There is a scene that is repeated in many Spanish companies: a veteran manager tries to explain why his department needs more budget, more resources or a change in strategy.
He argues it with passion, with experience, with all the conviction that comes with years of experience. And at the next table, a professional in his early thirties opens a dashboard, points out three metrics and closes the discussion in two minutes.
Welcome to 2026, where business intuition is still valuable, but The data has the last word.
The interesting thing is not that this is happening (we have been hearing for years that “data is the most important asset”), but who is occupying those chairs where decisions are made. And it turns out that there is a specific professional profile that companies are desperately looking for: people who understand business, who handle advanced technology and who know how to translate between both worlds.
If you are a regular reader of MCPRO, you probably already have an idea what the movie is about. The question is whether you are going to watch or be the protagonists.
When IT departments began to sit on management boards
A decade ago, data was a matter for computer scientists. Databases, backups, servers. Things that worked in a basement and that no one talked about until something broke.
Today, the Chief Data Officer (CDO) of a large company earns more than many CEOs of SMEs. And it’s not because companies have gone crazy with technological fads.
Let’s come down to earth with an example based on a real case: a medium-sized supermarket chain has 150 stores. Each one generates thousands of transactions daily. For years, purchasing managers decided what to put on shelves based on “what has always worked” and the occasional visit to the competition. Result: stockouts on in-demand products, excess inventory on others, and a lot of money left on the table.
A team enters that knows about analytics. They cross sales data with meteorology, local events, social networks, payroll cycles. They build predictive models. They optimize the supply chain. In six months, the company reduces its inventory costs by 23% and increases sales by 12%.
Magic? No. Big Data, Business Analytics and Artificial Intelligence working together.
Or let’s think about banking: how do you think your bank knows how to offer you exactly the product you need just when you need it? Or how do you detect in milliseconds that someone is attempting fraud with your card? It is not intuition, they are algorithms trained with millions of cases.
Have you ever wondered who builds those systems? Who maintains them? Who explains to the management committee what they mean and what decisions to make?
From consuming AI to building intelligent systems: the leap that separates the user from the strategist
here it is where most professionals are being left behind without realizing it.
While thousands of people are learning how to use ChatGPT or Gemini to write emails or summarize documents, there is another group building AI agent architectures that transform entire business operations. We’re not talking about personal productivity. We are talking about systems that work 24/7, that make complex decisions, that process thousands of variables and that generate real competitive advantage.
An AI agent is not a sophisticated chatbot. It is an autonomous system that integrates with your CRMs, ERPs, databases, and external APIs. That orchestrates workflows where multiple specialized agents collaborate with each other. That automates decisions based on business rules combined with machine learning.
The tools are there: n8n for orchestration of complex workflows with integrated AI, Make for advanced automation with enterprise connectors, LangChain and other frameworks for building applications with LLMs. The low-code and no-code ecosystem has matured to the point where you can develop sophisticated agents without being a software engineer, but you need to understand the architecture, the data, the business logic.
Let’s think about real cases: agents that analyze credit risk in real time by consulting dozens of data sources, multi-agent systems that dynamically adjust pricing according to demand and competition, automated pipelines that process legal or financial documentation extracting critical information, complete data science workflows that go from data ingestion to the deployment of models without manual intervention, conversational agents that resolve complex customer queries by accessing fragmented information in multiple systems.
This is not science fiction nor is it five years away. It’s happening now. And at ESIC they know it. All its business teams are training in agent construction with tools like n8n and Make because they understand something fundamental: the value is not in consuming technology, but in designing systems that generate sustainable competitive advantage.
He Master in Big Data, Business Analytics and Artificial Intelligence It prepares you to be the one who architects those solutions, not the one who consumes them. To be the one who understands when it makes sense to build an agent, how to design it, what data it needs, how to integrate it with existing systems, and how to measure its impact on the bottom line.
The problem with learning on your own (and why it’s not enough)
YouTube is full of Python tutorials. Coursera has machine learning courses. On GitHub you find code for almost anything. So why pay for a master’s degree?
The short answer: because knowing how to use a tool is not the same as knowing how to solve problems.
You can learn to do logistic regressions in three afternoons. But that doesn’t teach you when to use them, when not to, what to do when the data is dirty (which is 80% of the time), how to explain the results to someone who doesn’t know what a regression is, or how to design an entire project that generates real value for a company.
Self-learning gives you loose pieces. High-quality training gives you the complete puzzle, the box with the reference image, and someone next to you who has already put it together twenty times and can tell you where you are getting stuck.
Additionally, there is the issue of professional networking. You can be brilliant working alone at home, but the best job opportunities aren’t posted on InfoJobs. They move in conversations, in networks of contacts, in which someone who knows you recommends you for a project. That doesn’t happen at home watching videos on YouTube.
ESIC: the place where they have been doing this right since before the Internet was born
ESIC has existed since 1965. To give you an idea: when it was founded, IBM had just launched System/360 and most Spanish companies did not even have computers. They have trained more than 75,000 professionals who today are in leadership positions.
Why does this matter? Because we are not talking about a school that four people set up two years ago to take advantage of the data trend. ESIC has spent decades understanding what companies need and adapting its programs to that.
And here comes the important thing: ESIC does not design its master’s degrees from an academic office disconnected from the real world. They do this by talking to the companies they hire. With managers who say “I need people who know how to do this and this.” With professionals who are in the trenches and know which skills are critical right now.
The result is that when you leave there, you are not someone with nice theoretical knowledge. You are someone who can join a team and start adding value from the first month.
The master’s degree that opens doors (and shows it with numbers)
He Master in Big Data, Business Analytics and Artificial Intelligence from ESIC It is designed for professionals who already have experience and want to make the leap into roles where data is the center of the strategy.
And here comes something important: you can choose the modality that best suits your situation.
In-person mode: Calls in April 2026 and October 2026. Monday to Thursday from 7:00 p.m. to 10:00 p.m. Designed so that you don’t have to leave your job. You can continue getting paid, keeping your experience active, and also – and this is key – many students apply what they learn directly in their work projects. You start to see return on investment before you even finish.
The in-person format has things that only happen face to face: conversations after class, real teamwork, networking that is not a cold LinkedIn. People who have been through in-person programs know: the network you build is worth as much as the content.
Online mode: call in October 2026. For those who need geographical flexibility or have an agenda that does not allow travel. But we’re not talking about recorded videos that you watch whenever you feel like it. It is high-quality online training, with real-time interaction, collaborative projects and access to the same faculty and the same professional network.
This flexibility of modalities is essential because it allows professionals from all over Spain, or even abroad, to access training without compromising their work or personal situation.
The program covers everything: data architecture, predictive modeling, machine learning, deep learning, visualization, natural language processing, and yes, also building AI agents and advanced automation. But not as a list of buzzwords, but applied to real problems. Cases from real companies, with real (or realistic) data, with all the ambiguity and complexity that this implies.
The faculty is made up of active professionals. People who are doing this now in companies, who know the current tools, who know what problems you are going to encounter.
And now comes the data that really matters: More than 95% of students find a job or a professional upgrade during the first year after finishing.
It is not a marketing number. It is the result of leading companies actively searching among ESIC graduates because they know what they will find. Technology consultancies, analytics departments of large corporations, startups that are scaling and need to structure their analytical capabilities.
Los roles van desde Data Scientist hasta Chief Data Officer, pasando por Business Intelligence Manager, Analytics Consultant, Machine Learning Engineer, AI Solutions Architect.
There is a question you should ask yourself
Okay, data is important. We all already know that. Companies need professionals who know how to work with them. We also know it.
The real question is more personal: where are you going to be in three years?
You can continue in your current position, seeing how those digital transformation projects are led by others. You can learn loose things on your own, accumulating knowledge without structure. Or you can make an active decision to position yourself where the opportunities are.
Because this is about real opportunities. That when there is a strategic project that can change the course of a company, your name is among the candidates. That when they look for someone to set up an analytics team, they think of you. That when you have to explain to the CEO what all this means and what to do with it, you are in that room.
Serious training, with a structured program, with teachers who know what they are talking about, with real cases and with a real professional network, is what makes the difference between being qualified and being prepared.
If you want to better understand how Master in Big Data, Business Analytics and Artificial Intelligence from ESIC can change your career pathAll information about the program, the admission process and upcoming calls is on its official website.
It’s worth talking to the academic team, telling them where you are professionally and seeing if this is the step you need to take now. It’s not for everyone, but if it is for you, it’s one of those decisions that you will be grateful for for the next twenty years of your career.
