Last Sunday 19th September, David Martin, CEO at Damavis, attended the IB3 radio program: La gran vida, where he told us about his professional career, how Damavis was created and the way the company works.
“It is the dream of any computer engineer who has been working in this sector for a long time and at this point, you say I would like these things to be like this and as that place does not exist, I am going to try to create it for myself”.
Link to the interview: https://ib3.org/lagranvida
Mònica Borràs. We are talking to David, our guest. Somehow, and now he will explain it to us, the company where he would like to work did not exist and he has had to create it, right?
David Martín. Yes, I suppose it is the dream desire of any computer engineer who has been working in this sector for a long time and at this point, you say: I would like these things to be like this and as that place does not exist, I am going to try to create it for myself.
M. In fact, this is very interesting and, definitively, if something does not exsist, and indeed it is also why many of these startups are created, is exactly because of the need that their own creator has found that was missing, rigth?
Benjamí Villoslada. Exactly, if the company where you would like to work does not exist, create it by yourself. And, if the company where you are working does not move forward to digital, then you build a company like that. And this is the story that David told me, the company that is data-based is a bit abandoned, it did not exist, he saw here a good opportunity and that is why he started it. Is or was the world of data very abandoned?
D. Unfortunately it still is but ever less. I had the opportunity to work the last five years of my profession that I worked for third parties for a company in which I was dedicated to lead a R&D project. We were in a highly technological company, with a very important vision of the importance of technology and what it contributed, and that also growed a lot along those years. When I joined the company I think they had an annual turnover of around 200-odd million and when I left, it was close to 1000 million in a year, a big leap in those years, not everything was the fault of technology but it was quite a decisive key element.
The advantage of being in a place like this was that the importance of technology was so big and that gave me this possibility: I worked exclusively on data, I worked with artificial intelligence, on attracting talent, I worked a lot and I saw the possibilities that this gave me, it was enormous what could be done with that amount of data.
But at this point, after five years, what happened was that we had grown so much, we had such a powerful and good team, that the company was not able to absorb what we were doing; that is, they were not able to put into production the solutions we created.
And in this moment, like I am a restless person, the only thing that I was earning was money but it did not bring me any value. This is what you say: “what I am doing is not going anywhere” and with this knowledge I can bring value to other companies that really need it, where I really feel useful and bring them a lot of value, so I decided to change scenery.
When my work team find out that I am leaving, they tell me that they are coming with me. Of course, the scare for me is: “wait, you did not understand: I am going on my own, I am going to see what I can do”, and they tell me that no, that they are there because they are with me, that they like the concept and that they are going with me wherever I go.
M. Now you are really satisfied. If the work was not enough for you, that is satisfaction, is not it?
D. Yes, but the truth is that with the tension of saying… “wait, parents, people with mortgages and so on…”. I mean, one thing was to leave on my own, that I was calm because luckily I have experience, I have a backpack with knowledge and I know that I had a background. But suddenly, when your colleges, your team, tell you “we are jumping with you on the adventure, whatever”, it makes your hair stand on end. You think about it several times with your head, on the pillow, and suddenly you tell them: “look, I can not give you anything in return for the gratitude you have, so let’s become partners and we will start something” and we set up this, what we have today.
B. When you go to damavis.com you see that there are a lot of founders, that these are the things that we focus on when we look at startups, because founders are like the marriage, I mean, they are always there. Employees, if they do not work, you can fire, you can do things; but with a founder, if it does not work, it is very hard to prove that, for example, he does not do the things that are expected, because he can have his own criteria and he should be respected. You are many founders, so you explain it to us now.
D. There are five of us. I had already been working with them for years because I recruited them, I interviewed them, I put them to work with me for a long time. Before setting this up, we had been together for a long time and it was that way of saying: “well, it is not a new adventure, actually it is an adventure we were already doing, only that, instead of doing it for a third party company, we do it for ourselves or to provide to third parties”.
M. You know a lot about this matter but sometimes those of us who are not so involved find it difficult, that is, what data do you work with? data on what?
D. Today, all companies, all of them, have an enormous volume of data. For example, specifically here in the Balearic Islands, there is a high percentage of tourism companies that handle an incredible amount of data, but what is happening? Most companies, on one hand, are not aware of the data they handle; and on the other hand and most important, of the importance of that data. This data is not used, or it is not used correctly, it gets lost, and we are experts in that: one, that they do not get lost, but at a reasonable cost, not an exorbitant cost, which is very important; and two, in what to do with that data to improve my company, either by having better profits, offering a better product to the customer, lowering production costs… and this applies to all segments, all areas, that is, here it does not matter if we are talking about a shoe store, a hotel or a bakery chain, that is, it applies to all segments.
M. We are talking about sales data, interests, target audience, age, gender, where they live, etc… All kinds of data that can be extracted from customers and products.
D. Actually, one of the most important values is that normally companies have different datasets, that is, they have, for example, sales data on one side, in a database, and then they have the data from their analytics, the number of people visiting their website on other place, and then they have the data from the mobile app on the other side but all this data are not connected, they do not have a common sense. Our job is, on the one hand, to generate that datalake what means that we are going to store all that information, but also to put it in context, to understand everything that a customer is doing when he is visiting your website, but then also has the mobile app and finally one day he goes to your store and he buys from you phisically. Understanding all of that, put it in context and give it value.
B. One word came up: datalake. It is the first thing you do, right? A datalake, what he explained before about that they have data of many things. Actually, what they have are puddles, from these puddles they make some channels and they are taken to a lake so that they are together and have the same treatment, right? So in the same way that there are companies that said “now, we would like to have a website”, after they said “now, we would like to have a mobile app”, now there should be companies that say “we want to have a datalake”.
D. Yes, there are many of these. In fact, one of the things that happened to us was that when we started with this, as I was saying, we had been working for years in a leading company, with years of experience with respect to many other companies, and we had in our heads that they were all like that and that now all we were going to do were artificial intelligence models, machine learning… and the reality is that they were not. The reality is that we have come across companies that need to start from scratch, to start saying “okay, let’s build a datalake”. And what is this datalake? Well, it is nothing more than storing that information but in an economical way, because in some companies, it is usually a lot of daily information that grows.
To give you an idea, I have clients that generate three teras of data per day, that is, every day there are three teras of new data, so imagine what it means to ask a question about this data for one year away, “hey, last year, how many clients did I have that x? Imagine what that means. What happens? Storing that is not cheap, if you don’t know how to do it right, and then it’s not easy to ask questions to that volume of data, if you haven’t thought it through before you start storing it. Well, that’s our job, to structure that platform well, to make that data efficient, not simply to spend. It is a question of generating something that gives a greater value than what it has cost.
B. I really like a phrase by Jorge Wagensberg, he says that “changing the answer is evolution, but changing the question is revolution” and I often like to use it to explain how to work with artificial intelligence and how to work with data. Technology is capable of giving great answers and we humans are the revolutionaries, we are the ones who can ask the best questions. The problem is to ask good questions, right David?
D. That’s what I think we bring to the table, spectacular value in comparison. Every company needs data scientists, data engineers, our main technology rols. And, in fact, there are lots and lots of data scientists and data engineers in many places, but why do they call us? Because we know how to do that part, I mean, not only are we very good technicians, we’re highly experienced and we know how to use data the right way to use it, but we’re also able to: one, understand the business. We are not only technical, but we also care about understanding what that customer is doing, what they need and what their problem is. And two: convert that question into something technological that has an answer and provides value.
The use of data is an everyday thing, many companies do it. Now, taking advantage of it or asking the right question to have the answer that really brings value to the customer, that makes your sales improve, your expenses reduce or your customers have better treatment, that is what brings value and that is the reason why we have proyects.
B. This is how you begin to imagine the questions.
D. Directly, what we do is understand the customer, which is the most important thing. Since I’m a computer engenieer, and I’ve been in IT for many years, if there’s one thing I’ve learned is that the main thing is that computer engenieers can be really good, but if they don’t understand what the customer wants, they’re not going to bring anything of value. So, here my main job is first, let’s understand the customer, what their needs are, and from there, we understand what questions we should be asking to answer their needs. That’s the important difference.
M. In fact, talking with Benjamí Villoslada, reflecting on this matter, and his guest David Martín who has created this company that is in damavis.com, it is a pending subject in the world of computer sciencists not knowing how to communicate with the customer or knowing how to transmit all the knowledge they have, which is a lot. David explains it perfectly, the truth, and all this is very well understood. As far as you were talking about these data and those three teras that a single company creates in one day, where is all this data stored?
D. This is another of the exercises that makes us be continuously training, because each company is a world of its own. Each company has its infrastructure, its needs, even companies that are in the same sector and are dedicated to the same thing and are our customers, are then totally different inside. This forces us to be continuously training ourselves in all the technologies of all possible clouds because then we have to advise and recommend them. We are talking about hundreds of thousands of euros for a project, we have to be very sure, give good advice and we have to be continuously training ourselves. Why? Because of what I was saying to your question, in which place? Well, everywhere. We have experts in Google Cloud, in Amazon, in Azure and, obviously, if someone has their own cloud, we know how to put the infrastructure there. We need to be up to date with all the technologies so that we can then advise in the correct way and then implement the solution in the right environment.
B. Yes, because I guess there are things that need a very fast response, that you can’t wait for the next day or you can wait two or three days to get the answer, and I guess you can have a cheaper cloud that goes slower.
D. Yes, for example, in our clients we have processes that are one thing a day, that is to say, something very big is calculated during the early morning that takes several hours and during a whole day you use that report. But there are other cases in which every second you have a thousand requests, a thousand things that are asking you: “hey, what do I do with this customer? And that’s a thousand times a second, so each solution needs a totally different architecture. You have to understand the costs and what it brings.
B. There is a big industry growing, because as more things become software there will be more data on more stories and more and more the word datalake will become more and more familiar.
D. Our main job, and the one we give most importance to at Damavis, is to explain. That is to say, we don’t want to hear about black boxes or the typical “we put the data in here and a recommendation comes out here”. We do not do that, we are craftsmen, we make customized software for companies and we explain what is going on inside. That means we have mathematicians, we have physical engineers, but we need to convey that to someone with a business view. So, our job is to explain why and how to use the data, why those decisions are made and how a machine is going to make a thousand decisions a second to make that happen for the good of the business, in the way that the business needs it. But they have to understand it, otherwise they are not going to believe in us and, precisely, I think that is what customers like from us, because we explain it to them, they understand it and so they support us.
B. And what percentage of the software you use is free software?
D. We always use free software, but we also create free software, that is to say, we already have several free software projects that we have published, that we have put on the Internet and many companies are using.
M. Any company can improve, get to know itself better and improve its resources, so we understand that big companies are the ones that work with you, but could small companies also have access to you?
D. The big companies are the ones that have the budget to invest in large projects and we do projects of an enormous scope, so those are the first ones that appear. But everything we do in all sizes applies perfectly to SMEs, it happens that obviously they do not have the same budget, but we are working precisely on that because we understand that an SME with all the technology, structure, knowledge and artificial intelligence, can get on equal footing with a large one and suddenly both have the same chances of generating business because you do not need to be a company with a billion in turnover to fight against others that are four friends who are doing really well if they know how to understand the data, impose the relevant machine learning models. So really these tools that until now have been only for the big companies because they have been able to afford it, are increasingly reaching more and more places and that is where we bring that value. First, we have all the experience of having put this in large companies, but we have learned along the way everything we have to do so that when a small company arrives, we can do it without errors, the first time, make it go well and that lowers costs a lot.
B. In fact, if you create a company now, do not do it without a datalake.
M. The truth is that the guest that you have brought today, Benjamin, is very interesting. I recommend that all small and large companies visit this website, damavis.com, where you will find the work of David Martín, his colleagues and all this possibility of working with data. Thank you very much, Benjamí Villoslada, and thank you very much to David Martín, it has been a pleasure to listen to him.
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