When a digitalisation project stalls, it is rarely because the necessary technology does not exist. Sensors, networks, databases, analytical methods – all of these are available, proven and usually economically viable. The real challenge lies in combining them: What business question are we actually trying to answer? What data do we need? How can it be collected reliably from the plant? And who will use the result – and for what purpose? Ultimately, it is about combining existing solutions in a way that provides a clear and understandable answer to a real operational question.
That is why, for us, digitalisation is first and foremost an organisational project with a technical implementation – not the other way around.
The customer only wanted to know the cycle rate
One example comes from heavy industry. The customer’s question was remarkably simple: What is the cycle rate of our stamping press? For any further optimisation, this information is an important starting point. The machine had been running for years, but its cycle rate had never been measured systematically. Interfering with the machine control system was not an option. Our proposal: a non-invasive measurement using acceleration sensors mounted on the machine frame. The result is a very simple output signal – one pulse per stroke – answering the equally simple initial question.
Things became more interesting when we analysed the data. With the right process understanding, the same signal can reveal considerably more than the cycle rate alone. The shape of the impact changes as the tool wears. Irregular intervals can indicate process anomalies, such as the machine advancing too early during the stamping operation. What starts as a simple cycle measurement can therefore develop into a much more comprehensive picture of the machine’s condition.
The key point is not the sensor technology itself. It is recognising what the signal tells us about the real process – and expanding an initially narrow question where the available data can provide additional value. This is exactly where consulting can begin: before selecting a specific solution, we can jointly clarify which question actually needs to be answered and what additional information can be extracted from existing or easily accessible data. This helps avoid investing too quickly in technology that ultimately misses the real need.
From steel fabrication to services
A second example operates on a very different level. A machine manufacturer recognised that its business model would have to change: steel fabrication can be done more cheaply elsewhere. To remain competitive, the company needs to offer additional services – for example, providing equipment and charging based on actual use instead of selling it as a one-off product.
The strategic direction was clear. The technical path towards it was not.
That was our task: translating a business idea into an implementable technical concept. What data does a usage-based billing model require? How can that data be extracted from the machine reliably and in a tamper-resistant way? Which platform, connectivity and sensor technology are needed – and which components are better purchased than developed in-house?
The technical chain extends from the platform down to the individual sensor. We supported the selection of suitable components and accompanied the implementation. But the actual consulting work started earlier: translating a business idea into concrete technical requirements.
This type of consulting can vary in scope: from structuring requirements and independently assessing possible technologies to selecting suitable components and designing a viable system architecture. The important part is turning a business idea into a robust decision framework for the next steps.
Why good concepts end up on the shelf
The most common problem we encounter is not a poor concept. It is a good concept that never becomes part of day-to-day operations. Knowledge transfer from research often stops at the factory gate – technically sound and well thought through, but not sufficiently connected to the real processes, responsibilities and constraints within the company.
At the latest during implementation, three partly very different worlds come together: automation technology, IT and the mathematics behind data analysis. Each has its own terminology, priorities and understanding of what matters. Availability means something different in the control room than it does in the data centre. A measurement value means something different to a maintenance engineer than to a data analyst.
Our task is to put this puzzle together: to develop a concept that works across all three worlds – and to translate between them. This also means translating in the other direction: for the person commissioning the work, who needs to make decisions without being an expert in all three technical disciplines.
Consulting – now also as a standalone service
Consulting has always been part of our work with customers. Until now, however, it was usually embedded in an implementation project rather than offered as a service in its own right.
That has changed. Since the end of 2025, we have also offered consulting as a standalone service: eSENSEial Data Science is a registered management consultancy and is additionally listed for subsidised consulting services through the consulting programme of the Lower Austrian Chamber of Commerce (WKNÖ).
Whether you need a technical concept, an independent assessment before an investment decision, or a sparring partner for a specific digitalisation or integration question – this is now possible without a subsequent implementation contract. Eligible consulting services may also qualify for funding through the WKNÖ programme.
Looking beyond the immediate question
Both examples follow the same pattern. They begin with a concrete, often narrowly defined question. The real value is created when technical possibilities, process knowledge and operational requirements are brought together – and when we look beyond the boundaries of that initial question without losing focus.
Technology itself is rarely the bottleneck. The bottleneck is its integration into the organisation: Who makes the decisions? Who provides the data? Which systems need to work together? And who will ultimately use the result – and for what purpose?
Digitalisation is not a product you simply introduce. It is a chain of decisions – and none of them is made by IT alone.