14:00] things that we’re going to do as an organization that our leadership and everyone, you know, all the wise folks in our company have said, here’s where we’re going, well, how does data and, and, and, you know, how, how can we enable that with, with data?

Mario: How does our architecture, how does our data governance, how’s our data capability there? And then you’re aligning everything to that, to support it, to really help it thrive, so then you’re no longer fighting it out because what tends to happen is You know, when the treasure chests are flush with cash, everyone’s like, Hey, great, like just throw money at it.

Mario: Like, let’s see what happens. And a lot of the times, if you look at third party reports, it’s a lot of disappointment, Oh, this didn’t meet, you know, what we expected. It didn’t get the return we wanted. Oh, this didn’t meet the productivity thing here. Because you’re fighting with it rather than ask, it’s asking like, how can the, how can we have data and all our governance and our skill sets support the ultimate business outcome?

Mario: And I think that’s where [00:15:00] the intersection really is. It’s more of understanding how it’s an enablement and how it can really take you to, you know, the bold vision where the, where folks really want to go versus like, Oh, it’s another flavor of the month thing that we got to do. And then it’s going to die down because someone’s going to be like, Oh God, this is expensive.

Mario: Why are we doing this again? Yeah. What’s the value of this? And I’m sure you get this a lot, right? It’s like, so Ryan, tell me how much am I going to get out of this? It’s like, um, well, this is meant to support your business. So if you want to get those, you know, that 50 percent increase on, on whatever, or you want to get that 20 percent bump on productivity, this is how, this is what that’s meant to enable, not the other way around.

Ryan: Yeah. I, I love that. I, I do think that, you know, the last point that you made there around, you know, whether it’s the, the chicken or the egg is, is really valuable. I think that, you know, sometimes, know, we’ll [00:16:00] be doing, you know, marketing for. My company. And it’s, you know, they’re like, okay, so like dollar quantity, like what did this report make for somebody?

Ryan: And I’m kind of like, okay, you know, like decisions are where the rubber meets the road and analytics simply just in ideally inform good decision making. So it’s, I think it’s the decisions that get the credit. You know, I, I also really liked how you talked about strategy. When you defined it, you know how it’s more like a framework for prioritization versus a task list.

Ryan: And so if an organization has decided, Hey, these are our priorities, you know, we have [00:17:00] our company values. We have long term goals and we’ve decided to prioritize these things, whether that’s skillsets or people or systems or, or what have you, this is how anybody that doesn’t have direct access to us should make decisions is in line with this strategy.

Ryan: And then when you think about all of these different kinds of sub strategies that, you know, people talk about, whether it’s, you know, digital marketing, data, all these different parts of the organization. In theory, if they’re really following the overall company strategy, they couldn’t be at odds with one another.

Ryan: In theory, they should follow all the same prioritization, you know, and so that’s, that was really cool to get, to have you distill out as like clearly and easy to understand as that, you know, cause I think that there’s a lot of, uh, talk about what strategy is and not all of it hits home for me. Um,

Mario: of, there’s a lot of buzzwords [00:18:00] out there. It’s just like,

Ryan: I’ve heard a few.

Mario: yeah, you’re being strategic or tactical. And you’re like, Okay.

Ryan: Yeah, yeah, yeah. So let’s say that an organization has decided on a business strategy and they have also decided, okay, in order to, you know, Get these goals or get to these goals that we’ve talked about using this strategy. We have, you know, through all levels of the organization decided on this set of roadmaps, right?

Ryan: Like all the different parts of the organization are going to go in all these different directions and then they do it, I think. One of the transition points where I see folks stick is the [00:19:00] translation of that strategy into a roadmap of actions. And maybe, maybe that’s because that definition that you mentioned that I really liked isn’t as widely known, but like to put it really quickly, how does strategy successfully turn into action and completion?

Mario: there’s a lot of bad examples out there and right. Cause, cause it becomes a flavor of the month. It becomes a, Hey, we do this. And then you’re kind of going in a direction and you either have a leadership change or something. Kind of go, but there’s not that congruity as to like across organizations, like why this happened, right?

Mario: Or that are cascading properly just feels like it’s, it’s a knee jerk reaction or, or a whiplash. In order to really implement your strategy effectively, you have to know your internal realities as an organization. Where are you today? Right. It kind of goes back to the reporting side where you’re [00:20:00] like, you know, you have a report, you know, you know, here’s your health, here’s where you are, here’s your pulse, your weight.

Mario: You know, here’s how you’re going either with your customers, your operations, your financials, and then how you’re either improving your capability or capacity, right? And then as you’re going through, you know, a typical strategic process where you’re kind of learning, getting some insights, make your strategic choices, one key part is Knowing where you are today, even if it’s brutal truths, it’s like, maybe you don’t have the best service.

Mario: Maybe your customers don’t like how they’re being serviced, and that’s why they’re going somewhere. Maybe you need to be humbled a little bit, and maybe your competitors are doing something or the industry’s not, you know, evolving or changing as fast as it needs to be. And once you know that, then the next stage is knowing where do you really want to go and articulating that in a clear way.

Mario: Now, depending on the size of the organization you’re in or the company, if you leave it at the very top, [00:21:00] it can be a little too squishy. It can be very like, and that’s, that’s usually where folks stop, right? They’re like, here’s our big bold vision, right? You see companies going like, um, we’re going to eliminate email for everybody.

Mario: We’re going to, uh, you know, connect the world, right? Some of the tech companies, or we’re going to be the, you know, the family experience, pizza parlor of the next generation. Great. I don’t know what to do with that. So you can have the from to at that level, but really where the rubber meets the road is you got to get it down to the front lines.

Mario: And so there’s the, um, there’s this really popular book, um, on strategy, which is the, it’s called playing to win. And so it introduces a framework called, um, you know, the playing to win framework, and the. The author is, um, uh, Rajamaran, I believe. Really good book. Um, and within that, he talks about cascading of strategies.

Mario: And so he talks about like, well, you gotta, you have at the top, it’s squishy, and then you gotta go down to the indivisible level. [00:22:00] And so then that lets you know, okay, well, people cast their strategic choices, or really are just saying, here’s how we support the strategy above us, and then all the way up.

Mario: So it’s kind of like a giant scaffold. To implement things appropriately, you need to then do the from to stage as well and create that roadmap. It doesn’t have to be a crazy project. It doesn’t have to be, you know, a ton of PowerPoint slides, but you have to articulate it at that level. Because once you build that scaffold and then everyone has their whole roadmaps, everyone then can articulate, well, for us to really fulfill that incredible vision, that really bold goal that’s been laid out in front of us, we all have to change from here to over there.

Mario: And then here’s how we’re going to do it. And at that point, you can then articulate like, okay, well, I don’t have these resources today. Here’s how we’re going to, here’s what I need. And then you’re able to kind of prioritize and go about it. But it has to, you know, you have to articulate it all the way through.

Mario: Now there’s, there’s ways that it’s a little bit easier, right? You [00:23:00] that, so it’s not just like a huge, like just laundry list of stuff. You want to articulate it across like your people, right? Like, what are they, you know, what skills do they need? What do they need to look like? What skills do they need to have?

Mario: You articulate in terms of your processes. Like, do you have even a process? Is it well maintained? Is it even written? A lot of the times, a lot of things are just passed on as, you know, Um, just from on the job know how and people don’t realize the criticality of like, you know, you may want to write that down because if they win the lottery, chances are, and they leave, chances are, you’re going to be in a lot of hurt, in a world of hurt.

Mario: Um, you know, you articulated as well from your, um, you know, um, data and technology side. Do you even have an infrastructure today or an architecture to even like maintain this data and the reports that you need? Or is it literally just. An army of people that are just, you’re just throwing at it and you just don’t know.

Mario: And then lastly, like, you know, from, you know, like you’re covering [00:24:00] people, process, technology, and then is there anything else from your organization that’s important? And usually, like, if you cover those three to four categories, And people articulated, okay, now, now you have, you’re setting yourself up for success.

Mario: Then the rest of the thing is like, does the organization have the resources to help implement that? And if not, you just kind of prioritize from that. But the big failure that tends to happen is people just do it. The very, very top squishy things from to, all right, guys, congratulations, go figure it out. And there’s no consistency. So then everyone’s just kind of throws everything. And then people are like, well, this is too hard. Okay. We give up. Or. A few things make it and things don’t quite go all the way. And then, you know, you end up with something that doesn’t meet what you expected or what you forecasted.

Mario: So a lot of that, again, it just comes down to like planning, taking the time, making sure you tie the whole organization together across, and then you just. You go execute.

Ryan: I like you use the word scaffold [00:25:00] to describe it, and even though you were talking very generally about like any type of strategy, it was super clear to me. Let me back up one second. I personally am a believer, even though I like make my bread and butter doing data work, that it is literally just a tool for assisting with decisions.

Ryan: Thanks. Thanks. uh, you know, informing people as to like where things are at. So when you started talking about the scaffold, it was really clear to me, like, this is, you know, a hierarchy and what needs to happen as you were talking about it is like, if the leaders of the organization aren’t very intentional about breaking down.

you said that that, that strategic goal needs to get to the front lines and, you know, the process there, you know, okay, well, [00:26:00] if I have a sales team and I want them to sell a million dollars. Great. Leader of sales. You’re targeted with selling a million dollars. You’re right. Lots of times there it just ends, right?

Ryan: And if I want to make really sure that that actually happens, well, I should say, okay, leader of sales, you’re responsible for a million. However, the four region directors that report to you, they’re responsible for a quarter million each. And each of the 25 people that work for them is responsible for 10K each.

Ryan: And then now that we have clear goals for everybody that all roll up together, now it’s a great spot for a report to happen.

Mario: Yeah.

Ryan: And now each person can measure their contribution to the company goal. You know, so that’s, that’s a really, really cool way of thinking about it. Now, as much as, I mean, I could talk about [00:27:00] strategy stuff and the intersection with data with you all day, I want to. Change gears ever so slightly, you know, if for no other reason than, than I think that it’s, it’s interesting, but you, um, you come from an engineering background. And, you know, you actually spent a good chunk of time doing engineering and then thinking about engineering projects and managing engineering products and then ended up in strategy that way. I think that there are so many cool ways to think about data and decision making everywhere. You know, what has your experience been on the intersection of engineering and data?

Mario: Yeah. So I’m a, I’m a clanker by trade, right? Mechanical engineer. Like it’s, you know what, once you get in a, you know, after a couple of years, you can kind of start being like, what are you, I’m a clanker, I’m a sparky, you know, like people [00:28:00] out in the field start saying stuff, right? So when you really break things down, you know, it’s, it’s, it’s a really interesting question because.

Mario: Engineering, you’re really trying to take problems or things that have been solved theoretically, and you’re trying to make them now a reality, whether

Ryan: a cool definition. Wow.

Mario: trademark, right? But, but that’s what you’re, you’re really just trying to make things really, you’re trying to kind of capture the feel, whether, you know,

it’s a hard product, like you’re building a new chemical plant or refinery, a new tool, um, you know, a Gizmo or something, right?

Mario: Or you’re trying to build like a digital product, like, you know. A lot of the applications and cool things that are going on these days. And a lot of the thing is that you need to have data to inform things. Now, how you are able to capture that data and then digest it and then, you know, make actions is pretty, can be flexible, right?

Mario: You can be [00:29:00] very technologically advanced. Like, I don’t know if you’ve ever seen the movie Ford versus, Ford versus Ferrari with like Christian Bale and Matt Damon, right? And there’s this really interesting scene where you see the, you know, they put the engineers, it’s like, you know, bow ties, like, you know, they got the pocket protectors and they’re measuring the whole thing of like, you know, the cars, aerodynamics.

Mario: Right. And they’re like, you know, machines and sensors, and they got all the computers and you see them like, you’re like, you know, the. I think Christian Bell’s character comes in like, Hey, you know, we can just kind of like put some like rope and ties in and see how the wind is. And they’re like, the numbers aren’t saying this, you know, here, just humor us.

Mario: And they put it in and they’re like, Oh, you’re right. The wind is hitting the car and causing this issue. Right. And it’s still, it’s still like, when you really boil down to a data is trying to give you insights, they just, you know, it’s not as, you know, advanced as say a computer, but it’s effective. And so that’s really where the intersection of data and engineering comes in.

Mario: It’s like, you need to be [00:30:00] able to. Test things. You need to be able to do things in a way to prove that they are feasible, that you can make them a reality, you know, uh, finite, finite element analysis became super popular. And again, it’s, it’s data driven, it’s data driven so that, you know, like, Hey, I can make this real.

Mario: And that’s really where the intersection comes in is like, in order for you to really know, like this can happen, this can be saved, this meets the requirements, the tolerance, whatever you need, the data, you need the information. Now, what mechanism do you use? You can be flexible. It’s all about how cost effective and effective it can be for you to generate the insights you need to move forward or recycle.

Ryan: Yeah. And even some of the stuff that I know, like when you, you move from being a clanker, as he said, doing some more, uh, project, you know,

engineering project management and planning and all of that stuff, you know, once again, that’s, that was like a really, you know, cool way to tie it back to, you know, your answer to the [00:31:00] first question, which is, you know, okay, so if I’m doing project management. And everything’s going smoothly. Everything’s like going to budget the pieces or, you know, the puzzle are all getting made on time. Cool. Great. You know, however, you know, when things start going off track or when we don’t have information to know whether they’re on track or off track, that’s where, you know, Hey, let’s get some data here and let’s figure it out and then starts the questions of, you know, generating insights about what the status of affairs is that that’s kind of a cool full circle. thing, if I can change gears again, that I know that you’re into, and I think that this is probably just, you know, having that, you know, tinkerer, lifelong learner mentality, is Um, I know that you have invested a lot of time in learning and thinking and talking about what AI can do well and what it does poorly.

Ryan: What [00:32:00] is hype? Cause there’s a lot of hype, but there’s also like actual cool technology coming out. You know, I think my experience has been that. There are a lot of different areas, right? Like I can go in and like, you know, put my dog’s face on pictures of my friends and send it to them. Like, ha ha, you know, like there’s all sorts of like funky stuff that’s out there. As far as the portion that relates to decision making or data, I think that, you know, the big revolution of the last few years has been the proliferation of the large language model and, you know, chat GPT and all that stuff.

Ryan: And that’s, that’s valuable. And I think a lot of people are using that new tool and trying to figure out all the different places it plugs into. I’m curious to get your take on. You know, what is the state, the current state of AI in your [00:33:00] mind as it relates to data and decision making?

Mario: I think it’s still too early in terms of like being able to enable things like from a decision side. I think to the point that what people want, people want it. Like expect that this thing can, you know, help them decide. There’s like all the ads, like, Hey, this AI thing picked all the stocks for me. You know, it’s not the case.

Mario: Right. Cause I think there’s, there needs to be a little bit more knowledge as to how these things work. It doesn’t have to be everyone, but just some basic understanding of here’s what it takes, because, you know, it goes back to data, right? If you have, you have to be very intentional about it. And if you are.

Mario: You can get amazing results. What ends up happening is, is you have the whole thing of like, well, I want all the data. I want all the AIs, all the technology. It doesn’t go well, right? Because then you’re like, well, for you to

capture all your information, all this, this is what it takes. And they’re like, Ooh, I don’t like that bill.[00:34:00]

Mario: I don’t want it. Okay, well have, instead they’ve been like, Hey, look, here’s what I’m struggling with. Here’s what I need. What kind of, what data do I need? Well, you actually just need this piece, and if you start, you start getting results, and then you add another, and then you kind, you kind of modularize it, right?

Mario: You start building a little bit. Right. If you, if your strategy kind of, if you going back to the strategy piece, you’re, you’re, you’re building the scaffold top down, not from the ground up. But once you kind of get your, all your scaffold down, then you start building things up. And then it’s asking the question from, you know, how does data feed this?

Mario: How does artificial intelligence enable the, enable this for me? Right. Generated, generative AI is great if used correctly for the right things. And I think that’s where a lot of the, a lot of the rough comes is that people expected to fix everything, but really it’s like, well, why are you even using it?

Mario: Right. It’s, it’s great from a creative outlet, right? Like you got like, you know, the, the, the clouds, you got the chat GPTs, you got the [00:35:00] Gemini’s like, Hey, great. I can help me, you know, as a joke for a friend, I said like, Hey, I’ll plan you a business trip because he’s a very big, uh, you know, born identity fan.

Mario: Hey, here’s a business. Here’s a, uh, business. You know, a vacation after all the places that Jason Bourne was in and like all the things you should do for fun, right? It’s like, if you do that, great, it’s a creative outlet. But from a business standpoint to enable decisions, you have to be very intentional.

Mario: Okay, what are you doing? Are you trying to generate insights on your competitors? Is that, you know, the data is, it’s still early on in terms of like, it’s not fully reliable, but it can give you an idea. It lets you explore information, get creative, maybe get through some creative blocks or some, some analysis blocks that you’re just trying to get a different perspective.

Mario: Um, but it’s definitely not in the stage that people want it to be, which a lot of it, they want it to be, you know, human equivalent, like making decisions for me and everything. Right. But if that were to happen, a lot of people wouldn’t have the jobs they need, right? Because the computers are faster, they can [00:36:00] do things.

Mario: But, you know, who knows that what, um, you know, there’s always the, some folks saying, Oh, well, sometime next year, other people are like, Hey, we’re like really far away from this. You know, but at the end of the day, it’s a cost comes down to being intentional. Um, really not falling in the trap. I’m just going to throw money at it and value is going to come out of it because it’s not a lot of the time.

Mario: It’s not theirs. You know, there’s cybersecurity concerns. There’s a, you know, data breach, there’s a data handling concerns. A lot of, we’re still all still figuring this out. It’s early, but it’s exciting because there’s a lot of potential if done correctly. If you’re patient with it, if you take the time, if you then link it to all your, your strategic choices, make sure your data strategy or the data portion of your strategy enables it, then you’re going to be off to the races of doing some really amazing things.

Ryan: yeah. I, I love that. I mean, you talked about what I think are my [00:37:00] two big feelings around it in this space, which number one is, you know, you said modularize, right? Like, okay. I think a lot of people are like, okay, well, we’re going to do AI now. And it’s like, okay. That’s like saying I’m going to do shoes, right? Like, you know, like, okay, cool. We all, you know, we know that shoes are a thing. Shoes are really great, but there’s lots of different kinds and I don’t need all of them. I only need some shoes, right?

Ryan: And I need shoes that fit my feet. So I think that that talks to the second point, which is having a lot of clarity around a very specific use case. Um, I think, you know, if. Large language models are a square peg. I see a lot of people smashing that square peg into [00:38:00] anything that looks like any shape hole right now, because it’s so cool.

Ryan: Um, and it definitely is. However, if I don’t have, like we’ve talked about over the course of, uh, The call is strategy. Like if I don’t have clarity around, we are trying to achieve this specific goal, and this is a tool that will help us get part of the way there. I end up just like building stuff as demo ammunition.

Ryan: Um, you know, when it, when it comes to data, like large language models are. Not the most use of most useful, you know, artificial intelligence. If we want to use that term, there’s all sorts of like very cool machine learning algorithms, um, you know, another, another interesting thing that you brought up and it, you know, got me to have a new thought, which is a rare occurrence in this head.

Ryan: Um,

Mario: I [00:39:00] doubt that.

Ryan: That’s nice. But you were talking about it as, you know, a replacement for human capital. What’s a double edged sword about the AI that we have right now is that it’s all for the most part modeled after actual humans. That’s how we tried building stuff, right? Like we’re using neural nets, which are mapped, you know, like everything, we just kind of like looked at, Hey, what worked to generate our intelligence?

Ryan: Let’s try and make it artificially. Um, but that means it has all the same problems, right? So I think taking a general purpose, untrained AI model kind of like. Wheeling in an adolescent off the street and like throwing them in to a boardroom and being like, so what should we do?

Mario: Yeah. And you’re going to end up with, who was it? The, you know, the member of the show Parks and Recs,

Ryan: Yeah.

Mario: where, uh, I forget the guy that, that married, [00:40:00] uh, Amy Poehler’s character, but the, the comedian, right. He became mayor at 18. What did he do? He built a giant winter park, bankrupted the city

Ryan: Yeah.

Mario: it’s, and it’s, you know, he was like, it was cool, man.

Mario: I was, I was trying to do something. He’s incredibly embarrassed. And then he ended up making a career of getting cities out of. You know, the, those, those situations. But that’s the thing though, is that you need to experience and you’re just with the way LLMs work, you’re just, if you’re not careful, you’re just going to promote and keep, you know, make whatever’s bad exponentially worse.

Mario: And I mean, it’s, you know, is it really cool technology now that it’s a lot more available, but like you said, like, you know, AI is such a wide spectrum of tools, processes, and technology that you can actually use. And if you’ve done intentionally, right, maybe you don’t need generative. I, maybe you need workflow automation.

Mario: Maybe you don’t need to replace a purse, [00:41:00] you know, replace a person. Maybe you just need to, at the end of the day, if done correctly, it’s meant to be an enablement and an enhancement of the people you have today.

Take like, you know, just a regular data analyst or even someone that’s doing accounting or financial, uh, financial reporting.

Mario: A lot of folks don’t know, like even the simplest thing, like Power Query, right? The first time I, you know, I discovered it in like 2016, and this is roughly around the time you and I connected. It was one of those things like, wait, I can do this. And then once I did, I taught my folks about it. And then they’re like, You know, they’re, they were able to up their work capability, capacity by like 50%, they didn’t have to work extra hours.

Mario: They didn’t have to work overtime. They weren’t like stressed out. You know, they were actually a little bit better because they grew, they were using technologies for, in a way that helped their job. So they were like, this is amazing. And then they were able to manage more, but again, it was enhancement, not replacement, um, which is unfortunately what I think a [00:42:00] lot of folks have in their head.

Mario: And there’s a lot of the fear, and there’s a lot of that in boardrooms. I, a lot of people are like, you know, we’re going to replace blah, blah, blah, amount of workforce rather than like, If we take our current workforce today, upskill them, put the right technology, the right processes for things we’re good at and bring in the things outside that are good for us that we can’t do, our current people will be able to deliver more, be in a much better quality of life, have a stronger tie to the mission, and we will all benefit from it.

Mario: That’s what it should be.

Ryan: I love that. Okay. So obviously, you know, any, any listener now has gotten, you know, a ton of new ideas and value from your brain, but I’d like them to also know a little bit about. So, tell me a little bit outside of work, like, what’s your background, what’s your hobbies, what do you like to do? [00:43:00] Tell me more about Mario.

Mario: I mean, right now it’s chasing my son. He’s like, he’s, he’s a toddler. So, so for us really, it’s That’s the real cool thing about, you know, like being a new dad, right? Once your, your child gets up to a certain age, they’re experiencing exploring the world. So like, you know, like I’ve left to like, you know, before I was like, Oh, I’ll go like do CrossFit, catch up on rugby, you know, like look at sports, hang out with friends, cook, you know, just things that, that, that you enjoy that, you know, were fun.

Mario: But you kind of, now as a dad, you realize like how Special and incredibly enriching it is to see, you know, your, your son and your wife just

experiencing things, right? Like now, like he’s talking a lot more. So he says things, you know, he’s learning a little bit of Spanish, a little bit of English, a little bit of Chinese.

Mario: And the joke is like, people are like, Oh, what, you know, what are you guys teaching where I’m like, spich english, [00:44:00] you know, it’s like, it makes it all up. Um, But, you know, like we took, we took him to the zoo and just his reaction is seeing the tigers, the bears. We took him to a Jurassic park, like little, like immersive experience.

Mario: And, you know, he’s kind of the stage where he loves cars, loves dinosaurs, loves construction equipment. He can actually like, tell you

Ryan: are we supposed to have outgrown that stage?

Mario: no, he just, I just get the excuse to enjoy the dinosaur, you know, construction equipment blend with him and,

Ryan: Yeah,

Mario: I was like. It’s like, is this for him or for you?

Mario: I was like, honey, it’s for both of us.

Ryan: Yeah, what of it?

Mario: just, just being a good dad here. Um, and so that’s, that’s honestly like where, where life is, you know, just trying to, you know, be a good dad, get my son to just kind of see the world and, and, you know, do that all with my wife and really enjoy things to the maximum.

Mario: Like just when you see the, you see that twinkle in their eye when they experience something new and they like it and they [00:45:00] enjoy it. It’s, you know, you just can’t describe that. It’s a, it’s really, really amazing.

Ryan: That’s awesome, man. Well, you know, congrats on being a new dad and, you know, hopefully you get enough sleep and you’re enjoying it. Um,

Mario: No sleep.

Ryan: All right. So where can people find you? Where can they connect with you?

Mario: Yeah. So LinkedIn is the easiest place to connect with me. If, uh, you know, I’m very open, like if anyone ever has a, you know, a question or anything, I’m a pretty straight shooter, so they can just message me on LinkedIn or connect there.

Ryan: That’s awesome. I love it. I can’t thank you enough for taking the time to do this. I think we got to just rapid fire through a ton of cool stuff, right? Talking about all sorts of strategy and engineering and AI. Um, You have a, you know, a brain full of really interesting stuff to talk about. And I, I, I know that I got a ton out of, you know, getting the chance to pick your [00:46:00] brain.

Ryan: So thank you, Mario.

Mario: I appreciate it, Ryan. Thank you for the invite. It’s always, always, uh, really enjoy our conversations and, and appreciate having the relationship with you over the last couple of years. It’s, you know, it’s been great.

Ryan: Thank you, man. And thank you to the audience. If you guys learned something today or you laughed, please tell someone else about the podcast. Give us a nice juicy rating. Um, thanks again to Mario. This has been another exciting episode of making better decisions. Thank you guys so much. We’ll see you next time.

That’s a wrap for today’s episode of making better decisions for show notes and more visit, making better decisions dot live a special thank you to our sponsor canopy analytic canopy. Analytic is a boutique consultancy focused on business intelligence and data engineering. They help companies make better decisions using data for more information, visit canopy analytic.

com. There’s a better way. Let’s find it together [00:47:00] and make better decisions. Thank you so much for listening. We’ll catch you next week.