Discover our insights on effectively connecting data between the workshop and the data center for industrial companies.
Industry is evolving at the pace of technological revolutions. After mechanization, electrification, and automation, the fourth industrial revolution is redefining production by integrating cyber-physical systems and the Internet of Things (IoT). Today, connecting data from factories has become a major strategic challenge. This interconnection enables advanced real-time data utilization, optimizing production processes, improving predictive maintenance, and facilitating decision-making through artificial intelligence. But what are the challenges and opportunities of such a transformation? A closer look at a key driver of Industry 4.0.
Context and challenges
70%. That’s the average proportion of collected data that is never used within companies. And the situation is even more critical in industrial firms that have yet to complete their digital transformation.
In the era of “Industry 4.0,” where every machine, sensor, and production line continuously generates digital information, it is only natural to ask: how can this data be effectively leveraged to create value? This is the digital treasure that no industrial player can afford to ignore.
The upstream flow of industrial data to data centers
In other words, the complete journey that takes raw information from a factory sensor or machine to the data center — is undoubtedly a key concern if you're an IT leader in the industrial sector.
In the past, this information was limited to on-site operators, used for local and/or occasional monitoring. Today, with the rise of IoT and embedded, connected systems, it is now possible to more effectively collect, route, and process this data to identify, from the outset, insights related to quality, early signs of machine failure, or any other information that impacts production and supports business operations.
The journey of industrial data
According to iiot-world.com, an estimated 75 billion connected devices will be in use by 2025 — nearly half of them in the manufacturing industry. This figure not only highlights the vast potential but also underscores the urgency for industrial companies to take full control of their entire data ecosystem, now including low-level data. We're talking about more efficient production, lower operational costs, and even new business opportunities enabled by advanced AI-driven analytics.
Even better, by managing real-time data from the field, your company will be able to adjust production orders and improve manufacturing quality. Not to mention the potential to optimize energy consumption. Management and quality departments will gain access to additional insights, going beyond the occasional reports they typically rely on.
These figures highlight not only the potential of industrial IoT but also the challenge of managing this data effectively. Companies that succeed in harnessing this wealth of information will gain a competitive edge.
The growing number of sensors and connected machines in industrial environments is creating an increasingly complex data ecosystem. This complexity calls for structured approaches to efficiently collect, transport, store, and analyze the data.
Available technological solutions
The good news is that proven solutions now exist to leverage the vast amount of physical data available at the machine level in factories. From smart sensors capable of transmitting real-time performance data, to centralized cloud-based data lakes, and even embedded AI systems that detect patterns invisible to the human eye — a range of tools and methods is emerging to support industrial players. IoT platforms, Edge Computing, custom-built data pipelines, and more…
IoT plateforms
These solutions make it possible to manage the entire lifecycle of connected devices, from deployment to the collection and analysis of the data they generate. They typically offer user-friendly interfaces for visualizing information and configuring alerts. Some also provide interfaces to route data directly to the cloud data center.
Edge computing
This approach involves processing data as close to its source as possible, thereby reducing latency and transmission costs. Technological solutions exist to support this model. It is particularly well-suited to industrial environments with constraints or when certain decisions must be made in real time.
Data pipelines
These software infrastructures—often custom-built—automate the flow of data from collection to utilization, passing through essential stages such as cleaning, transformation, and enrichment to make the data usable.
The promise is to turn raw collection into strategic value.
The key question is this: how can the workshop be effectively connected to data centers?
- How can data be structured and governed to extract its full value?
- What technologies or frameworks should be adopted to move from raw collection to strategic usage integrated into business processes?
- How can embedded AI contribute to predictive analysis and real-time optimization?
- How can data security be ensured at every stage of its journey?
Looking ahead...
En attendant la publication de notre prochain livre blanc sur le sujet, vous pouvez dès à présent nos experts. While awaiting the release of our upcoming white paper on the topic, feel free to reach out to our experts today.
