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Tinamica is a company that stands out for its deep knowledge and experience in Smart Data and Artificial Intelligence. Smart Data has to do with useful data, data that has undergone a process of transformation in terms of completeness, referential integrity, accuracy, characterization, consistency and application of quality and business rules so that they can be interrelated in a coherent way to extract high value from them. “There's a lot of talk about 'big data', but in reality what matters to us is data with quality and density, even if it's 'small'. 'Small data' is much more interesting if it has these characteristics, than not analyzing all the data generated by a sensor. From there, advanced analytics and Artificial Intelligence come into play to extract all the value as long as we have the right functional knowledge to know how to ask or search,” explains Enrique Serrano Montes, CEO of the company.
The role of a CEO is complex and, in addition, he assumes total or global responsibility, an issue that an algorithm will never address. A good CEO must have both the ability to fly low and get muddy, and to take height and decide looking at the stars and the horizon. Sometimes you have to let things happen and wait until the last minute before you pull the handbrake and stop machines. You always have to be ahead of any system, report or strategic consultant, because no one knows your team, business and clients as well as you.
We use it quite well, which does not mean that we have improvement points, or, in other words, improvements that have not been sufficiently used. From the point of view of market analysis, the entry of information about business opportunities is quite automated, not only in Spain, but in those markets on which we focus, such as North and Latin America, Europe and the Middle East. In terms of project management and production activity, I honestly think it is very successful, because our systems have control to the millimeter that ensures that projects are completed on time, within the scope and within the estimated budget and margins. Any risk is detected in time and addressed. The machinery makes us able to launch complex minimum viable products in just 8 weeks. That is also given to you by the experience of having been in the field of AI for 12 years when many have just entered now. In this sense, we beat any large company far in agility and competitiveness. That is why we have never sought integration.
The first thing I always recommend is to start with prediction. To the extent that you are able to predict what you are going to sell by doubling your current view range, you triple your ability to buy at a better price, reduce storage costs, eliminate unknown losses, evolve your hiring of personnel... The impact is clear. On the other hand, you can choose a process within your organization and try to automate it as much as possible by accelerating it. Simply by resolving an order sooner, we can anticipate invoices and collections, you can solve a cash flow problem or, on the contrary, a lower dependence on bank funding. In the relationship with customers, it is wonderful that the attention can be immediate, even 24x7 and with the possibility of solving any type of problem with all the data sources available and, in addition, adapting the language to each type and personality of the client. Competitiveness improvements are brutal. As for jobs, I would talk more about transformation than loss. With sufficient strategic planning and vision in this area, companies should be able to anticipate these changes and prepare people for them. If we think that 80% of processes in companies are automatable, that implies greater speed and, therefore, that there will be other types of activities that will be generated where help will be needed. Nowadays it's impossible to find a good data architect, data engineer or data science expert in deep learning. But, on the other hand, data requires a lot of work to prepare, clean... Algorithms have to be trained and adjusted almost every month and taught how to respond to new questions (prompt engineering). We have the option of depending on third parties or preparing our people to do so and that real intelligence remains within the organization.
Slavery always stems from a lack of knowledge and talent. One can choose to be a slave at some specific time for a purely tactical matter, but always with the intention of getting rid of the chains as soon as possible. Fortunately, we are in a world of AI where open source reigns, papers are available to anyone and the source code of even the most powerful GPT algorithms can be available, even in previous versions. With knowledge of Java, SQL, R or Python as programming languages and a good mathematical base, it is possible to dive into the gut and tune and adapt powerful analytical wonders to your business. Another topic is the investment in time, training and computing that you are willing to make and that others with more resources already have and less time.
A Data Warrior is a Navy SEAL of data, with a highly perimeter and 360º view of its entire life cycle, and with the capacity both to solve problems involving technological data infrastructure, big data, and to apply advanced analytical or deep learning solutions (neural networks), always under a paradigm, preferably open source. In addition, they have a high knowledge of the main market manufacturers such as GCP, AWS or Azure, as well as SAS and other players that we consider important. This means that our teams in complex projects are no more than three people and our actions are very fast and effective. We can answer complex questions in a matter of weeks and develop complex LLMs or foundational models in a matter of a few months.
The project was launched in 2014 when we realized that the talent we need, these Data Warriors, is not in the market and it takes a long time to provide them. There is no time, the state of the art changes every 9 months. In addition, we predict that there will be a delay of 3 or 4 years in meeting a changing demand that means that when they reach the market it is already late, because the demand is different. Today, the most requested profiles have to do with Prompt Engineers, Data Architects, DevOps, Experts in Deep Learning and Cyberintelligence and Quantum Analytics. None of them have an equivalent study program in the formal training market, so at MBIT School we are pioneers in launching cutting-edge programs.
I would say to any type of person who is restless, curious, with a certain analytical capacity and passionate about data and technology. Or in another way, they are not suitable programs for well-off people, with little culture of effort and who hate technology. The good thing is that people do it, and in less than 9 months, before the end of the program, almost 100% have a good, long-lasting and well-paid job.
We have agreements with large companies that not only provide us with students or request tailor-made programs in Big Data, Data Engineer, Data Scientist, People Analytics, Artificial Intelligence, Data Driven, Data Governance, but also search directly among school students for the profiles they need. This secures the job many times even before the end of each program, and without age limit. We have students over 60 years of age who quickly find work. It is clear that the best profiles are always for Tinamica. There is also the modality in which companies commission groups of students to carry out the Master's Final Project with their own data and solving any complex business challenge. They only have to provide the challenge, the datasets and tutor it in conjunction with the MBIT School faculty.
From the Ametic Artificial Intelligence and Big Data Commission, which I chair, we have done a lot of hard work for months to present our position in this regard, both to the Government's Directorate General for Digitalization and Artificial Intelligence and to the Spanish Secretariat of State. The final document is powerful and with a proportionate commitment to mobilize 1.5 billion euros in resources compared to the previous 600. It acts on three axes to strengthen capacities and talent in the development of AI, to facilitate its application in both the public and private sectors and, above all, to promote transparent, ethical and humanistic AI. However, we would have liked a clearer impetus so that SMEs can access high computing capacities in a more democratic, agile and affordable way, as well as greater facilities to adapt to the AI Act, the new European regulation, with a regulatory sandbox that should already be in place. On the other hand, we need more support for an emerging topic such as neurocomputing, where there are already concrete projects.
Tinamica is a company that stands out for its deep knowledge and experience in Smart Data and Artificial Intelligence. Smart Data has to do with useful data, data that has undergone a process of transformation in terms of completeness, referential integrity, accuracy, characterization, consistency and application of quality and business rules so that they can be interrelated in a coherent way to extract high value from them. “There's a lot of talk about 'big data', but in reality what matters to us is data with quality and density, even if it's 'small'. 'Small data' is much more interesting if it has these characteristics, than not analyzing all the data generated by a sensor. From there, advanced analytics and Artificial Intelligence come into play to extract all the value as long as we have the right functional knowledge to know how to ask or search,” explains Enrique Serrano Montes, CEO of the company.
The role of a CEO is complex and, in addition, he assumes total or global responsibility, an issue that an algorithm will never address. A good CEO must have both the ability to fly low and get muddy, and to take height and decide looking at the stars and the horizon. Sometimes you have to let things happen and wait until the last minute before you pull the handbrake and stop machines. You always have to be ahead of any system, report or strategic consultant, because no one knows your team, business and clients as well as you.
We use it quite well, which does not mean that we have improvement points, or, in other words, improvements that have not been sufficiently used. From the point of view of market analysis, the entry of information about business opportunities is quite automated, not only in Spain, but in those markets on which we focus, such as North and Latin America, Europe and the Middle East. In terms of project management and production activity, I honestly think it is very successful, because our systems have control to the millimeter that ensures that projects are completed on time, within the scope and within the estimated budget and margins. Any risk is detected in time and addressed. The machinery makes us able to launch complex minimum viable products in just 8 weeks. That is also given to you by the experience of having been in the field of AI for 12 years when many have just entered now. In this sense, we beat any large company far in agility and competitiveness. That is why we have never sought integration.
The first thing I always recommend is to start with prediction. To the extent that you are able to predict what you are going to sell by doubling your current view range, you triple your ability to buy at a better price, reduce storage costs, eliminate unknown losses, evolve your hiring of personnel... The impact is clear. On the other hand, you can choose a process within your organization and try to automate it as much as possible by accelerating it. Simply by resolving an order sooner, we can anticipate invoices and collections, you can solve a cash flow problem or, on the contrary, a lower dependence on bank funding. In the relationship with customers, it is wonderful that the attention can be immediate, even 24x7 and with the possibility of solving any type of problem with all the data sources available and, in addition, adapting the language to each type and personality of the client. Competitiveness improvements are brutal. As for jobs, I would talk more about transformation than loss. With sufficient strategic planning and vision in this area, companies should be able to anticipate these changes and prepare people for them. If we think that 80% of processes in companies are automatable, that implies greater speed and, therefore, that there will be other types of activities that will be generated where help will be needed. Nowadays it's impossible to find a good data architect, data engineer or data science expert in deep learning. But, on the other hand, data requires a lot of work to prepare, clean... Algorithms have to be trained and adjusted almost every month and taught how to respond to new questions (prompt engineering). We have the option of depending on third parties or preparing our people to do so and that real intelligence remains within the organization.
Slavery always stems from a lack of knowledge and talent. One can choose to be a slave at some specific time for a purely tactical matter, but always with the intention of getting rid of the chains as soon as possible. Fortunately, we are in a world of AI where open source reigns, papers are available to anyone and the source code of even the most powerful GPT algorithms can be available, even in previous versions. With knowledge of Java, SQL, R or Python as programming languages and a good mathematical base, it is possible to dive into the gut and tune and adapt powerful analytical wonders to your business. Another topic is the investment in time, training and computing that you are willing to make and that others with more resources already have and less time.
A Data Warrior is a Navy SEAL of data, with a highly perimeter and 360º view of its entire life cycle, and with the capacity both to solve problems involving technological data infrastructure, big data, and to apply advanced analytical or deep learning solutions (neural networks), always under a paradigm, preferably open source. In addition, they have a high knowledge of the main market manufacturers such as GCP, AWS or Azure, as well as SAS and other players that we consider important. This means that our teams in complex projects are no more than three people and our actions are very fast and effective. We can answer complex questions in a matter of weeks and develop complex LLMs or foundational models in a matter of a few months.
The project was launched in 2014 when we realized that the talent we need, these Data Warriors, is not in the market and it takes a long time to provide them. There is no time, the state of the art changes every 9 months. In addition, we predict that there will be a delay of 3 or 4 years in meeting a changing demand that means that when they reach the market it is already late, because the demand is different. Today, the most requested profiles have to do with Prompt Engineers, Data Architects, DevOps, Experts in Deep Learning and Cyberintelligence and Quantum Analytics. None of them have an equivalent study program in the formal training market, so at MBIT School we are pioneers in launching cutting-edge programs.
I would say to any type of person who is restless, curious, with a certain analytical capacity and passionate about data and technology. Or in another way, they are not suitable programs for well-off people, with little culture of effort and who hate technology. The good thing is that people do it, and in less than 9 months, before the end of the program, almost 100% have a good, long-lasting and well-paid job.
We have agreements with large companies that not only provide us with students or request tailor-made programs in Big Data, Data Engineer, Data Scientist, People Analytics, Artificial Intelligence, Data Driven, Data Governance, but also search directly among school students for the profiles they need. This secures the job many times even before the end of each program, and without age limit. We have students over 60 years of age who quickly find work. It is clear that the best profiles are always for Tinamica. There is also the modality in which companies commission groups of students to carry out the Master's Final Project with their own data and solving any complex business challenge. They only have to provide the challenge, the datasets and tutor it in conjunction with the MBIT School faculty.
From the Ametic Artificial Intelligence and Big Data Commission, which I chair, we have done a lot of hard work for months to present our position in this regard, both to the Government's Directorate General for Digitalization and Artificial Intelligence and to the Spanish Secretariat of State. The final document is powerful and with a proportionate commitment to mobilize 1.5 billion euros in resources compared to the previous 600. It acts on three axes to strengthen capacities and talent in the development of AI, to facilitate its application in both the public and private sectors and, above all, to promote transparent, ethical and humanistic AI. However, we would have liked a clearer impetus so that SMEs can access high computing capacities in a more democratic, agile and affordable way, as well as greater facilities to adapt to the AI Act, the new European regulation, with a regulatory sandbox that should already be in place. On the other hand, we need more support for an emerging topic such as neurocomputing, where there are already concrete projects.
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