How Hard Is It to Get Things Done in Data Projects

In this episode of Data Analytics Chat, we sit down with Juan Gorricho, a leader in data analytics with experience at top companies like Walt Disney, Visa, and TD Bank. 

Now the founder of Strategic Data Transformation, Juan shares his journey in the data field, the challenges of delivering data projects, the importance of aligning data projects with business goals, and managing expectations amid the AI hype. 

He also provides leadership lessons, emphasising the value of empathy, listening, and team growth. 

00:00 Introduction to Data Analytics Chat

00:07 Guest Introduction: Juan Gorricho Career Journey

01:00 Early Career and Education

01:37 The Intersection of Data, Technology, and Business

06:05 Challenges and Success in Data Projects

11:46 Managing Expectations in the AI Hype Cycle

15:25 Leadership Lessons and Team Management

23:36 Influences and Principles of Leadership

27:22 Breaking into Leadership: Overcoming Fears and Embracing Change

29:56 The Importance of Servant Leadership

30:45 Challenges in Data Projects: Business Alignment and Adoption

34:23 Human and Cultural Roadblocks in Data Projects

43:22 The Role of Communication in Data Project Success

52:03 Overcoming Obstacles and Achieving Success in Data Projects

Thank you for listening!

Transcript

juan: 0:00

I was lucky to get in data early on before data was sexy, before data was cool. It's better when you take an approach of earning the trust and respect of the team. You are leading that and rather than assuming that because you are in the position, you will now have that respect and that trust from the teams. My success is directly proportional to the success of the team. Prioritize the growth of the people and then always be willing to unlearn and relearn new things. Right. Every day. After a point in time, it's all about people. The, the more you listen, the better you're gonna be in life. If you prioritize listening for understanding, if you prioritize empathy, your life is gonna be so much easier and everybody's gonna appreciate you so much more than if you just basically think you know at all. It's the human side of things in terms of execution, delivery, and it's the expectations. It's gonna make you or break you as a, as a data leader.

ben parker: 0:59

Welcome to Data Analytics Chat, the podcast where we discuss the world of data, ai, and the careers shaping it. My guest today is Dwan Gar. A leader who's embraced change led global teams for leading companies, including Walt Disney, visa tv, and, and now the founder of Strategic Data Transformation. Joanna has led teams driven transformation and picked up some great lessons along the way. So in today's episode, we'll explore his journey, the challenges. And insights here is for anyone looking to grow their own career. And also the day topic will look at how hard it is to get things done in data projects. Jowan, welcome.

juan: 1:39

Thank you, Ben. Uh, thank you for having me. It's a pleasure to be here and, um, yeah, I appreciate you. You having me?

ben parker: 1:45

No problem. My pleasure. And yeah, you've obviously, you had a fascinating career, so I'm looking forward to Yeah. Sharing, sharing your story. So, do you wanna start just introduce yourself, give yourself a bit of a background?

juan: 1:55

Yeah. Sounds good. So, so, um. I'm originally from, uh, Columbia. Um, been living in the US for about 25 plus years. My whole career, like 25 plus 20, almost 28 years, has now been in data. I was lucky to get in data early on before data was sexy, before data was cool. And so I've been very lucky. I'm very happy along this journey. Undergrad degree in industrial engineering. Although I never worked as industrial engineers, so don't ask me any questions about that. And then I have a master's degree, an MBA from University of Virginia in Charlottesville. But yeah, so e effectively, my, my whole career has been, as I mentioned, working in data. Usually at the intersection of, uh, sort of what I call data technology and business like, I see that that intersection in which I can, understand business problems, understand how data and tech can be brought to solve the business problems. And it's been a fascinating journey to your point. Uh, as part of the intro, I've been lucky to be part of companies such as Disney, visa, TD Bank, and, and many others in which I've been able to work in. Some really cool and, and also some, you know, lesson learning projects. So yeah, very happy to be here and thank you for having me again.

ben parker: 3:06

Brilliant. So I guess what first drew you into the field? Obviously, like you said, it's sexy now. Uh, everyone wants to get in there. So yeah, I guess what first drew you into the, um, industry?

juan: 3:16

Yeah, so I think I've been, um, I was lucky growing up in that, my, my dad, early on gave us access to computers at home. And so playing with computers, I, I had a chance to play with like early versions of, uh, Excel or even Lotus 1, 2, 3. You may be too young to know what that is, but it's basically Excel predecessor. So with that and other programs, I always fascinated how you can structure data and then you can analyze it, you can summarize it, you can play with data. And so that sort of got me interested in data while I was in college. There was a couple of courses that also like, sort of like showed me how. Through databases. Again, you can store data, you can track things, and you can sort of take businesses and represent them in data. And so that became a fa a fascination throughout college that led me to an internship in, uh, my first internship and. It was just working on data, just doing data work and and running SaaS processes to actually analyze data and do analytics and statistics. And I just became fascinating, fascinated with the idea of being able to use data to answer business questions and to drive business value. And that came also with a lot of pain points of also understanding how data sometimes misrepresents the business or the data quality gaps. But it was again, this notion of I can. Understand businesses through data, businesses can be represented in data and there's a lot of opportunity to to, to help organizations use data. I've been lucky to apply that in the private sector, like, you know, with the companies I mentioned. I also have had the chance to work in data in the, in nonprofits and some charities, both in Columbia, the us and it's the same thing, like you can always take data and have. Make an impact or have an impact towards. That's really what has driven me in my career to be where I am and to be like, you know, got into the industry in the first place.

ben parker: 5:03

Did, I mean, did you always wanna get into data or is it something, I mean, did you wanna be a sports athlete or

juan: 5:10

No, so I, I think, I think, so what I wanted to do was to basically use data, right? And, and, and build databases and, and answer questions through data, right? I, I've always have this bento. Using data for business purpose. Right? And so that's probably the reason, because at some point in my, in my early my career or even in college I had a chance to maybe go down the computer science path on information technology path. But I, but to me that was too technical. It was more on the, Hey, like if you have good data and you are able to analyze the data and structure data, then you can answer all these questions for the business and you can make the business effective. And that's really what, what has been very attractive to me throughout my career, but even, even now, that's what I'm thinking about is how can I continuously help organizations use data to add business value? And, and so to me, I, if I look at my career, I've been kind of industry agnostic. Like I've been in more, probably more than six to eight different industries. I've switched industries. So is more than a specific industry or a business. It's that ability to say, I wanna be able to use data for answering. Business questions and that's really what drove metu it. And what has gotten me more specialized in data, there's been points in my career in which I've had chances to work on different paths or sort of a little bit more distant than data teams for data work. But I always end up coming back to, to, to the data space. So one way or another, because again, that passion of, uh, using data to have a business impact.

ben parker: 6:35

I think it's becoming even more important to understand data from a domain, doesn't it?'cause obviously now is, we're blessed. There's so many technologies out there that can do coding, heavy lifting for us. I think if you can actually understand data for business, what the impact is, you're gonna. Actually be able to provide the insights for the company at a higher level, but yeah,

juan: 6:56

exactly. You're spot on. Exactly. And I think that's, and, and honestly, that's to me what I've learned throughout my career, again, 20, probably eight years or something of doing this is exactly that, is that that to me sometimes is the key difference between success and failure in data projects. In data work is the, are you strategically aligned on your style, what you're doing? Or are you just doing a tech project for the sake of doing a tech project? And, and that. To the extent you are one side or the other what, what makes a difference? And in my experience, data teams that I've seen succeed, uh, are teams that are very aligned with the business that truly understand what the business is trying to accomplish, and are very well in tuned and again, aligned with how the business is thinking about their objectives. Whereas on the other hand, the teams that are more disconnected. From that objectives, it usually means that struggle to deliver value and that actually ended up, uh, being sort of cataloged as probably failures.

ben parker: 7:47

Yeah, and I think it's, it's a big cha I think a big challenge for businesses is that getting that right balance of actually understanding business and the tech data side there's always the balancing act isn't there.

juan: 7:57

Yeah. Agree. Exactly. 100%. 100%. And, and, and so, so. Delivering the data space is a, is a notion of sort of people process, technology and data. So you do need technology, but you also need processes and people, right? And, and it's finding that balance between, as you said, between the tech aspects of it and the business aspect of it. I tend to say, and I know this probably, uh, you know, something that sometimes get me in trouble with my peers, where technology, uh, oriented is that for the most part, tech is usually the easy part of, of data work, right? There's plenty of tech solutions, there's plenty of solving tech problems, is a very well structured situation. Is usually on. So, and I call that the, the, the how and the what. Why are you using that technology to solve a business problem? Well, like what and what problem are you solving from. That's usually what the gaps are. And that's hard, right? Because that requires data leaders to be, to listen openly to the business, to be, to have empathy, right? To to, to be able to understand the business and to be, to be able to be in the business to understand how, what objectives am I gonna be enabling through data? How is, how is the company gonna be different? How is the process gonna be different? How is growth gonna be different? Or, or retention, whatever the objective is. That's usually something that, it's hard for some data leaders, particularly if you end up taking a, a very heavy tech view to, uh, to data. And so that, that's really, that comes to back to. How close are you going to the business and are you able to bridge that gap and build those relationships with the business?

ben parker: 9:33

Let me just pause this for a sec. Hey Ben, can you still hear me? Duran Ben? Yeah. So the signals I, can

juan: 9:39

you hear me?

ben parker: 9:40

Yeah, I, we can edit this out. It's just the signal keeps cutting out. I just dunno if it's my end or your end.

juan: 9:44

I, I, I, I think it's me. I'm being, having a little bit of issues with my connection.

ben parker: 9:49

Uh, recently. It's just reconnecting. Right now I've just got. With my wife. I dunno.

juan: 9:54

Yeah, I think, I think it's my connection is, is giving me grief again. Now it's back. Now it's back, I think.

ben parker: 10:00

Okay. Can you hear? Yeah, I can

juan: 10:01

hear you. Yeah. Can you hear me?

ben parker: 10:03

Yeah, yeah, yeah. I'm just recording What time, just to remember what time's back. Okay. Alright then Cool. I'll, I'll do, I'll jump into the next question. We can edit that out, so that's fine.

juan: 10:12

Sounds good.

ben parker: 10:13

Okay. Um, so looking back, has there been a defining moment when you realized this industry it was the right choice for yourself?

juan: 10:21

Yeah, I, I, I was thinking about this question and I think that the, so I think there's been multiple moments. I would, I would say. In which, I have confirmed that this was the right field to be in. And I think usually coincides with sort of all this hype cycle, right? Because like if you, if you think about the AI hype cycle we're in right now, there's been plenty of those cycles throughout my career at least. Right? You had, early on you had this discussion about data warehouses, right? And, and then parallel pps, like, parallel databases, right? And then you had big data and then you had like, you know. All these BI stuff and then machine learning, right? So there, every now and then there's a new trend that sort of drives that that hype cycle. But each of those inflection points or whenever that happens, um, it. Having been able to help companies make good progress in data through those cycles is something that has confirmed that, yeah, this is definitely the space I wanted to be in, and I'm happy I chose to stay in this space and afford as long as I, as I've been, um, just being able to help sort of demystify. Or bring some set of reality to, and there's not the hype about the things, but really it comes down to, again, what outcomes are you enabling? What business outcomes are you enabling to data? And you can do that with the hype element or not. You can do it with AI or machine learning, or you can just enable value period. Right? And, and so the hype cycles have been useful in bringing attention to what's going on and the opportunities, but also have been a, a good challenge, a good chance for me to show. What value can be brought with data and through data. So yes, I think there's been plenty of, of times. And then I was reflecting on this and there hasn't been any time that I said, man, I chose the wrong business or I chose the wrong field to be in. I'm very happy to be the, in this space and I'm glad I got in as I did.

ben parker: 12:05

Yeah I agree. I think it's obviously when you go through these, like you said, the hype periods, the, all these big changes, it's, I mean, if you're interested in tech it's exciting, isn't it?'cause it's that change's. The improvements, like the impact that can be made for companies?

juan: 12:20

Yeah, yeah, exactly. I think that, and I think so, so it is exciting. I, I think that sometimes, and especially lately with AI and all this stuff, there's also a lot of risk in that. There's some, I would say dash done. Especially because those hype cycles tend to inflate expectations, right? I think they, they tend to increase expectations much more rapidly than the ability of data teams to deliver. And so. But at the same time then sort of puts you as a data leader in a position in which you need to do a lot of education. You need to do a lot of sort of storytelling about the reality between the hype or the, what's the gap between the hype and reality? What can be done, what should be done, how should it be done? And that's, it's a challenge, but it's an opportunity. Um, my concern lately has been that because AI is seemingly so easy to use, right? Anyone can feel like they're doing vibe coding with LGBT or cloud or whatever, right? And so it is so easy, right? That and, and so tangible. I can touch it, I can see it, right? That is infl expectations to a point that it's putting a lot of pressure on data teams. Especially when we know that the infrastructure may not be there, that the data foundation is not there. And that's again, that's a challenge. I see it as an opportunity, right? And, and in that notion, in that you have to do a lot of education, a lot hopefully proactive education with the boards, right, with the C level. And it's like, yeah, this all sounds great, but let me show you. Where we are in our capabilities, what can we do, right? Uh, and how should we do it to avoid plenty of, uh, or to avoid many of the risks that, um, this brings if you don't do it the right way. Right? Thankfully, there's plenty of examples of company have jumped with both feet into the hype cycle early on, right? And bleeding edge of adoption. And they've been burned badly. Because of privacy issues or security issues, all sorts of things, right? So you can use those examples as a way to educate your, uh, your key stakeholders.

ben parker: 14:09

Yeah, no, I, I think you agree. Like, especially when something big happens a lot. So generative ai, like the expectation then is sky higher. So you've gotta, you've gotta balance that with where people are at. And I guess it seemed like if you have a sports team that overachieved one year. Maybe wins a cup or championship. Your expectation is they've gotta win it every year now, but obviously it's not gonna happen. So it's, yeah. I guess it is challenging for exactly to manage people, isn't it? Because people where everyone's wants, I think. I think also, like another example, I think like in the old days when the iPhone come out. It's not amazing. And the features, if they're not as high, your expectations, again, you, it's hard to answer, isn't it? Because you expect every time something massive to, to happen.

juan: 14:55

Yes, exactly. No, no, exactly. And, and, and, yes. And, and you see lately how the, like the new features to your point on the iPhone, are seemingly cosmetic to an extent, right? To an, to an, to an extent. But expectations management is probably one of the big things that any data leader should be able to be really good at. And that, that comes with influencing with like being able to talk to the business, with the business to educate, right? To bring a dose of reality to don't let. The pressure put you in a position in which you will be set up for, for failure. I honestly think that if you look at the CD's tenure, the Chief data officer's tenures, uh, I think on average it's like 18 to 24 months. Right. And, and I think to a great degree that comes down to. This gap between expectations and ability to deliver and not managing those expectations appropriately. Yeah.

ben parker: 15:42

And then we also have social media to deal with where you've got things to influence as well. So obviously is obviously is a challenge for leaders. Yeah, I guess it's part. Business, isn't it? Exactly. So what, was there ever like a, I mean, failure is a strong word, but, or a learning that's turned out, turned out to be a, like a really important lesson for you?

juan: 16:04

Yeah. I I, I, I think so. There's been plenty, right? I, I think that anyone who tells you that they haven't failed, uh, is probably lying to you. Right. And, and, uh, or they're not aiming high enough, right? I, I had a great boss who used to say that if you're skiing and you're not falling, then you're not really aiming to be better skier. Or something along those lines. I think that, um, probably, um, one of the big lessons from my career, and it happened early on is, is sort of this notion that when you become a leader and managing teams of people, a lot of it, a lot of magic will happen because of the authority.'cause now you're in the position of authority and that's, I mean, it depends on your approach to leadership. That can be true to an extent. But the big lesson for me was. It's better when you take an approach of earning the trust and respect of the team. You are leading that and rather than assuming that because you are in the position, you will now have that respect and that trust from the teams. And so it happened early on when I got promoted to, to, to managing a small team. And so there were people in the team that would have more experience than me. Uh, they were older than me. But I think more critically, one of the individuals was actually someone who thought that they should be leading a team and not me. I felt like, okay, I'm the leader now. I'm the manager here. And it was a very challenging few months managing the situation because I hadn't earned the respect of those individuals, particularly this one. Uh, and it took me, that taught me a lot about. How do you listen to your teams? How do you build trust? How do you build respect? How do you earn that respect from your team so that you can become a true leader? And, and so again, the key lesson there is the title is meaningless. The position of authority is meaningless. I, I, I had similar situations when I searched in the Army as well, in which like is not, the rank is more the trust and the respect that you earn. What really gives you sort of the. Quote unquote authority to lead teams. And, and that has been, let's say, that was the failure in terms of not being young and naive and understanding those dynamics. I think if I look at the, at the opposite of the successes in every new situation in which I've been promoted or which I've been lucky to take on larger scope, to manage larger teams, I applied those principles like I, I'm here, but it doesn't mean that I've earned your respect. I need to earn your trust. If you earn respect, and I do that through a lot of the. Principles of empathy, of, of listening, of working with the teams to show them that yes, I'm the leader and I'm, I'm ultimately the last person accountable for the outcomes of the team, but also that we're a team and that I'm here to help the team succeed. And my success is directly proportional to the success of the team. So. Those two in like the, the failure and the, and the, and how I frame that as a positive going forward, uh, since that point in time, early in my career, that's been one of the defining sort of leader leadership lessons or even like, you know, life lessons for me.

ben parker: 18:52

Yeah. No, I think it's su a. Yeah. Communication skills is critical, isn't it? It's so important, like just the way you portray things and like you said, to be successful, you need to, everyone needs to be put in a shift and it's, everyone's got, everyone brings different skill sets to the table. So what qualities or skills have made the biggest difference for you stepping into a leadership?

juan: 19:14

Yeah. I, I, I think that, uh, sort of related to the prior question and reflecting on this, I think to me it comes down to, I'm a big fan of, uh. Servant leadership principles. Um, and, and, and I mean that not just like for how you run teams and manage teams, but also how you interact with stakeholders in a business setting. And there's three core principles that I recently highlight from the servant leadership principles. One is listening, being able to listen. Listen first to understand. You know, um, I'm, uh, I've been accused many times to be a very quiet leader that is more, uh, sort of passive, but that's because I prioritize listening over speaking. I think the second key skill is empathy. I just, compassionate empathy, right? Try to understand where are the others coming from, what their framework of reference is, what they mean when they say something. And then the, the, the third one is, uh, commitment to the growth of people. Right? And I think, again, I think these three things can be applied easily to teams, more very importantly to your teams. And that's why I was referring to before in the prior, when we're talking about the prior question. What's about like, my, my success in life as a leader is my, is my team growing? Are the individuals developing? Are they better off when I came after I left? Uh, are they better off? Did they grow? And that many times can mean that with my team or without my team. So part of listening also means understand what drives them. And there's been plenty of conversations I've had with people in my teams that. They didn't wanna be part of the team once I came. They felt they wanted, we were not aligned or they wanted to do something different. But being able to have the trust to have that conversation to listening and empathy allowed me to help them find different roles for them to grow. Right. And, and then finally there's the, the fourth element I would highlight is sort of these, I call it intellectual curiosity or sort of your desire to learn, learn and relearn. Right? I think that's, uh, something that I definitely, when I build things or when I manage things, it's something that I. Usually tend to look for, to hire for, because like if you think about the data space or any space, but especially data, it's changing so fast and it's moving so fast that what you know today is gonna be useless in like a month from now. Right? And so having that curiosity tool, always wanting to learn more, unlearn what, you know, be able to break with your mental models and learn new things is crucial for success. So I would say those four elements is what sort of are the. Key things that have made a difference, at least in my experience, leading teams.

ben parker: 21:40

Yeah. Def And I think like a lot of business, obviously, especially like hiring a lot of people, they're just looking for the skillset, like tech skillset. When I think you would need to, and obviously it's a hard one to test as well, isn't it? To get people whether they're gonna grow into the role, but you need to under again with way things are going now, is I guess having that in people, individuals that got that ability and willingness to learn.'Cause yes. Things moving so quick. Now, even like when we, I mean, when I was doing hiring like 10 years ago, the amount of people we speak to were quite rigid in their ways. But I think now, I mean obviously the last couple of years, it's just you've gotta keep evolving. Now it's just the way it is.

juan: 22:15

A hundred percent right? A hundred percent. Like as I mentioned, when I started my career, right, SaaS was the big thing, right? And everything was SaaS. And nowadays, right? Like, you know, you do, you ask the younger people, like they have barely can spell SaaS, right? And so, and that require and for a lot of people in the middle that require. On learning technology and learning new things and new way of doing things, and to your point, exactly. I mean, that's usually what makes a difference. And so that's something when, when I mentor people, like some people that I'm lucky to be able to mentor them younger in their careers or some experienced executives is that, listen, have empathy, right. Prioritize the growth of the people and then always be willing to unlearn and relearn new things. Right. Every day. Right. Because things are changing too quickly and, and what, you know today is not what's gonna be the difference tomorrow.

ben parker: 23:01

Yeah. And I also, it's, you've got a, obviously you've got people that will talk the talk, but then you, you also need to get people that will walk the walk as well because it's, it's easier saying than doing, isn't it? Like, and I think everyone's, yeah, I think everyone's probably Vix. For this is you need to just do stick to your word, aren't you? Exactly. And

juan: 23:20

you mentioned that, you know it, it's hard to measure, right? It's hard to assess. And this is where you're gonna make, you know, hiring mistakes, right? And, and you're gonna discover that people will know what they're telling you. Right? But yes, absolutely. You have to make sure they're able to walk the talk and they're able to to do that. Right. And, and that usually is what makes a difference into like someone who can grow into a role successfully versus someone who just basically is stuck to their own ways.

ben parker: 23:44

So has there been a big influence on your or someone, or has technology been a big influence in your career?

juan: 23:49

Yeah, I think, I think definitely technology. As I mentioned, I was very lucky early my career that my dad gave us and, and my, my siblings access to computers early on, right? I remember coding in an Apple two e uh, in a language. Program called Logo, right? Which was like this early sort of drawing program. And so that, that, and, and not only the access, but my dad is someone who's incredibly curious intellectually and with technology. And so he. He's instilled that in us, my, me and my siblings in that how technology is amazing enabler if used the right way and for the right purpose. I, I think also there's been great leaders that have had the privilege a lot to work with, right? And that's something that, uh, has actually made a difference in, in my roles and, um, uh, and I've learned from a handful of amazing leaders that I would follow anywhere. A a lot of these things that I've talked about, like empathy. Caring for the teams and the human side of leadership, the people side of, of leadership and the people side of business. I mean, similarly, I've, I've, I've been lucky that I haven't had that many sort of, let's say, bad experiences, uh, with leaders. But there's been situations in which there was no alignment with leaders, right? We wouldn't see eye to eye. And that also taught me a lot of what I was not willing to compromise, right? How my principles were important and that, you know, in some of the situations, I decided to move on, right? Because I, I clearly didn't. Feel I was in a situation that, uh, was aligned with my principles from a leadership perspective. But yes, definitely. I think the, the, the people, the leaders I've had the chance to work with have been amazing. Some of, some of them have been amazing, have taught me a lot of things about the human side of leadership. Like, it, it's very interesting. When I was in business school and I was sort of networking, I was like, you know, this was 25 years ago. And you tend to be like, you're young, so you're very focused on the technical aspects of your business degree, right? Like, you know, finance and this and that, right? And every alumni I would talk to for networking, they would say, listen, after a point in time, that's great. And, and the tech, the technical or the functional experience is great, but after a point in time, after a point in time, it's all about people. Like, yeah, you're gonna find yourself managing work through people, managing people, influencing people. So it's all about people. And, and you hear that and say, yeah, yeah, yeah, but I still wanna take some of these technical sort of classes that, and then lo and behold, right, the moment you get into leadership and you start growing your career, it's all about people, right? It's managing teams, dealing with teams, dealing with people, dealing with humans, right? Dealing with stakeholders, right. Influencing and so definitely, I think that's why. I value a lot the lessons that some of the key people in my life, um, have.

ben parker: 26:21

Yeah. And I, I think it's quite easy, especially in early in your career,'cause you're not looking at leaders in that way. It's more about learning your trade, isn't it? I think it's, you overlook the leaders you've, or the bosses you've had in your career. Like,'cause if you look back now, you've probably think actually, yeah, I like that about x Uh, I like that about YI think it's, you do stop, you pay attention to it, don't you?

juan: 26:41

Exactly. You pay a lot of Exactly. You're, you're spot on and, and something that I was thinking about recently. And that Simon Sinek actually he has a, a really good article about it or a post or something like that. It would like some, sometimes when you, when you, like, if, if you don't have the leader that you, you wish you had then become the leader you wish you had. And so, and to your point, I think that that's, uh, something that situations in the past sort of led me to that. And what I went back and did is like, I, I sort of listed all the leaders that were great, right? To me, what did they do great from my perspective and then what should I do to be, and it's incredibly helpful, right?'cause it sort of shows you what your principles are, like, what's good, right? And, and started like sort of making sure that you are driving to towards that, those kind of, uh, success as a leader.

ben parker: 27:27

Yeah. No, definitely. So if, let's say it's obviously a lot of people have a fear to move into leadership for not having, not thinking they know enough or wanting to know more. Well, I mean, what sort of advice would you offer someone who wants to sort of. Break into leadership.

juan: 27:44

I think, I think that, yeah, this is, this is a great question, right? And I do, and I do get it a few times. As I mentioned, I mentor a few people and so I think the, the first thing is that usually when you're getting into leadership, you're gonna be in this inflection point in which you were usually, let's say hands-on, you are more maybe individual contributor or, or like leading a smaller teams. Heavily hands-on. But the more, the more you become into leadership, the less hands-on you're gonna be. Right? And I think it's really important to recognize that and understand that, because that's sort of the. That's the realization of whatever brought you here is not gonna make you successful in the future. And that also is a key point to understand. You now need to lead and get things done through people. So letting go of the hands-on aspect of it and being able to then manage people in terms of setting goals, setting expectations, following up with those expectations, those goals, giving feedback, giving proactive feedback or constructive feedback is something that new leaders need to be able to. To recognize and I think so being able to let go of that desire to get things done yourself hands on versus leading to people is, is, is the, is one of the critical piece of advice I give people and when I see new leaders fail, is usually when they can resist the sort of the pull to get things done. Because whoever they are leading are doing things differently or in. And they jump in and get it done right. That, that's one of the big things is you need to let go of the how. You need to agree on the what and the why. But that's the big thing is especially as, as you grow in leadership and you're managing larger teams, right? Like one of the teams have led 120 people, right? There's no way in life that I can ever know what the 120 are doing. Even if I wanted to, I wouldn't have the time of the day right to, to do that. So I need to learn to trust. To make sure that I'm clearly communicating, that I'm setting expectations, that I'm measuring the outcomes, and then empowering the different layers of leaders underneath me to be able to do the same way and empower decision making, delegating.'cause I think there's, uh, definitely a, a. The larger the team or the larger your scope, the less intelligent you're about the details. But then the more intelligent you need to be about people management and expectations management. So I think that's one thing I would tell people. And the second thing is definitely, as I mentioned before, the the key people, the key principles of servant leadership, right? Empathy, listening, prioritize the growth of your teams, right? That's something that's gonna carry you along. And there's different styles of leadership, right? Some people are different. They may not agree with the servant leadership principles. I'm a big fan of them. They've served me well and I think definitely that's something I would say. The, the more you listen, the better you're gonna be in life. If you prioritize listening for understanding, if you prioritize empathy, your life is gonna be so much easier and everybody's gonna appreciate you so much more than if you just basically think you know at all. And always lead with talking and directing. Of course, context matters and situations sometimes require you to be much more prescriptive and directive. For the most part, listening and empathy are something that's gonna carry you a long way.

ben parker: 30:43

Yeah, no, that's some great advice there. Okay, cool. Alright, we'll we'll move on to the, uh, data topic then, and obviously, yeah. I guess the goalpost is always constantly changing with data projects. It's obviously Yeah. A massive challenge for firms. So what, I mean, why would you say so many data projects that look promising on paper struggle to live to deliver real business value?

juan: 31:05

I think, I think to me the key to this is the lack of business alignment. Are we able to answer the question, like, why are we doing this? Um, as, as you and I sort of talked about this a few minutes ago, is like, technical competency is important. Like, you know, knowing how to get things done is important. Doing it the right from a technology perspective is important. But usually to me, the gap. And it's happened to me many times in project projects have failed, is that there was no clear understanding of why are we doing this? How is the business gonna benefit from this? Do we understand how they need to use this solution or adopt this solution? And are we doing the right sort of change management and adoption management training with the business? And so that's basically the, the. Like to, to your point, like, you know, something paper, like a new predictive model to do something may look fantastic, right? But if there's no clear why, there's no clear adoption, there's no clear adoption plan, adoption strategy, then it's going is, is usually that would leads into failure because that usually, if the business teams and the data teams are separate. And if the business team, the data team, see it as a, I'm just producing outputs that hopefully someone will use. That's the key break in, in expectation management and, and alignment. That makes pretty. PowerPoint with projects not come to realize their full value, full potential.

ben parker: 32:23

Yeah, no, I think also there's a lot, there's a lot that goes into it, isn't it? Like it's the goalposts constantly change. I mean, again, it's, it's, it's easier saying than doing, isn't it? Like what you're dealing with. People change management. You've got the process. I think that's the biggest thing, isn't it? When you, you're changing is the like processes. It's just'cause as soon as you change one thing, obviously, obviously more opportunity, but there's also more challenges. Like everything keeps moving.

juan: 32:50

Exactly. No, no, you're, you're exactly right. There's, you know, one example in my career, you know, uh, in Disney, like we were launching this amazing project to redo a process completely. So this was a process that had some analytical components in it. We redid the analytical platform thinking that, hey, we're just redoing the analytical engine that is producing these outputs that the. Park operators need to use to run the parks or the attractions of the parks in Disney. There's no need to like talk to them. There's no need, like it's just a technology project from a data perspective. And lo and behold, right, what happened was the outcomes changed, right? And that the new analytical engine was producing where more accurate outcomes. In terms of projecting and predicting some key drivers, but they were different. And so when you have these operators of the parks who are like, have a very fine tuned gut in terms of what they need to run the business, and all of a sudden they start getting different numbers. This is crazy. System's broken. I'm gonna override it. I'm gonna use my own numbers from my experience for 20, 25 years of running this park attraction and it all came down to. We didn't have the change management. We, we didn't explain to them how this was gonna be better. Why is this amazing, beautiful new model that was completely based on some early machine learning elements was gonna be better and do things in a different way. And so that's an example of missing the point and missing the talking to the leaders.

ben parker: 34:07

Okay. So, moving on to the next question then. So do you find the bigger roadblocks in data projects come from technical issues with the data, or is it more from like the human cultural challenges?

juan: 34:19

Yeah, this a tough one because I would say it's a little bit of both, but I think generally to me, the bigger obstacles are the. The bigger roadblocks are definitely human and cultural. Cultural, right. Uh, of course data itself presents challenges, right. And issues, but usually is the sort of people and process side of things goes back to something we talked about in terms of change management, expectations management, that that is usually the, what is the biggest roadblock there? Uh, used to do a survey as a CDO survey. I dunno if they do it anymore, but they used to do a survey for, they did it for many years. And like the top question always like what is the biggest obstacle? Change management process management, the top of the 20. Top 20 reasons for challenges, the top five. What had nothing to do with technology. I think Randy Bean does a similar survey and it usually comes down change management, people management, process enablement, process improvement. Right. So definitely that. I, I, I do think that once you overcome those, so you make progress on those many times, data is a challenge. Right. And that's when. Sort of data governance, data quality comes into play. Misaligned expectations, right? And, and also misaligned understanding and agreement of who owns data and who should be driving data changes. But if you ask me to choose one, it's definitely human, cultural and process centric. Those are the biggest barriers.

ben parker: 35:33

Yeah, and I think also we touched on it earlier, wasn't it like expectation, right? If you deliver one. Yes, one project put like amazingly well your exp expect, it's, I guess your expectation is it's gonna run smoothly again. Well from your, your boss, your peers, it's gonna run smoothly again. But then obviously, like I say, every journey is gonna be a different situation. A hundred

juan: 35:52

percent. And I, and I think to that regard, you, you just mainly think about something that is very important is, I'm a big fan of pilots and POCs, but sometime pilots set even more inflated expectations, right?'cause they made it, they make it seem as. Something that is very easy, very and is not sustainable. Right? And that's why, a lot of the vendors that have approached me in my different corporate roles, like, yeah, we can do a pilot of this, we can do a pilot of that. I said, great. But to do that, you have to make the pilot work or the POC work in the context of my organization, which will test not only the tech aspect of things, but also like procurement and cybersecurity and all these things that matter when you do. What you do. Otherwise, you're just doing a shiny object that's not the right expectations, that's not gonna scale, uh, or it's gonna fail, and then it sort of continues to damage your reputation and your ability to deliver in the future.

ben parker: 36:40

Yeah, and I like you mention that'cause it's similar with the hype with gen generative ai.'cause it's, I mean, anyone can crack ball, not obviously people can create an easy demo to make it look work workable. But then when you put it into a business context where it's gotta be scalable to a global company, obviously that's when it's gonna be challenges.

juan: 37:00

Exactly. Exactly. 100%. And to your point, gen AI is making even data even harder because yeah, anyone can take that and make it, make it seem beautiful, but then once you start seeing at the real data or use that actually like, try to scale it right. They, then that becomes a, a, a bigger challenge.

ben parker: 37:17

Perfect. So then obviously with looking at expectations, obviously between like executives, data teams, and end users, I mean, how does this, this misalignment sometimes? How has that, how's that derail progress?

juan: 37:30

I, I think as we, as we mentioned a few times, right? Definitely. I think that the missile expectations due well progress because what. Business teams or leaders or board of directors or Celia are expecting, there's all this external pressure that it sort of continues to inflate expectations, right? Like I'm sure that the. Boards of directors and, and some of C-level people have experience are experienced a lot of formal, right, like fear of missing out, right? And so that puts pressure on. So they put pressure on leadership to like, I wanna do ai, like, great for what purpose, right? There's that. And so definitely the real progress because resources that can be or should be used to advance the foundation that is needed to do data the right way, get directed to some of these short term. POCs and pilots and, and projects that are aimed at sort of satisfying the pressure that the board or so level executives are putting, but they're not sustainable, right? And because they're not sustainable and end up creating more problems than fixing solution or fixing, fixing problems, then. They continue to adapt to the bad reputation of the data teams or the executives leading that type of work. So I think it goes back to something we mentioned at the beginning in terms of managing expectations through education to training is incredibly important to make sure that all this sound great, right? But to get it done, there's a lot of things you need to get right so that you can make the right progress. You need the right data, you need security, you need all these things and, and. That sounds cumbersome and sounds like, you know, like a lot of bureaucracy, but it's, but it's incredibly important to get it right and as I mentioned before, right, I think because j AI is making it so easy or seemingly so easy to get things done, that's increasing the pressure on teams and people, and definitely create a, a wider gap between ability to deliver and expectations.

ben parker: 39:19

Yeah. No, I agree. I think it's. It is also a lot of hype around, like, Jenny, I can do everything when it's not the case. It's like it's still a massive human element needed to deliver these projects. And, um, I think, I think things do get overhyped sometimes. Why? I mean, why would you fit? Why do you think like speed. Uh, it's so often hard to achieve in data project as compared to other business initiatives.

juan: 39:47

What, what I've seen in, in what I've seen in this regard is usually something that has to do with how the projects are executed. And so, for example, in in, in one of my experiences. When I came on board, the approach was, Hey, we're gonna build this amazing data lake. We're gonna fill it with all the data that we have across the company, right? And we're gonna be counting how much data we're loading. But there was no sense of what to do with that data, right? It's kind of like the classic building and the will come approach, right? And so that I, I, I consider that a very sort of tech oriented view to data or a very sort of left to right view to data. Just build this amazing foundation, make all data AI ready, and then eventually magical things will happen. That. Slows progress because progress defined as business outcomes like is at zero for a long time, right? Like if you take three years or five years to build this beautiful data lake, in those two to five years, the business is basically seeing nothing. So, so from their perspective, progress is slow, right? Or even zero. I think that you are better off, uh, usually by, by taking a much more sort of iterative approach and. An I te approach to delivery that is heavily focused on business outcomes. It goes back to the point we've talked about before in terms of how do I under identify those business outcomes? How do I make sure that I know. What is important for the business, and then very slow, very, very quickly in quick iteration, start delivering those outcomes in a way that the business can use it and, and drive value through that. And then you really just need to build that fracturing process that repeats that over and over and over, right? That's what usually will bring the agility or the agile principles in data. But definitely, as I mentioned it, it slows down when you take a too much of an extreme approach, right? Because on the other hand, if you do too much to the business too quickly in a way that is not sustainable and not scalable, at some point that machine is gonna break, right? And, and you're gonna have these massive amount of solutions all over the place, all silos, all point solutions, that they're gonna become sustainable and unmaintainable. And that will bring progress to a halt that's game. Because eventually you're gonna say, Hoho, stop. I can't produce anymore. I need to run these things. That have been producing. So pro is low against finding that application of true agile principles like lowercase, agile, be be customer driven, making it a way that is sustainable and scalable, and just do that over and over. There's plenty of low hanging fruit to do that, right? I think teams, data teams underestimate the importance of just talking to the business and identify the key priorities and just deliver on those. In a way that is intuitive with MVPs and with like, but just do it and do it in a, with a scalable, sustainable, that's usually to me, my, my, my experience. What makes this, this sort of challenge with the speed and agility, uh, that's the way to improve it and make, make it better.

ben parker: 42:34

Yeah. No, and I think it's again, you like touched spot on with like dealing with the outcome. Like what do you wanna achieve right now? Because obviously like techies, they wanna be doing the exciting projects, but sometimes you are not ready for that. Or even, even like with LMS at the minute, like obviously that tends to be the easy part of the project, but doing all the, the behind the scenes work, which is less exciting, is the most challenging aspect of the work.

juan: 43:00

Exactly.

ben parker: 43:01

So in your experience and how often. Do communication breakdowns rather than technical issues. Cause the real delays.

juan: 43:07

I think that that's, that's one of the, that's you like I think the way you phrased it, right? Like you, that's the root cause usually. Right. And I think that I define like breakdown communication as sort of lack of alignment between data teams and business. And that, yes, usually that's what causes delays, right? In that there was no, the project was approached as a, you'll gimme all your requirements and I'll go into this black box for the next nine months and build on and come back. This is like breaking, that's a clear breaking communications, right? Because communications with the, with the business should be something that is ongoing, that is constant. If we go to the prior question and ask, like to think about if you're really delivering in an agile way, in a, in a. Partnership way with the business, then communication is ongoing and you can, you can tell the business, this is working well, this is not working well. We can do this. We cannot do this. Right? Or I need you to help, I need your help business. Prioritizing this over that because we can do this more easily. This is gonna take us longer. So that requires ongoing communication. The moment that breaks, because how you're approaching the project or because you don't wanna share in full transparency what's going on behind the scenes or because for whatever whatever reason, that's what's gonna basically. To your point, delay the progress. You are gonna end up building outcomes or outputs that the business doesn't really need. You're gonna be less focused on outcomes and that's what's gonna usually break all things between. So I'm a huge fan of transparency. I'm a huge fan of our communication. And, and mainly partnership, right? Delivery of data solutions is a partnership between the, the delivery teams, data teams, tech, and the business. They should be acting as one entity, like the data teams need, need to be so embedded and so savvy about the business that they can. They, they can understand what the business needs. And at the same time, the business teams need to also be savvy about what it takes to get data, the right, the right way, right? The business teams need to understand their own data and they are responsible for data. And so, so that is based on communication, right? Getting to those sort of optimal point of delivery requires constant, ongoing and transparent communication between the teams.

ben parker: 45:08

So would you say. It's obviously, no, it's, there's a lot, like you mentioned, there's a lot of entities that need to be involved with these projects. Is it like not bringing everyone into the meetings or is it having too slow for meetings you think is the big problem for businesses or is it something else? I

juan: 45:26

I, I think that, um, yeah, so, so a lot of it could be that you're not involved in, but I think what I'm, I guess what I'm trying to say is that you do have to be careful about how you structure the communication so it doesn't become then I think you. Point, just meetings for the sake of meetings or many meetings, or large meetings or, and then that's when I'm a big fan of understanding accountability, very clearly, understanding accountability, who's responsible, who's accountable, who's gonna be informed and who's gonna be communicated. And in many organizations, right? And I lift through many organizations that like, they try from consensus and, uh, we don't, we, we are a consensus based culture, right? That usually euphemism for everybody shows to the meeting. We all get, like, get together, but there's no clarity on who's accountable. Like I've been in situations which where we try to define accountability, like through a RCI matrix or to whatever tool you want to use, you end up with like two teams being accountable. Like that's not possible. There's only one accountable and one responsible, right? And, but when you start sort of understanding that and making sure that who needs, who really needs to be here, who needs to be informed, that's where you will bring clarity. And, and the thing is, is. It doesn't mean that you just go dark and you go in a corner and do things on your own with your one partner, and that's it. It requires you to have a very well-defined communication strategy, to be able to work with all partners and to make sure that all partners understand what's going on and they feel informed and, and they can chime in if needed. But at the end of the day, there's gonna be one responsible party and one accountable party. And if that's clear, identify, then go for it and make it happen. Yes. Consensus can be a challenge, but mm-hmm. Usually when, when you bring that clarity and accountability, that will help you and drive a lot of the success.

ben parker: 47:06

I mean, especially if you could, you have so many different levels involved, obviously. I mean, I, I have stories of people like just having a meeting to attend a meeting when it's end day, it's just a waste of time. Like you need to be actually I think being with your meetings, don't you, sort of thing. And then also like you've got trust, the people involved to do what. Gonna get delivered.'cause you don't wanna speak. You actually what you need to deliver. Not just keep having these meetings.

juan: 47:31

Exactly. No, no. Exactly. Like I, one of my, one of my prior roles, right there was this project we were executing and there was this like weekly meeting to review progress. Right. And 45 people would join the meeting. But like three or four would speak. And so at some point, like I started measuring that, like, what are we doing here? Like these 42 people were, are joining What are the, and so I killed the meeting and only added the four that need to be there and restricted of, and, and blocked the meeting from me being forwarded and people were up in arms. I need to know like, no, no, don't worry about it. We'll communicate them we'll. We'll share minutes right of what happened and decision make, but this is. And it worked, right? People were, yeah, people just were like, like 45 people have one hour a week to join a meeting, to just listen, ah, we have a bigger problem here. Because that, that just makes no sense. Sometimes you have to be like, sort of a little bit, sort of, again, directive and decisive about that, but don't forget that communications important. Right. And if there was someone in there, why was, was it why they were here in the meeting in the first place? What do they need to know about this that I can't. Maybe communicate to a minutes meeting it, email or summary or outcomes or something like that. Uh, and so balance, always balance the effectiveness and efficiency of the meetings with, uh, your communication strategy to make sure that you still get to get things done.

ben parker: 48:43

Yeah. Brilliant. I like that as well. It's'cause yeah, like the, yeah. You want people, yeah, you want deliver, like end day delivery is important. Don't, yeah, people don't need to be there sometimes, like as I say, fair enough. If you've got minutes taken, it's get, be efficient, isn't it really? It's all about being streamlined.

juan: 49:02

Yeah, exactly. Yes, exactly.

ben parker: 49:03

Cool. So if you could wave a magic wand and remove any wand recur in obstacle in data projects, what would it be?

juan: 49:11

Oh man. Like I, there's so many things, but I think I, I would say, I would say the biggest thing is probably lack of trust between the stakeholders of data projects. I think that trust is something you earn, right? By working with your stakeholders and your customers your business leaders, right? And business teams. I think that because of everything that's happened with data throughout years, right? I think usually trust is not something that is in a good place when I, at least that's what's happened to me when I've taken over teams or new roles. And so if I could basically remove that with magic wand, that would be amazing because it would give me a fresh start. I usually, when I've gone into these situations, I had to like earn the trust back of the business team so that we can build the relationship, the right partnerships we can deliver. And I think going back to our expectations conversation a few minutes ago, the inflated expectations created by hype. Is, is continuing to sort of put more pressure on that importance value of trust, which is gonna make it harder for data teams to earn the trust back to deliver. And so it just becomes sort of this vicious cycle that you to, to break out. But yeah, de definitely, uh, many data teams and many data leaders deserve a chance to start fresh, right. And, and that would mean to me. Uh, remove the lack of trust, uh, the business teams may have because of past experiences and, and give new leaders a chance.

ben parker: 50:28

Fascinating. I think it's, it is also quite interesting to hear that a lot of these impact impactful things, it's not on the tech side, is it at all? It's more more on the human side.

juan: 50:40

That's exactly right. And, and I think that, the, the, the successful data leaders that I've been able to work with or have a chance to work we or to know or to talk to, they double down and emphasize that sort of, I think to your point, human side of things, that's what they focus on. That's what they double down on the first 90 days they spend a lot of those days they spend on the humans getting to know the teams, the business stakeholders, right? Know the business stakeholders business, right? When you prioritize that, your auto success grow significantly higher. Again, not diminishing the tech side, but tech side is usually the easier side of things, right? Like it's all well-defined and it's all sort of, you know, you know what a good server looks like. You know what a good database looks like. You can optimize performance, but it's the human side of things in terms of execution, delivery, and it's the expectations. It's gonna make you or break you as a, as a data leader.

ben parker: 51:30

Yeah. Brilliant. Cool. Okay. And then do you wanna, can you share an example where. A data project is overcame, overcame the odds and succeeded.

juan: 51:40

Oh man, there's plenty, right? And I and I, I've had my first set of challenges. In my career. And so reflecting on this, I think there's maybe a couple. I think one was at some point in, in, during my Disney tenure, like working with this team and sort of, again, part of the hype of the machine learning hype back then was we got this request like, oh, we need a recommendation engine. Recommendation engines are cool, right? And so a lot of the challenges were sort of start working with the stakeholder, which was the chief customer officer of the. That line of business that I was working with to say, well, why? Well, you know, I wanna be able to increase sales. I wanna increase penetration of my customers. But having that conversation, which took a while to like understand the why behind we need a recommendation engine is what really sort of allows us to start overcoming the odds of just saying, here's a ticket for a production engine. A quick way out would've be just like, turn around, do an RFP, build the engine, or buy it or model whatever, and just deploy it. Boom checked, done. But once we understood why, right? And what what was important, then we were able to work in partnership with this executive to say, okay, we'll build the engine, but then we need to embed in a business process, okay, what business process we gonna choose? Let's choose the call center. Okay? And so I can tell you like 80% of the work we had to do. All change management, like train the call center agents to use the recommendation of the engine, right? Have them like, you know, capture the data, then measure afterwards what happened when the customer was offered the new product. Right? And that took months and months of work. The engine itself, the algorithm itself was super simple. Like V one was like the, the simplest thing. It was more sort of the whole process and change that. And I think because we, from the beginning as a data team, we took the approach of. Why, what is the business outcome? How do we measure jointly instead being basically a successful problem, which like we prove that. The recommendation engine or the, or the improvement of the, of the sales process through the use of analytical recommendation would increase sales by 50%. At least the population would test it. That was a, a much nicer story to tell the board than the executives, than saying, yes, we have an engine check. And it, it, it took a lot of work to earn the trust of business to basically be able to. Help educate us on the sales process so we could actually understand where and where, what, where was the best place to embed the engine and the analytical, how to change the process, how to measure the outcomes, how and so that, that's one. I think there's plenty others. I mentioned the one about Disney, which we're improving or replacing analytical engine or process in which we never talk to the park operators about the outcomes of the, of the new tool. And to sort that required us tool. Once we deploy the end, we had to go back and. Talk to them, retrain them, do the change management and so that allowed us to sort of save face and save the project. But I think, I think definitely, I think the difference on, on those projects or those initiatives was always engage your business stakeholders to always be a partner, understand why things are being done. Understand the degree of change management that is needed and always lead with that. And if you lead with that, you'll know exactly what to do, where to do it, and how to do it. If you just do it for the sake of doing cool analytical projects. Cool models, cool predictive models, sure you can do plenty, but they will be all theoretical exercises because I, I think something that I live by is that any data solution that is not used by the business has zero value. You can have the most elegant predictive model, the most beautiful machine learning algorithm, the most amazing dashboard. If nobody's using the outcomes of that, of those solutions to drive business value, those rules are worth zero regardless of how many millions of dollars you invested in getting them done. And so enable to, you know, ensure this adoption usage, you have to lead with a business mindset. You have to lead with a, why are we doing this? How is it gonna be used? And how do I get things done? So that, that's what I would say. In the projects that I've had challenge challenges or that have faced obstacles, usually recovering has been always by living with the business, work with the business and just get things done in partnership.

ben parker: 55:42

Yeah, I, I find it fascinating, isn't it? Like some projects can just obviously run so smoothly and in others you come across. Everything goes wrong. Uh, I guess part and parcel are alive, isn't it? It's up and down. I guess it's similar to you, like if your, your sports team one week they can actually perform amazing and then next week it's just, yeah, they're completely different. Team

juan: 56:02

and con context matters, right? The field was different, the weather was different. You have some injuries, right? You have, but yeah. Context matters. Exactly. Context changes and you have to able to adjust to the context and do what's the best you can in the new context. Agreed.

ben parker: 56:14

Brilliant, Ron. Well, it's been a pleasure. Having you on and you've provided some, yeah, obviously you great career story and also some great insights on, uh, the data side. Yeah. Thanks for joining.

juan: 56:25

Uh, on the contrary, Ben, thank you for having me. Like I really enjoyed our conversation and, uh, I hope you uh, uh, you and people find it useful.