Interpretability driving industry

What role do data play in an increasingly digitalized and demanding industry?

In a context marked by digital transformation, terms like Industry 4.0, digital twins, artificial intelligence, and machine learning are no longer futuristic concepts, but part of current industrial innovation. However, there’s an element that often goes unnoticed: the interpretability of models. At Datharsis, we believe that the true value of data emerges when it can be understood, explained, and used to make reliable and efficient decisions.

From digitalization to decision: Industry 4.0 and 5.0

Industry 4.0 has led us to a new paradigm of sensors, cyber-physical systems, machine learning, and digital twins.

CPS & DT

They connect the physical world to the digital

Big Data

Big data from diverse sources with process information

AI/ML/DS

Optimization, prediction, classification, and anomaly detection

Now, Industry 5.0 goes a step further: it combines digitalization with sustainability, resilience, and a human-centric approach. Automation is key, yes, but not to replace experts, but to empower industrial analysts and operators with tools that enhance their capabilities.

Advanced, yet understandable models

The evolution of artificial intelligence has been dizzying: from deep neural networks (deep learning), through foundational models, to large language models (LLM) and intelligent agents.

These models are capable of generating text, images, analyses, and even programming… but they are not always interpretable. And in many industrial environments, that is a legal, operational, and safety necessity.

Why does interpretability matter?

To make informed decisions: If a model predicts that a batch doesn’t meet specifications, we need to know why.
To reduce false positives/negatives: Detecting errors in real time saves resources and avoids unnecessary shutdowns.
To comply with regulations: Like the EU Artificial Intelligence Act, which requires transparency in automated decisions in critical sectors.
To audit processes: Especially in industries like food or pharmaceuticals, or the control of critical infrastructures like energy generation, where bias or opacity can have serious consequences.

What does Datharsis do in this context?

At Datharsis, we develop 100% interpretable models for:

Final quality prediction in production lines.
Predictive monitoring in industrial processes.
Integration of multimodal data, such as hyperspectral images and process variables.
Explanatory analysis of critical variables, even those difficult or costly to measure directly.

Additionally, the use of advanced technologies such as augmented reality, virtual reality, and LLM-based agents to create natural interfaces with data (by voice, gestures, or natural language), facilitating their interpretation even without technical knowledge, is a future research direction at Datharsis to improve our data interaction capabilities.

Conclusion: it's not all about automating, it's also about understanding

In a complex industrial environment, predicting isn’t enough. Understanding is essential. Artificial intelligence can automate, but its greatest power lies in expanding our capabilities to analyze, visualize, and decide.

At Datharsis, we don’t just believe in the power of data. We believe in the power of understanding data. Because that’s where true change begins.

If you want to take your industrial processes to the next level with explainable, auditable data aligned with the current challenges of your sector…