#490 From Data to CTO: Keith Cassar’s Evolution in Tech Leadership

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Mehmet: Hello and welcome back to any episode of the CTO Show with Mehmet today. I'm very pleased. Joining me, Keith Cassar, CTO, at Game Lounge. Keith, the way I love to do it is I keep it to my guests to introduce themselves. Uh, you have a fantastic background. I know you've done [00:01:00] a lot of things on the data side and now as a CTO and you work in very cool places as well.

So I gonna leave, you know, the stage to you, tell us more about you, and then we are gonna start the discussion from there. So the floor is yours. 

Keith: Thank you. Hi, my name is Keith. I'm the CTO at Game Lounge. I've been, uh, working with Game Lounge and affiliate marketing company for the past, uh, four and a half years.

And, uh, I, I, I moved from, I, I started as a head of head of data and uh, that's where I kind of also want to share. Where my background is coming from, uh, data engineering and, and I, I, I followed through and kept on working until, uh, becoming Chief Data Officer. And for the last, uh, year now I've been, uh, CTO, um, which was quite, quite a jump for me from just focusing on one area, which is data and data analytics.

To, to, to the whole at Game Lounge, [00:02:00] but, uh, a very rewarding one. Uh, my, my previous jobs before joining Game Lounge, so I live in Malta right now. Malta's a small island and the Mediterranean. Uh, and before that I used to live in London. Uh, I worked, uh, moved to London when I was around. Uh. 27, 28 to do a master's degree in information security.

But at the same time, I was always very much in love with data and data engineering. I was very lucky to find a, a job with a company called King, the makers of Candy Crush. Where there I I, I joined at Hatan when, when the games developed by our company, were by the company, were still, uh. Um, not well known, and so I, I, I, I learned a lot over there on, on, on the principles of data engineering and on democratization of data.

So that is me. 

Mehmet: Great. And, uh, again, thank you for being here with me [00:03:00] to today, Keith. A lot of things. We, we gonna discuss the first thing which you just mentioned in your introduction, the, the, the shifting, you know, from, uh, data to, to, to coding. So. Um, I know like some people, they, they kind of think that these are like, of course all falls under technology of course, but you know mm-hmm.

Being, you know, starting because I, what come to my mind, uh, when I say data, so you start as a data engineer and then, you know, like you, you take your way until you, you reach like, um, chief data Officer, and the other path is you start as a developer. Yeah. And of course, like there are a lot of branches from out there.

Mm-hmm. So, uh. How, how this journey was for you and, you know, do, do you think like we have kind of a merger that is happening with all the AI thing that's, uh, yeah. Happening today? 

Keith: So I, I started working back [00:04:00] in 2004, so nearly over 21 years ago. And I started as a software developer where, um. Development was still, um, the, the main arm of, of technology and, uh, we used to develop applications which were.

On premises working and obviously data, even though it was important, but we didn't realize that all right, we're saving stuff in a database. But let's remember at the time, um, the cost of, uh, of, of physical memory was very expensive as well, or more expensive than it is today. So, so, so the most important thing at the time was to, to save the, as the, the most important information and saving it in the least amount of space.

Because obviously the real estate of, of hardware was, was quite big. Um, when I, when I started then at, at the, at that time still, I [00:05:00] mean, uh, the databases were already quite big and important for sure. But I, I, my, I always felt that I was much more of a data person rather than just building front ends and, and, and, and the backend of, the backend of, of the application itself.

Uh, this, this then kept on growing. And in, in, uh, in, at one point I realized that. I, I wanted to study a little bit more and, and, and getting into security. So after doing that course in information security, which I also, um, wanted to specialize in, uh, in, in data, in smart part technology and databases, uh, I, I, I, I, I, I, I moved to the uk.

Over there. They, the roles of, of whoever works in data, were still no data engineer wasn't, still wasn't a title yet. Mm-hmm. I remember one of my first roles was. SQL [00:06:00] developer, for example, and then database developer. It was always like that and, and I could understand why because the word developer used to always just be on its own.

And if you're just focusing on one specific area, it's like you are just working on that. And I think, I think looking at, at the story of it, the trend of software engineers versus data engineers. Started shifting, uh, and growing, uh, in the early two thousands. But what I noticed, and I've even seen some, some.

Some charts about this is that the title software engineers stayed very, very flat all through these two thousands. But the software, the data engineering role kind of crossed the line and became more popular. And this is on popularity when, where p where companies are looking for developers. Okay. Uh, around the year 2016 and 2016, it crossed the line and the term data engineer became a real term.[00:07:00] 

And, and, and, yes. I mean that's, that's I think where even our comp, my company where I used to work on at the time, king. Um, also we decided to, to give, give us a name, which was data engineer and kind of also create that, that career path for people who work solely on, on the data part. 

Mehmet: Right. Now. Let's talk a little bit about, um, you know, data.

If that's, if that's possible, I. Yeah. And I know like, um, you, you have a passion about making data usable, right? So, um, when we say this, you know, when, when you, when we say that we gonna make the best out of our data mm-hmm. Beyond just, you know, like the simple things we see, maybe kind of a simple task, um, in especially, you know, maybe also in the domain you are in.

So you've been in, in, in the gaming and I think also Yeah. Here where at Game Lounge as well. So what are like. Uh, you know, the real meaning when we [00:08:00] say we're gonna make, uh, data, uh, you know, usable, what does it mean? Like, if you want to, you know, explain it to, to, in simple terms, let's say this. 

Keith: Okay. It's a very big passion of mine and, uh, what we're pushing, what I push for and what I've learned that is important is, uh, to be data driven.

Data driven, meaning taking decisions based on data. Now. To be able to do something like that, data needs to be available and in everyone's hands. One thing that we normally find difficult is who should have permission to see the data? Now, let's skip that line and say that we already have the parameter and we know who has the data.

Who, who is a, whose data, who has availability to that data. If you have. Access to the data and you are in the know of it. My most important thing is [00:09:00] that you're using it and that you understand what it really means. And this is where I, I always push for data availability. So data availability then has two other sides.

There's the side, which is having the data and being able to. Play with it. Sign testing, learn, understand the trends. Have someone to explain to you what it is, but also having the data available in a timely fashion. And this is also something important because if we are treating our users and giving them data about the data is already expired or like old enough that one cannot take decisions on.

It's not used, it's not a, it's, it's, we're, we're, we're still in step one. We want to also always have systems and, and, uh, give, make, make data available to our users in a timely fashion. And when, where the user is primed already, and being data led. [00:10:00] 

Mehmet: So, Keith, you touch on multiple things. I, I like you mentioned availability because I think people forget about how important is this, like securing the data and, and making sure securing it, not only from laws, also securing it from, especially if you are in a, and I think, you know, everything is regulated nowadays, so you need to make sure that the privacy is there.

And you mentioned also about the speed of the data, which is, which is awesome now, um. When you say data-driven decisions, right? Mm-hmm. Um, so in your opinion, and, and this is why the whole show is called CTO show, because you are the CTO, so you, you translate, you know, technical to business and vice versa, right?

Mm-hmm. Now, if, if, if we want, you know, to, and let's, if you want take example from what you're currently doing. So if we want to, to have this. Um, transformation [00:11:00] from raw data into decision, uh, ready insights. Yes. What are the approaches, you know, that usually, you know, you think are useful?

And feel free to give me examples from either the work you did before at King or what you did you do today with the Game Lounge. So, so we gave, you know, idea to, to the audience about. How we can do this transformation from raw data to something really decision ready. 

Keith: So let's, let's, let's now take it on, on where I'm working right now with Game Lounge.

Game Lounge is an affiliate marketing company. Affiliation works in a way where you have partners, you are, are working with them to provide a service to them. And, uh. Once you provide a service, so a part, someone then comes to your site, finds what they want, you redirect them to, to your partner's website, you want to know if that was actually, um, [00:12:00] a, a good thing if, if, if they are happy with, with what they've seen.

Um, normally you wouldn't know anything unless then the partner will then send you the data. One. One problem that I found here at Game Lounge, for example, was that the data wasn't granular enough, and this is where I'm, I'm getting to your question. Mm-hmm. Um, the data that we are looking for, we definitely, when we're looking at the raw format of it on its own, it does nothing.

We then need to transform it and apply certain rules to make sure that the data that then we are providing to our. Users to our stakeholders is trusted and governed. By governed it means we mean that any user trying to to pull out any information or intelligence out of it. Um, they always come up with the right numbers and let's remember that behind or on, on [00:13:00] underneath.

The nice dashboards and ways of users being a data scientist, being, uh, marketing manager, that they are analyzing the data. There is a web of links, of tables of structured and unstructured data that we are linking together. Um. What we definitely need to make sure is that that layer, right, we, we govern it, we are owning it, and we are making sure that it's, it's okay, it's safe.

Safe meaning it's accurate, and by when, once it's accurate, I feel, I say it where it's safe because then I know that my stakeholders are trusting the numbers and then a number that will be will come out from the system. Will be correct, will be accurate. So then there's the accuracy part. 

Mehmet: Right now we are talking data and we cannot talk about data without talking about AI also as well.

So the first thing, [00:14:00] uh, and let's take what you're currently doing at, uh, Game Lounge, uh, Keith. Mm-hmm. Any, any use cases that you found useful because. And maybe I don't like loaded questions as, as, uh, some of my guests, they tell me you're asking loaded questions. I try not to, but the reason I'm asking because not always there is a use case for ai, but just out of curiosity, within, you know, what you're doing today, like what are the, the, you know, the use case you found useful to utilize ai, especially because you are very much into data and we know like AI cannot work in a good way without data.

Keith: Exactly, and let's be fair, ai Now, it took a, a, a term on its own and it's becoming more popular thanks to the rise of, of LLMs and, uh, the likes of JGBT and all that. But until two, three years ago, anything to do with ai. Was either linked to an application of machine learning or, or something of that sort, [00:15:00] or something that the data scientists used to do.

That's where AI used to sit, and I remember from the first data scientist I spoke to and I worked with back like 10 years ago, the first thing that they, I always heard was, not everything needs ai. Let's start with numerical analysis. Let's start with things to do with prediction. Prediction is not ai.

Prediction is, is the mainly, uh, transform nu numerical transformations and, and numerical analysis and statistics. And that's where normally we start. In fact, one of the first things that, so that Game Lounge, we, we, we did now, especially because. AI hasn't just become just something that you're gonna be using within your business intelligence tools and your, and your tech tool, tech suite.

All right. Is we wanted to, to get a, an AI innovation person ahead of AI and innovation to make sure that, [00:16:00] um, we are also training. Everyone in the company to, to, to, to start using ai. And, and for me that, that, that was an important step to start. Also differentiating what we're, what we are talking about when we are talking about AI predictions.

And that's where I think I'm, I'm going to go into what you were asking before, um, the headaches start because. Um, there is the part where we are trying not to reuse, not to redo, recreate things twice in the sense of if we have already the right tools and the right people for us to do predictions, to do to, to, to, to, to look at time series forecasting and all that.

Why are we now trying to also do that using. Uh, by, by getting data from our internal tools, from our govern data, from our internal part, and putting it [00:17:00] into a system, for example, like, uh, JGBT, which would also give us the same answer, but without the, the confinements and the rail, the, the, the railings, um, the side that, that we are trying to create.

So, education, AI education is very important at this point. 

Mehmet: R Right. So one thing which also comes to, to the mind Keith, and now you, you came from your, the previous company King, and I'm sure like it was like a massive infrastructure and you know, kind of abundance in whatever resources you need to Game Lounge.

And I'm sure like you had to build that. Mm-hmm. Now, some people might think, okay, if currently I don't have the architecture. Both from hardware and, you know, all the mm-hmm. Proper designs. 

Keith: Yeah. 

Mehmet: How, how I can start and is it possible to start on a small scale and then grow it later? How can, how can I scale?

I would say with [00:18:00] limited resources. 

Keith: So the beauty of, of the cloud is exactly that. And, uh, in fact, that's what what I noticed that, uh, we, we could do in a company like Game Lounge that we started with a very small data set and little requirements or ideas and specifications at the time. And then knowing that we could grow.

Um, I, I don't want to mention one versus the other because both Microsoft, uh, Google, and, uh. Uh, AWS they all have their own setups, but what I found really working is that find a system that you trust or that you've worked with before or you are ready to learn. Um, all you need is a couple of people and break it down into, into three sections, in my opinion, where, where it comes to what you want to collect, where it comes to your data.

How you want to present it at the very top layer [00:19:00] and the transformations that are needed in, in, in the middle. What I now see also that would also definitely help is, uh, looking. So I did this last time five years ago, if I had to redo it now, I definitely can be using much more, uh, applications that are all linked with ai, that, that can do my job faster and more efficient.

It doesn't mean that I wouldn't need. Same, the same type of roles and people. So I, I, I can, I, you would still need data engineer and the developer, but definitely, um, I, I'm just con making a, a, a simulation, a contrast between kind of five years ago and now, the, the nothing really changed other than the tools, how to make it easier.

The most important thing is that you're understanding the, the, the data sources and, and what you want to answer. So ask more, ask a lot of questions. That's always the thing that I always [00:20:00] is my motto. Ask questions. 

Mehmet: Absolutely. Keep, stay curious. I would say stay. Yeah. Yeah. So, Keith, just because you mentioned this and this is, you know, an unplanned question or quick question actually.

Yeah, go for it. Because, um. So the utilization of AI for writing code. Mm-hmm. Because you, you mentioned something like, okay, I don't, I don't need less people, but I can, you know, still utilize ai. Mm-hmm. Um, what's your point of view? You know, I like to ask. Everyone, especially CTOs and you know, engineers on, on the show that comes up, uh, about their opinion.

Like how are you seeing really the AI coming good in coding and you know, are we really reaching this phase where, you know, they would be doing, let's say, the job of the junior developers and then, you know, the others would be just supervising or maybe. Spending more time on the more complex task. Qa, how are you expecting the AI to, to, to be, um, performing?

I would say in an organization, in a technical [00:21:00] organization. 

Keith: So me personally and as a company, we are embracing ai. Mm-hmm. And when I say embracing ai, we want to use it and utilize it as much as possible. The main reason is not to. Reduce the amount of headcount that we have, but to have actually our headcount more empowered.

For example, the junior developer today, I feel they are super lucky that with the expectation is not that you go for an interview, you're asked 101 questions and you're expected to know everything by heart, but you are ready and available to understand the concepts. The concepts are still important that you know that, but then again, I'm expecting you to, to use.

A, a number of tools that the company will be, um, approved by. All right. To make sure that, uh, you are, you're, you are giving me the best result ever. And what we definitely are doing as a company is definitely, we're, we're not going to [00:22:00] be, um. Putting production code, which is just generated by ai, actually all we're, we're, we're trying at the moment still to, to, to send box it as much as possible for testing purposes because we don't, we, we want to make sure that what comes out.

Into our live websites and our live systems has been regularly tested. And then the, and the testing part, most probably we want to use much more AI as well, because if, if testing means testing something over and over again, where if something needs to be repeated. More than once. Definitely there is a good use to look at ai.

For example, this coincidentally it's go, it's reminding me of, uh, of a workshop we've done, um, uh, already in the beginning of the year where we wanted to try to help our development teams think where they can apply AI in a way without making them feel [00:23:00] that they are replacing their jobs. Definitely that's not what we want.

So what we did was we had an exercise where we told them, um, list down a, a typical day of yours. And they started listing down one step after the other, what they were doing. Um, and then after listing everything down, we asked them just right next to each one, what you want to, if you want to do more of this thing or less of this thing.

Fun enough after doing this or this exercise and looking at what they want to do less of. Our intention was to tell them whatever you wrote that you want to do less of is the thing that we would like you to try to, uh, automate either via AI or via something else. What we believe now is that with AI you can automate much more things that until two years ago, you might have said, oh, this is too complex.

This requires. A lot of rules. This requires me to explain it into [00:24:00] so many little bits that the computer won't understand it. Whereas now, I think through the tools that we have right now, just saying what we mean, it can be translating into many, many ways. 

Mehmet: I, I like this approach of, uh, writing things down and seeing what's the tasks that you, you don't want to do, and then yeah, try to go automate it.

Absolutely. Um, and talking about that, Keith, um, so you've built, you know, a high performing team, right? Mm-hmm. And, um, and you scaled it also very fast. So what kind of framework do you use for building high performing teams? Uh, especially. You know, and your sector is, is something like push, you know, it's, it's very demanding and yeah.

Uh, we talked about the data and how it's important for your stakeholders. So tell us more about, you know, the framework you used as a CTO to build your team. [00:25:00] 

Keith: So I, I, I've been raised and I believe a lot in, in the agile framework of how, how, how things work and keeping it flat, not creating so many layers.

And I feel like the most important part in teams to be really functioning well is to be cross functioned. So we try to create cross-functional teams. That work together, that, uh, really feel that they are working as a team. And obviously we were looking at T-shaped people and the sense of people that, uh, are experts in their area, but they can, they can relate to the other areas in, in whatever team they are.

We don't have just data teams. We have, uh, backend internal tooling teams. We have, uh, teams that work on our WordPress sites. And definitely the part which we really feel that is really important for them is that they are understanding of each and every role. They can definitely work together. [00:26:00] Now, agility for us, it's important because we want to pay fast.

I know these are things that most probably heard over and over again, but, uh, I, I feel that it's still the same. So sometimes we don't want to know six months down the line that something that we're trying to build. Not, is not good enough for us. So we definitely try to want things that are open for feedback.

They try to do whatever they can to, to understand and to create, to do those design sprints where possible. So that when creating also new, new, new, new applications, new technologies, which we are trying to build for our internal and external users, we, we, we built past, and we, we, the, the idea is always, let's not immediately think of growing the team in numbers before we understand really what is needed.

Because we don't want to then, um, over [00:27:00] employ like what happened in other customers. Companies that you always hear, they didn't hear like what happened during the COVID era and then end up in a situation where it's not sustainable anymore. So we really care about the things that we are building and that's what why we, we try to use this, this framework and methodology.

Mehmet: Yeah, I think Keith, what you just mentioned is like beyond also the framework and, you know, um, the Agile and you know, the, the other thing you mentioned, it's the culture. Yeah. And culture is something I discussed a lot and I like to discuss it because, you know, the more we discuss and I don't see a. Any harm in keep discussing sometime the same thing, because in my opinion, we need to talk more about some stuff right now, when, when it comes to culture.

And first of all, I'm not sure, maybe you can answer me if possible. Um, so because you mentioned COVID also, so. Remote teams, um, especially for technical [00:28:00] teams, for developers, and you know, even product managers and so on. Um, what have you seen working from culture? And when I say culture, you know, like, you know, the culture, the company culture, and as a CTO, how much role do, do you think like, um.

Uh, in general, like the CTO should play in also keeping this, especially because now as I was saying, we talk about, you know, flexible working remote work and, and so on, especially in a post COVID era. And you've seen it both like before and after. So yeah, what you can tell us about this,

Keith: it's, I know that different people. See it in different ways. And, uh, I, I, I, I think most of us, everyone who worked before COVID, um, worked in an environment where. You are near the, always at the office working there, but I've [00:29:00] seen also that before COVID, like a company like I used to work for, we also used to try to be flexible enough rather than thinking about work from home or or hybrid or whatever.

It's about flexibility. And that's, that's, that's part of the culture. Um, I feel that we have to start from there. And me personally and, uh, the work companies I've worked for, and I, I now, I am also leading here at Game Lounge. We believe in that flexibility. Flexibility in the sense of making sure that you, your teams are comfortable because your teams produce the best when they feel that they are.

Trusted and working from wherever, let's say, wherever in the sense of from the office or from home, is actually telling that, telling your, your employees, your, your team, that, that we trust you. Doesn't mean though that doing that, it doesn't come with also determin and conditions of, [00:30:00] of expectations of, you know, people are working.

But what do we definitely want to, what we are doing here at is that we, we want to provide, make sure that our teams are working in a place where they're feeling trusted, they're happy, and they feel that they're producing. So we. Definitely see the need of having the right tools for the teams to work with tools at in our offices, but also tools which are online.

So anything that used to be, I remember back in 2015, having wide boards where you are moving sticky notes from one place to the other. We need to have the same. Tools online and not just having the tools, having the right training to use them. So you need also the right people to make sure that they know how to use these tools.

Because I've seen a difference also having an agile coach that was a hundred percent trained to have people working with them in the office. Whereas having an agile [00:31:00] coach that really knows how to use, for example, online tools, it makes such a big difference in the team. Also to make sure that they are responding to what you are trying to do, because let's be honest, the biggest problem with not being with, not with being online is communication.

We're, we're using a communication tool to see each other, but if we don't know how to communicate and express ourselves, person to person can, can differ how, how they react to that. 

Mehmet: Absolutely. Absolutely. And you know, I know how challenging it is, but uh, you know, if people applies it to, to the way you just mentioned it, I think they would be successful.

And I spoke to a lot of CTOs who, um, you know, they did the. Very well, uh, as you explained it and yeah, like everything was, was okay. It's, it's, I think about the preparation, right? Yeah. And talking about preparation and, because I can see [00:32:00] also Keith, like you talked about security and data availability, so I'm mm-hmm.

I'm thinking in my mind, like you have in your leadership style, uh, something that, um, and I want you to shed some light on this. Like, uh, crises can happen everywhere, right? Yeah. Now and, but. In some domains, and I can imagine whether, you know, in previous company or at your current company, you know mm-hmm.

You, you need to have, you need to have the, the service available all the time. Right. So, um, what, what can you share with maybe other CTOs or to be CTOs and or any leader, um, about what works well on the time of crisis, especially, you know. We talk about data driven and, you know, the data is everything. So any lesson you can share with the audience, Keith?

Keith: Sure. Um, when, when I, when I became CTOI, I was, I didn't know everything [00:33:00] about the company. And even if you're joining a new company and you are a CTO of that company, you don't know. The, the nuts, sand bolts of how things are working. So my, my, my first suggestion is to definitely get to know everyone and make sure that you have a team of direct reports that are.

That, that are in each and every angle of, of, of the company you are working with. And that's what, what, what I've built over here. And the first thing I wanted to make sure is that my direct reports are kind of, um, the best have, have, are linked to each and every area, be it production, being data, being our tech ops, being our dev teams are, are, are linked to each and every area over there.

Um, then though, and I think this is coming also from my experience of being, coming from the data, from the data side, um, is I wanted to instill also this idea of being the first to know. [00:34:00] Mm-hmm. And by saying, first to know. You might say, okay, um, you should already be like that. But for the CTO, normally the CT O is relying on, on a number of teams, which are, uh, in, in, in, in his org chart, uh, in, in, in other areas, which sometimes we don't realize.

That, uh, that maybe someone knows, but like, how is it also being going to be also linked to me knowing or like the, my, my core team knowing which is also important for incident management and all that. So building a team that I use trust is first. Second, which what we did is working on, uh, a number of tools and the number of ways of, of having a core team then and another smaller team of the ones that are always first to know.

And when it's a crisis, it's important that this thing, you also trusted that they can leave things. On their own. And I [00:35:00] think it's all about leadership and, and most important of all, it's also about, um, visibility. And when I say visibility, uh, I, I have to link data observability to it, which is also a term which is also growing a lot.

I mean, in the past we used to just think about anomaly detection, but nowadays it's also about, um, observing what's happening in each and every area of your business. Making sure you have the right, um, tools to alert you when something is changing and something is not. Um, to link it as well. I think it's related also to what's happening right now with ai.

Ai. I. And all the things that are happening. Um, it's, we're, we're, we're, I feel sometimes we're a little bit in the unknown all the time. We're like, it's like we're, um, simulate dimed room for, for, even for CTOs that know, know their, their job very well and they are seasoned CTOs. [00:36:00] We are now in a, in a phase of change where we don't know if what used to work until now, we'll keep on working tomorrow.

So observing what's happening and noticing the normal KPIs and trends that you normally are looking at within your company and seeing, noticing where things are changing. It doesn't mean that it's because there's a problem, but maybe it's because there's another opportunity which we are missing. Um, I don't, I hope that I'm, I'm clear on it.

Mehmet: Absolutely clear, absolutely clear. Keith.

So, Keith, what are like some of the trends, um, and, you know, technologies that are, you know, exciting you the most?

Of course, we talked about ai, but anything within the ai, AI is a very large thing or any other thing maybe outside of ai. 

Keith: What comes with AI, in my opinion, is the personalization. So what I see that is going to take over and what I'm really interested in is not, not a one size fits all [00:37:00] ai, but how we can make it as personalized as possible for teams and also for individuals.

The way I learn and understand something is different from you and from. Each and every person in my team. So I am really looking forward to see how this will happen. And uh, we are already looking at ways that even like a picture sometimes or a chart might be super useful for a person who knows how to chars.

But what about if you put a face and a person next to it, like the clippy one or whatever, and it's going to explain it to you and use analogies and, uh, examples from the things that you like. Some people might understand it more, talking about football or baseball, and some other people might understand it more if it's something about, uh, their favorite music, music band or music type.

And I, I, I see [00:38:00] that as, as the next step now in, in where we're going and scaling up with, with, uh, with, with ai. 

Mehmet: That's great. Keith, as we are almost coming to the end, uh, and this is a traditional question I asked specifically to CTOs who want to advise. You know, people who are like having this ambition to become CTOs, maybe some advice final, uh, you know, wisdom words, I would call them, uh, for someone who's still early, maybe in their career, maybe they are, you know, looking to be CTOs, whether in a startup or maybe in a large company.

You know, just final words of wisdom from you, Keith. 

Keith: What I would say is, uh, so first of all, I don't think I, I, I thought that I was going to become a VE CTO. I always felt that I was going to be mainly stuck to data. But I think what really worked is, and the mindset, the mindset of being curious as we started, also the [00:39:00] discussion today, and not to let that imposter syndrome take over sometimes, because it does take over everyone.

Yeah. Especially when you are in meetings with the board or with your C-suite, and, uh, at that point there, you cannot keep on thinking that you are just the tech person, but you are also getting involved in the running of the business, which is super important that you notice that you accept it and, and you make sure that you're working on yourself.

So other areas that I definitely worked on and uh, and advice is to find a community that you can work, that you can share and talk with. So there are many out there and there are Mies that you can join and all that. But most important is, uh, work on your leadership. If, where, if you do not accept that you are gonna make mistakes, you have a problem [00:40:00] because everyone will make mistakes.

Um, but it's all about making sure that when you make a mistake, you, you get the feedback and you listen to it, and you try to, to, to get better in the next round. Um, that those are the main two and the second, and the, another thing that is coming to mind now is make sure that you are surrounded with the people that, uh.

Have opposite attributes that you have. So for example, if I am a person that I kind of, uh, am very detail oriented, make sure that at least you have one person in the team that, that is not just staring at the detail, but looking at the bigger picture. Mm-hmm. Or vice versa. It's important so that you have a complete, a complete theme.

Mehmet: This is very, uh, inspiring. I would say Keith, because you know, especially the last part, and this is where, you know, founders are, even when we talk in the startup or you [00:41:00] know, they are advised to find someone who can compliment, you know what they do. Yeah. So if you are, let's say you are very good at. Uh, numbers and in sales marketing go bring a co-founder who's good in technical, but it applies on teams as well.

And I've seen it myself many times exactly the way you you describe it, whether in a technical, you know, uh, division or technology division or whether it's like other business division. Um, yeah, absolutely. This makes a lot of sense to me and. Also like what you mentioned about, um, you know, all this, uh, making mistake and accepting that you're gonna make mistake.

Yeah, of course. You know, like, uh, if you have this mindset and the most important, my opinion is like believing that you're gonna make the mistake and you're gonna learn from it and you're gonna do better every time. Absolutely. Exactly. Yeah. So, so this is, was really, really, uh, inspiring Keith before I let you go.

And they do the closing where people can get in touch. 

Keith: On LinkedIn? Uh, the best place is on LinkedIn for [00:42:00] me. Definitely. 

Mehmet: Okay, great. I will make sure that I'll put your profile in the show notes. Okay. Uh, Keith, again, thank you very much. It was a very, uh, you know, very, very inspiring, uh, conversation. I liked, you know, the story you took us from, you know, your beginnings and you know, when.

Becoming a CTO and everything in between the culture, the leadership, uh, that we discussed, and, you know, the trends in technology that we also touch on. So really, I appreciate your time and this is how usually I add my episode. This is for the, you know, audience. Um, if you are. Just discovering this podcast now.

Thank you for passing by. I hope you enjoyed it. If you did, so give me a small favor, subscribe, share it with your friends and colleagues, and if you are one of the people who keeps coming again and again, thank you very much. I'm really grateful for all what you are doing, for all the support for the show.

Without you, I couldn't. By the way, do six top 200 charts at the same time. And by the way, Keith Malta is one of them, [00:43:00] so I'm really happy. One of my guests also, uh, the country are in also, it's like a popular podcast. And this couldn't happen without two factors. First, my guest, including you, Keith, and of course you the audience, thank you very much.

And as I say, always stay tuned for any episode very soon. Thank you. Bye-bye. 

Thank you.