Two career paths, one way of understanding data
We come from academic and clinical backgrounds. Today we help scientific and healthcare teams turn their data into decisions with impact, combining methodological rigour with narrative clarity.
Our story
From research and the hospital to data science
We humanise science so that it can be understood, shared and applied with impact. Because behind every data point there are real people.
Our principles
What guides every project and every decision
Communication
“How you tell it matters”
We commit to understanding your context and objectives in depth, and to structuring the results as clear, visual storytelling geared to decision-making and adapted to the audience that needs it.
Judgement
“Questioning to innovate”
We will question you, respectfully. In every project we propose improvements to the initial approach if we spot weak points, risks or potentially sounder solutions. Our priority is not to complete the engagement, but for the result to make scientific and strategic sense.
Commitment
“Social commitment”
We reject practices that compromise scientific ethics or distort results to meet expectations. We support open science and look for responsible ways to contribute to it through our work.
Purpose
“What it's all for”
We only take on engagements where we can be an active part of the team. That includes regular meetings, collaborative iterations and a useful result aligned with your objectives.
Have a data project on your hands?
Tell us about it and we'll work out together how to approach it.
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Our methodology: D²I
We analyse to understand, we understand to act, and we act to create impact.
01
Core
Understand
We get a thorough understanding of the problem and the data before acting: context, objectives and the questions that really matter.
02
Model
Model
We apply the right statistical method to turn that understanding into solid, actionable evidence.
03
Impact
Communicate
We translate the results with narrative clarity so that anyone can understand them and make decisions with them.
Published research
Part of our scientific track record, peer-reviewed and published in international open-access journals.
Identification of high-risk patients for referral through machine learning assisting the decision making to manage minor ailments in community pharmacies
Read more →Amador-Fernández N, et al.
Trends and Seasonality of Information Searches Carried Out through Google on Nutrition and Healthy Diet in Relation to Occupational Health: Infodemiological Study
Read more →Palomo-Llinares R, Sánchez-Tormo J, Wanden-Berghe C, Sanz-Valero J
Perception of risk of exposure in the management of hazardous drugs in home hospitalization and hospital units
Read more →Bernabeu-Martínez MÁ, Sánchez-Tormo J, García-Salom P, Sanz-Valero J, Wanden-Berghe C
Shall we talk?
Tell us what you're working on and we'll give you a straight answer on how we can help.