In this episode
Jose Martinez, Global Chief Data Officer at Coca-Cola FEMSA — the world’s largest Coca-Cola bottler, operating across 15 countries — explains what it takes to build a data foundation that AI systems can act on rather than one that only produces reports. Recorded live at Ai4 in Las Vegas, the conversation covers the two things that break at Coca-Cola FEMSA’s scale (real-time transactional streaming and petabyte-level volume across many sources), why competitive advantage comes from interconnecting domains rather than storing them well, and how a chief data officer sells foundational data work to a board that hears “governance” and thinks “compliance.” Martinez is direct about the trade-off underneath all of it: business grew faster than technology could keep up, so the current job is covering technical debt and adopting new capability at the same time.
Key takeaways
- Competitive advantage comes from interconnecting data domains, not from storing data well. A clean data lake or warehouse is table stakes; the differentiator is whether you can see correlations between operations and finance, and act on them.
- Not all data carries the same weight. Martinez frames the real questions as whether the data is enabled, available, ready to be used — and whether it’s the right data for the decision at hand.
- AI is logic, not intelligence. Citing Geoffrey Hinton, Martinez argues that feeding bad data through pure logic produces a confidently wrong result, no matter how capable the model.
- Bad information now moves as fast as good information. His example: an LLM that ingests a social platform as its knowledge base will repeat that platform’s misinformation back as an answer.
- Data quality is not an AI-era invention. The same discipline that governed regressions, statistical tests, machine learning, and deep learning governs agentic systems — what changed is the speed and blast radius of getting it wrong.
- A chief data officer has to be part seller, part marketer. Pitching “we need a governance team” reads as compliance and slows things down; pitching lower data-usage cost, faster information, and new revenue use cases gets funded.
- “Fail fast” has a price, and it needs a compass. Every proof of concept carries cost, so testing should start from a specific business question — Martinez’s example is investigating why a territory underperforms — rather than testing for its own sake.
- Scale creates two distinct data problems, not one. Millions of points of sale generating real-time transactional records across financial, commercial, operational, and retail sources; and billions of records — petabytes — that must be processed fast, reliably, and interconnected.
- Technical debt is a current condition at enterprise scale, not a legacy footnote. Coca-Cola FEMSA’s business outgrew its technology, so the team is closing old gaps while standing up a single source of truth.
Chapters
- 0:00 — Why an AI mistake reads to the customer as “this company doesn’t know me”
- 1:23 — Introducing Jose Martinez, Chief Data Officer at Coca-Cola FEMSA
- 2:37 — From Nissan CIO to building a multi-country data strategy
- 4:14 — What Coca-Cola FEMSA does: the world’s largest Coca-Cola bottler
- 5:11 — Data at real scale: millions of points of sale, billions of records
- 6:59 — When business growth outruns technology: covering technical debt
- 7:23 — Turning data into competitive advantage
- 8:38 — Why mindset beats tooling — and what tools are actually for
- 10:06 — Organizational silos, proofs of concept, and the cost of failing fast
- 14:03 — Is there a different data-quality bar for agentic AI?
- 16:36 — Misinformation in, misinformation out: why bad data travels faster now
- 17:17 — Rebuilding trust in data when tenured leaders trust their instincts
- 18:19 — How to sell governance to a board that hears “compliance”
- 20:23 — A highlight from Ai4 in Las Vegas
- 22:02 — What Jose does to stay agile
What separates a data foundation AI can act on from one that only makes reports
The distinction Martinez draws is about usability, not tidiness. Organizations can have data well established in a lake or a warehouse and still get nothing from it, because the questions that matter are whether the data is enabled, available, ready to be used, and whether it is the right data — since not all data carries equal weight. The foundation becomes actionable when it supports interconnection across domains, so a pattern spanning operations and finance is visible rather than trapped in a single-domain view.
Why one usable version of the truth is harder at millions of points of sale
Coca-Cola FEMSA’s scale creates two separate problems. The first is streaming transactional data: selling in real time across many countries generates millions of records daily, spanning financial, commercial, operational, and point-of-sale sources simultaneously. The second is sheer volume — billions of records, petabytes of information — that has to be processed quickly, reliably, and in an interconnected way across several distinct data sources. Martinez describes the company as still moving toward a single source of truth that can handle all of it under one strategy.
Technical debt as a present condition, not a legacy problem
Martinez is candid that business growth outpaced what technology could absorb. The result is a two-track workload: closing gaps left by technical debt while simultaneously adopting the newer capabilities the company needs now. That framing matters for enterprise leaders reading foundational work as a one-time cleanup — at this scale the cleanup and the build run concurrently.
Why AI raises the stakes on data quality without changing the discipline
Data quality and governance existed long before the current AI cycle, through the eras of regressions and statistical testing, then machine learning and deep learning. What changed is packaging and speed: everything now arrives as one automated bundle queried through neural networks. Martinez’s point is that clean, curated, correctly valued data was always the prerequisite for a statistical test, a machine learning model, or an agentic LLM workflow alike — but because usage has increased and answers come faster, bad information now spreads faster too.
AI is logic, not intelligence — and what that means for your inputs
Martinez invokes Geoffrey Hinton’s position that AI is not intelligence but pure logic. The practical consequence is that duplicated, un-normalized, or simply wrong data produces a wrong result regardless of model quality. His illustration is a model that draws on a social platform as its knowledge base: if users flood that platform with misinformation, the model answers with misinformation, because the logic is working correctly on bad inputs.
How to sell foundational data work to a board
Asking a board of directors to approve a governance team invites the response that it sounds like compliance and will slow the business down. Martinez’s reframe is to lead with outcomes: lower cost of data usage, faster access to information, access to information the organization doesn’t currently have in its data lake, and the new use cases that unlock revenue increases or cost savings. Attaching value to the data is what recruits people to the work, because everyone wants to contribute value — and he extends the point beyond data to any function whose contribution is real but poorly marketed internally.
Fail fast, but bring a compass
Testing and proving concepts is the right posture, in Martinez’s view, but every attempt carries cost, and that cost can’t be estimated by calculation alone. The discipline is to start from a high-level view of the value you’re after — his example is looking at a specific domain’s data to understand why sales in an area aren’t where they should be — so the effort has direction. Occasionally undirected testing finds gold; more often it goes nowhere while consuming time and budget.
FAQ
What does Coca-Cola FEMSA do? Coca-Cola FEMSA is the world’s largest Coca-Cola bottler, producing and selling Coca-Cola and other brands including Powerade in cans, bottles, and other formats. Its largest markets are Mexico and Brazil, and it operates across countries including Uruguay, Argentina, Colombia, and others.
What is Jose Martinez’s role at Coca-Cola FEMSA? He is Global Chief Data Officer, responsible for the entire data landscape across 15 countries — building reliable data the business can mine and use so decisions across different areas are made on facts and data.
What makes data a competitive advantage rather than just an asset? According to Martinez, the differentiator is how the data is used: whether it’s enabled, available, and the right data for the question, and whether the organization can find patterns and correlations across domains such as operations and finance instead of viewing each domain in isolation.
Does agentic AI require a higher data-quality bar than earlier analytics? Martinez says the requirement is the same discipline that applied to statistical tests, machine learning, and deep learning — clean, curated, correctly valued data — but the stakes are higher because AI usage is widespread and fast, so bad information now propagates faster.
Why does AI produce wrong answers even with a capable model? Because AI is logic rather than intelligence, in Hinton’s framing that Martinez cites. Duplicated, un-normalized, or incorrect inputs yield an incorrect output no matter how advanced the model, and models that ingest misinformation-heavy sources will repeat that misinformation.
How should a chief data officer make the case for governance to leadership? Avoid the compliance framing. Martinez recommends leading with decreased data-usage costs, faster and more complete information, and the specific use cases that produce revenue growth or cost savings — attaching visible value to data is what gets teams and executives to participate..
About Jose Martinez
Jose De Jesus Martinez Camara, MS, PMP, is an innovative executive currently serving as the Global Chief Data Officer at Coca-Cola FEMSA. Previously, he served as the Americas Head of Data & Analytics at Nissan North America for Finance, with a dual track record as well as Chief Information Officer.
With over 20 years of experience driving organizational excellence, Jose specializes in managing complex IT portfolios and commercializing technology products across the consumer goods, automotive, finance, entertainment, and consultancy sectors. He is a visionary professional known for aligning technology initiatives with long-term business objectives and fostering Agile and DevOps cultures.
In his current global role at Coca-Cola FEMSA, he leads the enterprise data strategy, governance, and advanced analytics capabilities to drive digital transformation and value creation at scale. Prior to this, as Americas Head of Data & Analytics for Nissan Finance, Jose oversaw the data landscape across Mexico, Canada, and the United States, ensuring strategic alignment with corporate needs.
Jose Martinez on LinkedIn
———- Resources ———-
The Agile Brand podcast is brought to you by TEKsystems.
We’re proud to be a media partner for #MAICON26 – Oct. 13-15! Learn how AI can power your marketing and business and help you grow smarter. Use code AGILE150 to save!
Reach your customers with Reddit. Spend $500 in ad spend, get $500 back in ad credit!
Chaser is the only Slack-native project management platform that helps teams turn messages into tracked tasks, automate follow-ups, and maintain team-wide visibility, without adopting another tool. Now integrated with Claude and other GenAI tools. Learn more at trychaser.com and use code AGILEBRAND for a 3-month free trial (normal trial is 14 days).
The most influential minds in software, AI, and engineering leadership will be at WeAreDevelopers World Congress North America, September 23-25 in San Jose.
Start building your own apps with Replit and get $20 off.
Enjoyed the show? Tell us more at and give us a rating so others can find the show.
Don’t miss a thing: get the latest episodes, sign up for our newsletter and more.
Check out The Agile Brand Guide website with articles, insights, and Martechipedia, the wiki for marketing technology.
The Agile Brand is produced by Missing Link—a Latina-owned strategy-driven, creatively fueled production co-op. From ideation to creation, they craft human connections through intelligent, engaging and informative content.
Transcript
[00:00:37] Greg Kihlström: Hi. I’m Greg Kihlström, host of The Agile Brand, and here’s a question for you. When an AI system gets something wrong about a customer, the customer doesn’t experience it as a data problem, they experience it as a company that doesn’t seem to know them. Agility isn’t really about moving faster, it’s about being able to change repeatedly without the company starting to contradict itself, and that depends almost entirely on whether there’s one usable version of the truth underneath everything. Today we’re recording live at AI4 here at The Venetian in Las Vegas, and we’re going to talk about what it takes to build that. We’re gonna be covering a few areas relevant to any enterprise leader. What separates a data foundation that AI systems can actually act on from one that only produces reports? How a company operating
[00:01:23] Greg Kihlström: across many markets and millions of points of sale keeps its decisions consistent as more of them get automated? And how to make the case for foundational data work when the payoff doesn’t look like a campaign result? To help me discuss this topic, I’d like to welcome Jose Martinez, chief data officer at Coca-Cola FEMSA. Jose, welcome to the show.
[00:02:23] Jose Martinez: Thank you. Thank you for having me.
[00:02:25] Greg Kihlström: Yeah. Looking forward to talking with you about this, and definitely a lot of talk about AI in the air here at, at AI4. before we dive in though, why don’t you give a little background on yourself and your role at Coca-Cola FEMSA?
[00:02:37] Jose Martinez: Yeah, sure. my, my background is a little bit more technical background. I was, previously was CIO for, for Nissan in Mexico six, seven years ago, and I, I started thinking about kind of like putting a baseline in Nissan for, to have a, a great, platform for managing the data analytics and stuff like that. So I created the data strategy for, for Mexico by work. So, they told me that I could continue moving forward with, with the rest of the countries, US and Canada and stuff. This is, this is how I enroll entirely on the data landscape, I will say. but before that, I was doing, I was doing enterprise architecture and, and working
[00:03:22] Jose Martinez: with so many other type of technologies. So I, I will say it helped me. It helped me understanding where we’re heading to because at that time, AI was not a thing. Was… It, it was, it was the, dashboards era, you know-
[00:03:37] Greg Kihlström: Right
[00:03:37] Jose Martinez: … the analytics era. So when we jump into AI, I already knew what, what was coming. And, and now in Coca-Cola FEMSA, I’m the chief data officer. I’m in charge of all the data landscape for, for 15 countries across also America and Mexico. And, basically what I need to do is, is to have reliable, data, we can use for different purposes. So we can mine that data, and, and work with that data for different areas to take decisions based on that.
[00:04:14] Greg Kihlström: Yeah. And, and maybe for the, for the listeners, can you explain a little bit about what exactly does Coca-Cola FEMSA do?
[00:04:21] Jose Martinez: Coca-Cola FEMSA is, is a bottler. It’s the biggest bottler in the world. Is, is a, is a bottler that sells the most Coca-Colas around the world. So we, we have the production of the Coca-Cola, different, different type of, presentations of Coca-Cola and other products as well from Coca-Cola, like Powerade, Coca-Cola obviously, the, the other type of flavors as well. And we, we sell Coca-Colas in cans, in, in bottles, in different type of presentations as well. The bottler itself, itself is, is operating in different countries, being the biggest ones are Mexico and Brazil, but it’s operating across,
[00:05:06] Jose Martinez: Uruguay, Argentina, Brazil, Colombia, a- and so on.
[00:05:11] Greg Kihlström: Great. Great. Wonderful. So let’s, let’s dive in here, and I wanna start with talking about data at real scale ’cause stuff, you know, as the, as the largest bottler [laughs] you know, you’re, you’re certainly dealing with some scale here. So let’s start with the physical reality. So you’re operating across multiple countries and serving an enormous number of points of sale, many of them small independent retailers. What does that scale actually do to the data challenges that you have? You know, what, what’s the hardest part of getting to one usable version of the truth?
[00:05:45] Jose Martinez: I will say the, um… There are two things. The, the streaming, the transactional data, because we, we are generating millions of records every day, because, we are selling in real time. And we are, we are also catching, different k- type of, data: financial data, commercial data, operational data, retailers, uh point of sales data and stuff. Everything at once in, in, in, in real-time. And the second piece, it would be the amount of data that we generated. We’re talking about billions of records. we’re talking about, petabytes of information, that we need to process, that we need to work with in, in a fast way, in a reliable way, in a interconnected way because we have several data sources, several. So, right now we are, we are in the, in the, challenge of trying to create this s- one single source of truth that can handle all the information at
[00:06:59] Jose Martinez: once in w- w- with one single strategy. and we are, we are towards to that. because business grows really fast. Technology was not able to catch up in time, you know, with all the growing that we were having. So right now we are kind of like, covering some gaps from technical depth and at the same time moving forward with the, the new technologies that we are, we are using right now.
[00:07:23] Greg Kihlström: Yeah. And ta- to talk about that moving forward, you had a panel here at, at AI4 talking about turning data into a competitive advantage. So that certainly seems like-
[00:07:32] Jose Martinez: Exactly
[00:07:33] Greg Kihlström: … the, the, the right theme there. So in a, in a practical sense for, for your organization, what, what does that look like to use data as a competitive advantage? Is it about operational efficiency, better last mile delivery, more effective trade marketing, all of the above or something else entirely?
[00:07:51] Jose Martinez: It, it, it’s all of the above and more. you know, data, data is an asset. Da- the data is a new oil, as everybody’s saying, and the way you use that data is, is what will differentiate you from the rest of the competitors. You can have the, your data organized, you can have your data well established, you know, in a, in a data lake or, or data warehouse or whatever. How you use that data. Is the data enabled, available, ready to be used? Is the right data there? Because not all data matters the same. There, there are different weights of, of the data that you’re using. And also, are you looking at the data as a whole, or are you looking at the, as a, the data as a single, in a single domain? How you interconnect the data.
[00:08:38] Jose Martinez: So do you… Are you able to find patterns between one domain, let’s say operations and financial? Do you see the, do you see the difference? Do you see the correlation? Do you see where, where they are-
[00:08:51] Greg Kihlström: Yeah
[00:08:51] Jose Martinez: … getting mixed? That’s where you get the competitive a- advantage. If you are able to see that, and, and it’s not just the tools, it’s also the mindset. We’re talking about having a company that thinks about taking decisions with facts and data. So if, if everybody in the company is thinking in that way, everybody is a, is able to find those interconnections and then use them. Tools are for automation. Normally, what you want to do with an AI solution, with a dashboard or something else, is to automate a result, to have a result. How you get the most out of that, those tools? Knowing what you’re looking for. If you don’t know what you’re looking for…
[00:09:36] Jose Martinez: If you can ask any AI, if they don’t know what you want, they can give you even, even something worse. They c- they can give you hallucinations or stuff like that.
[00:09:46] Jose Martinez: So it’s kind of like finding the mix. I would say that’s how you find the competitive advantage.
[00:09:53] Greg Kihlström: Well, and I think that’s a great way of, of framing, you know, why, why we’re doing… You know, it’s, it’s, as fun as this is, right? It’s, it’s to, to your point, it’s to build something that we can act on as an organization, right?
[00:10:06] Greg Kihlström: So, alongside the, the data silos and some of the tech data and, and all of that, there’s al- large organizations are going to have organizational silos and, and things as well, in addition to some of the legacy systems and, and things that you mentioned. To be successful in, you know, in, in your approach so far, you know, should you pr- prove things out first? Like, is it, is it a matter of a lot of proofs of concept? do you integrate broadly? Like, what’s the, what’s the, what’s the right approach to follow here?
[00:10:40] Jose Martinez: Yeah. You need to be able to, to fail fast, I will say. Fail fast and try again.
[00:10:46] Greg Kihlström: Yeah.
[00:10:47] Jose Martinez: But you need to, you need to balance the cost of doing that because e- every, every, opportunity you have to test and to prove a concept is also coming with a cost associated with that. So how much are you willing to, to give in order to, to learn and in order to, to get the most out of it? That’s something that you cannot estimate, you know, by, by, doing the calculation. You need to kind of like think about bus- think about how you can get a result, how, how you can get value of what you are trying to g- to achieve. If, if you’re just testing by because you, you want to test, you’re driving something that it’s not,
[00:11:32] Jose Martinez: it’s not going anywhere. Probably you can find gold, right? Yeah. Wow. We nailed it. But that’s not, that’s not always the case. You need to have kind of like a, a, a compass. Hey, I’m looking to see the data of this domain because I’m trying to see if I can sell more Cokes in this area. I don’t know what’s happening there. Probably I can, I can increase my, my numbers there. Let’s see what’s happening there. So you have, you have at least a, a high level view of what you want to achieve. And that’s how you focus your efforts. And yeah, you need to, you need to have your data available so you can, you can test and you can prove. And that’s, that’s how we are working today. We, we are trying to, to test fast, fail fast, do fast, and, so we don’t lose time trying
[00:12:23] Jose Martinez: to fix and figure it out if we, we did something good or wrong.
[00:14:03] Greg Kihlström: [gentle music] We’re here at AI Four and, and certainly the term AI, as you mentioned, you know, it… You would think that AI was invented a couple years ago when, you know, it’s been around for decades in various, various forms. But, you know, there’s also, there’s a lot of types of AI. So, you know, everything from some of the, let’s say, less complex automation to giving insights, some of the things that you just mentioned, to agentic systems that are doing a lot of things either autonomously or semi-autonomously. From a data quality standpoint, how do you think about that? You know, is there a different bar for quality when, when you’re looking at some of these different types of, of-
[00:14:47] Jose Martinez: Yeah, for sure. It’s, it’s even… You know, data quality, data governance has been there as well for a long time already. as you mentioned, AI as well in some kind of forms. Before we have, it was regressions, it was, naives and, and it was a lot of type of statistics, you know, tests that normally people were working with, with data. Then, machine learning came in, deep learning came in, and that, that was the new AI. And then now, everything is in one single package, automated, and you are asking questions in, in, in using neuronal networks.
[00:15:27] Greg Kihlström: Yeah.
[00:15:28] Jose Martinez: So data quality was present in all that since, since then and before. So you need to have data that is clean, that is curated, that works, that has, the right value, so you can run a te- statistical test, run machine learning, or now use an LLM model to do something agentic if you want. If you, if you have tons of data that is not normalized, that is duplicated, that probably is not the right data you need to have, you will not get any result. You can have the biggest brain, entropic kind of whatever. You will not have the result you, you’re looking for
[00:16:14] Jose Martinez: because, the data is not well, managed and, and, and that’s why it’s important. And it’s even more important today because we are… Everybody is, is, is using AI somehow right now, and the usage of that has been increased. It’s faster. So if you are getting also bad information faster.
[00:16:36] Jose Martinez: Right? You can see Grok, for example. If you, if you, somebody in, in X is-
[00:16:43] Greg Kihlström: Oh
[00:16:43] Jose Martinez: … is generating a lot of misinformation data in X, Grok takes that s- that information as, as, as the knowledge base.
[00:16:52] Greg Kihlström: Yeah. Yeah.
[00:16:52] Jose Martinez: And you can ask a question to Grok based on misinformation that everybody’s spreading, and Grok will answer you with misinformation, right? So they are not intelligent, and even Geoffrey Hinton tells that. AI is not intelligence. It’s logic, pure logic. So if you, if you are using bad data, based on that logic, he will give you a result that probably is not the right one.
[00:17:17] Greg Kihlström: Yeah. Yeah. Well, and I, I wanna talk a little bit about the, the people and the organizational part of that too, because I think there’s a lot of… And not even, you know, speaking specifically about your organization, but, you know, at, at large enterprises with disconnected data, there, I think there’s often-
[00:17:34] Greg Kihlström: … with good reason, there’s a level of mistrust in some of the data because it’s been, you know, coming from all different sources. There’s not a single source of truth. And so, you know, as a, as a chief data officer, you know, how do you look at the task that many others like yourselves are in kind of rebuilding trust in the data? Because for lots of reasons, but also they need to be able to… Teams need to be able to trust what the AI is going to do, especially when you have a lot of leaders and, and others that have been in an organization for
[00:18:06] Greg Kihlström: … you know, sometimes 10 or more years, they think, you know, they, they have a, a way that, that things got done and kind of they trust themselves more than the data. So, you know, how do you, how do you look at that, that task?
[00:18:19] Jose Martinez: you need to be a little bit of a seller, and a little bit of a marketing person. You cannot be no longer a technical/data scientist guy. You need to, you need to think about also business. And what I mean by business is you need to show the value of having the right data, of involving the right teams to understand the data that we are working with, so you know, the, the, the, the co-stewards, right, from different areas. So if, if you go with, with… If you go to the, the, the board of directors and say, “Hey, I need this, this team to do governance,” they will tell you, “What? Why? What, what, what is that? What, what’s that, what’s that for?” I mean it’s, it’s going to slow me down, right?
[00:19:09] Jose Martinez: It, it sounds like-
[00:19:09] Greg Kihlström: It sounds slow
[00:19:10] Jose Martinez: … sounds like, sounds like compliance, right?
[00:19:13] Jose Martinez: But if you, i- instead of saying that, you say, “Hey, I think we can, we can, we can decrease costs of usage of data. I think we can get faster information. I think we can get more information that we don’t have right now i- in, in the data lake that we’re using. I think we can… With that information we can, we can create more use cases like this, this, this, this, that can give us, increase of revenue, cost savings, and stuff like that.” If you are start thinking about that, you are adding the value to the data. When you add the value to the data, it’s, it’s more, it’s more often that you can, you can involve more people because they want to do that. They, because everybody wants to add value. Everybody wants to, to put their mark
[00:19:58] Jose Martinez: in the, the company. So you need to be able to convince them that that’s the right way to, to work with, with the data. And that’s, and not just data. E- everything that you do, even compliance or even all those areas that doesn’t seems like are giving any value, they are giving. They are, they are doing that, but we are not able to sell that because we… Normally we are engineers, we are people that are technical, that we don’t think about that, but we need to, we need to do that.
[00:20:23] Greg Kihlström: Yeah. Yeah, love that. Well, Jose, thanks so much for joining today. I’ve got two last questions for you as we wrap up here. first one, you know, we’re here at AI4 in, in Las Vegas. What’s been a highlight of the conference for you so far?
[00:20:37] Jose Martinez: it’s, it’s a really nice place. I mean, being here with all the people that, you know, talks about AI, talks about data in one single place is, is a really nice place to be. because normally you don’t see so many people working in the same area at once in one single place. So you can talk about this kind of stuff, this nerdy stuff [laughs] that normally you don’t do that very often in other areas. You can do that very well. You can look at… You can lose yourself a little bit. So that’s, that’s great. And also, you know, look, look at new technologies. It’s, it’s always nice because it gives you ideas. Yeah, everybody’s using AI, but how they are using it is the right, is the right question. So if, if someone said to me, “Hey, you
[00:21:22] Jose Martinez: can ask a question to the AI.” I know. Yeah, but someone says to me, “Hey, this is going to be working with you in the background. I will organize all the information. I will do this, I will do that for you. You, you don’t even notice.” That’s a new search that probably could make more sense, for, for some companies to have because they have a, a, a tons of people working in the background doing, doing that. So you start thinking about the value, right? Oh, okay, so I can do this and I can decrease this cost and I can do that. So this place is, is a nice place to start, you know, think about that and, and, and find out new ways of work.
[00:22:02] Greg Kihlström: Yeah. Well, that’s great. And last question for you, what do you do to stay agile in your role, and how do you find a way to do it consistently?
[00:22:10] Jose Martinez: For me, I will say it’s automatic. I, I love, I love to be learning every day, literally learning every day. every day I, I read something new, a new paper, a new technology, a new conversation, survey, whatever. it’s… I, I’m, I’m… Not just because I want to be, you know, on top of the subjects, b- but because I like it, you know? I, I want to see things and ask question things because I don’t, I don’t take anything for granted. No, no, no matter who, who says whatever they said, I always look at that and say, “Hey, is that right? Is that the right way to do it?” And that’s how I start thinking myself and motivating myself on, look at things in a new way, or, or even try to, to implement something that I saw and, and see if that works. I have found out that something that is hyping right now is n- it’s not new. It’s kind of like they are just i- inventing a new term to name something that was already existing. So yeah, it’s marketing at the end because everybody wants to sell something, right? I try to kind of like see if that makes sense, and also how can I apply that to my real world.
