Industrial data: the new battleground for European competitiveness

Data, industry’s new raw material

Industry is no longer being transformed solely through equipment upgrades or the automation of production lines. Data has become a central driver of competitiveness, a new raw material that, when properly captured, structured and analysed, can fundamentally reshape industrial performance.

Every industrial machine now generates vast quantities of information. A single production line can produce tens of terabytes of data each day. Yet without an appropriate architecture and clear governance, the overwhelming majority of this data remains unusable or is simply ignored.

At GYS, the French industrial group specialising in the design and manufacture of welding equipment and battery chargers, every charging or welding cycle, electrical variation and diagnostic signal potentially contains actionable information. The challenge is to capture, structure and interpret it. This is where proprietary software and artificial intelligence come into play, not as miracle solutions, but as tools capable of turning raw signals into operational foresight.

Data, a long-underused industrial asset

For years, data generated by machines was stored without an overarching strategy. It was occasionally used for one-off analysis, but rarely contributed to a long-term industrial vision. In many industrial IoT environments, only a marginal proportion of available data is actually analysed. McKinsey cites the example of an oil platform where only around 1% of sensor data received detailed examination.

At GYS, this observation led to a fundamental reassessment of the group’s industrial approach. For a company manufacturing nearly 2,000 machines each day, with equipment used across 132 countries, the objective is no longer simply to design and produce robust, high-performance machines. It is also to develop proprietary software capable of transforming real-world usage into actionable knowledge.

Behind every data point stands an operator, technician or engineer, together with expertise accumulated over time. Data creates value only when it extends human intelligence rather than claiming to replace it. Developing a precise understanding of how a machine behaves in the field, under industrial, climatic and cultural conditions that may differ considerably from the controlled environment of a test bench, has therefore become a major strategic issue.

“Our machines already generate all the information required to understand their actual condition, their areas of vulnerability and the ways in which they can be improved. The real challenge is no longer to produce data, but to structure and use it intelligently so that we can make better decisions, faster,” explains Bruno Bouygues, CEO of GYS.

Artificial intelligence: an accelerator, not a miracle solution

In this context, artificial intelligence is a powerful accelerator, but certainly not a magic solution. Without clean, harmonised and contextualised data, AI can produce only limited results. When properly integrated, however, it can identify correlations invisible to the human eye, anticipate failures and optimise machine manufacturing and maintenance.

The benefits are tangible. Fewer production failures and the use of predictive maintenance reduce unplanned downtime, extend equipment life and lower overall maintenance costs. This shifts industrial operations from a reactive model to a preventive approach driven by better-understood data.

Architecture, the often-invisible obstacle

Another major challenge lies in systems architecture and data sovereignty. Industry currently suffers from extreme fragmentation across protocols and standards. Every manufacturer, machine and technology generation speaks its own language and uses its own dictionary. This heterogeneity makes the centralisation and exploitation of data considerably more difficult.

Faced with this reality, GYS favours a pragmatic approach: local data processing, secure data flows and complete control over sensitive information stored in proprietary software. This in-house architecture makes it possible to combine operational performance, data security and industrial sovereignty.

Data as a new lever of industrial sovereignty

Control over usage data is gradually becoming a strategic issue. Whoever structures the data gains a detailed understanding of how a machine performs in real conditions, where its limitations lie and where further optimisation is possible.

The location of that data remains a sensitive question. A report by the Swedish National Board of Trade indicates that around 85% of European companies transfer data outside the European Union using standard contractual clauses. Although the figure does not relate exclusively to industry, it illustrates Europe’s continuing dependence on non-European infrastructure.

For GYS, designing the hardware, developing embedded operating systems, building analytical tools and retaining control over data are all part of a long-term strategy. This is essential to guarantee technological compatibility over ten or twenty years in an industrial environment characterised by particularly long investment cycles.

From predictive to prescriptive, a transition still incomplete

Industrial tools were initially descriptive and later became predictive. The transition to prescriptive systems, capable of automatically recommending actions, remains limited. Industry 4.0 maturity indexes show that only a minority of manufacturers have reached these advanced levels.

In the most developed configurations, machines learn not only from their own operating history but also from thousands of other machines working in a wide range of environments. Each cycle enriches the collective knowledge base. This development remains gradual, however, and requires considerable discipline in data management.

A new frontier for industrial competitiveness

When conducted with rigour and ambition, Industry 4.0 projects are no longer experiments but a matter of economic survival. They can dramatically reduce downtime and increase labour productivity by 15% to 30%. The manufacturers most advanced in their use of artificial intelligence already attribute more than 10% of their EBIT to advanced analytics. This is no longer a technological promise, but a measurable competitive advantage.

Data alone cannot solve everything, but without it, European industry condemns itself to falling behind. Failing to use machine data intelligently means accepting a gradual loss of control, margins and sovereignty. Conversely, companies capable of turning their data into operational intelligence create a structural, often irreversible advantage that permanently reshapes the industrial balance of power.

In a world where energy, capital and labour costs are all rising simultaneously, data is becoming one of the last internal levers of competitiveness. Companies that delay activating it will not merely fall behind. They will eventually be forced out of the market.

While other continents are industrialising data at speed, Europe continues to hesitate. That hesitation is no longer neutral. It is widening a competitiveness gap that becomes more difficult to close with every passing quarter.

“Structuring the data generated by machines today means giving ourselves the ability to make decisions and innovate faster tomorrow. It is becoming a genuine dividing line between manufacturers that will control their own future and those that will have it imposed upon them,” concludes Bruno Bouygues, CEO of GYS.