HERNÁN FALBO ÁLVAREZ

I turn complexity into clarity, decisions and product.

I work at the intersection of data, business, artificial intelligence and product: understanding complex problems, giving them structure and turning them into tools that help people make decisions, investigate, or work better.

I am not interested in adding technology for its own sake. I care about information having context, decisions being traceable, and what is built fitting the reality of the people who use it.

02 · BUENOS AIRES → MADRID

Context changes the way we look at a problem.

I arrived in Spain in 2002, after Argentina’s economic crisis. I started by working and adapting to a new context; specializing in data would come later, driven by a need that was far more personal than professional.

ORIGIN

Buenos Aires

I left Buenos Aires in the aftermath of Argentina’s economic crisis. That abrupt change forced me to read the environment, adapt and start again.

PROFESSIONAL BASE · 2002 →

Madrid

I arrived in 2002 and started working. Over time, a personal need would turn learning and understanding into something much more concrete, and data would begin to take on a different role in my journey.

WHAT STAYED WITH MEI learned that context matters as much as the data: the same reality can mean very different things depending on who is experiencing it.

03 · WAY OF WORKING

I don’t start with the tool. I start with the problem.

Before choosing a technology, I try to understand what is happening, what information exists, who needs to use it, and what decision the system should make easier.

01Understand

What problem needs solving and for whom.

→
02Structure

What information matters and how it is organized.

→
03Connect

Which pieces were disconnected.

→
04Build

What really needs to work.

→
05Explain

How it is interpreted, maintained and questioned.

PRINCIPLEFor me, explaining is part of building.

A system that nobody can interpret, maintain or question is not finished yet.

04 · LAYERS THAT ACCUMULATED OVER TIME

They weren’t restarts. They were new questions.

My career did not start in data. It started with working, understanding business and learning to operate in different contexts. Going deeper into data came later, when organizing complex information stopped being only a useful skill and also became a personal need for understanding.

01
BUSINESS

Impact and context

Constraints, priorities and the why behind a decision.

02
DATA & BI

Measurement and structure

Modelling, visualization and a shared view of business evolution.

03
DATA SCIENCE

Patterns and exploration

New ways to analyze relationships, signals and automation.

04
AI & KNOWLEDGE SYSTEMS

Retrieval and traceability

Queryable knowledge, context, limits and governance.

05
PRODUCT

Making it work for someone

Connecting all the layers around a real user need.

ACROSS ALL AREAS

05 · PROFESSIONAL EXPERIENCE

Where I applied those layers and what each environment added.

Experience serves here as credential and context, not as a second timeline. The organizations show where the work was applied; they do not repeat the conceptual evolution above.

01
CURRENT

Santalucía Seguros

Senior Data & Business Intelligence Analyst

An environment where analysis, business, information models and BI have to translate into understandable, usable decisions.

02
EXPERIENCE

OnMobile Global Spain

Senior Business Analyst & BI Specialist

A stage that consolidated the connection between operational needs, forecasting, analysis and information solutions without separating the business problem from its implementation.

03
BUSINESS FOUNDATION

Staples Business Solutions

Pricing & Margin Manager · 2006–2019

This is where I consolidated pricing, margins, profitability, KPIs and reporting, together with a principle I still use today: understand the business first, then use data to explain, measure and improve it.

04
VOLUNTEER PROJECT

WHO

Data project during COVID-19

A one-off collaboration that brought analysis into a different context and reinforced the value of working with clear, traceable and useful information.

View full CV →

06 · EDUCATION

Formal learning, put to the test in real projects.

Specializing in data did not start with a degree. Formal education came later to bring method, tools and technical language to a search that had already begun.

2018
Executive Master · Big Data & AnalyticsMSMK
2023
Master · Data Science & Big DataMIOTI

07 · WHEN DATA STOPPED BEING ONLY WORK

When my son was born, learning stopped being only a professional decision.

When my son was born, I found myself facing a complexity that, as a father, I needed to try to understand. I wanted to understand better what was happening, why, and what he might need; not to take the place of a doctor, but to be able to ask better questions, organize the information and support him with better-informed judgment. That need was what pushed me to go much deeper into data, and it ultimately changed my professional path as well.

EchoFatherDiscover EchoFather →

08 · WHAT I BUILD TODAY

Three territories. One way of approaching complexity.

Three territories summarize where I focus my work today. They combine differently depending on the problem, the context and the decision the system needs to support.

01

Business & Data

Models, KPIs, visualization and information systems for understanding what is happening and supporting decisions.

BI · business evolution · analysis
02

Knowledge & AI

Complex document-based information, semantic search, evidence retrieval, privacy and traceable knowledge systems.

Document AI · research · knowledge
03

Health & Product

Real workflows, information architecture, functional prototypes and tools designed around how people work.

HealthTech · UX · product
View my work →

09 · PRINCIPLES

Ideas that should remain valid even when the tool changes.

These are not slogans detached from the projects. They are criteria that should be recognizable in how the work is built.

01Complexity is not removed by hiding it.

It is organized.

02A tool is only useful if it fits the real workflow.
03AI needs context, limits and traceability.
04Data needs a question before a dashboard.
05Explaining well is part of building well.

10 · CLOSING

Projects change. My way of approaching them does not.

Understand the problem. Give structure to the information. Connect what was separate. Build something useful. And be able to explain it.