Telemetry in industry goes far beyond simply collecting data. In its strict sense, it involves remote measurement and data transmission; however, in the context of Industry 4.0 projects, it must be complemented by data acquisition, integration, contextualization, and data quality management to effectively support operational decision-making.
Data itself only becomes valuable when it is properly processed and placed within the context of the underlying processes. In industrial environments, this entire journey begins with data acquisition—the systematic collection of information from sensors, meters, PLC controllers, and other IoT devices. It is at this stage that the foundation is built for further analysis, automation, and process optimization.
The principle is simple: to optimize, you first need to measure. In practice, however, this means much more than just connecting devices and starting to read data. In most manufacturing plants, data is already being collected, but it remains fragmented, locked within separate systems, and difficult to use in a broader context. Without a consistent telemetry layer, organizations operate on a partial view of reality, which limits their ability to make informed operational and business decisions.
In fact, the root of these limitations is not the lack of data itself, but the way it is collected, transmitted, and made available within the industrial environment.
Challenges of industrial telemetry
Most telemetry and data acquisition projects delivered by Fabrity take place in existing production facilities—so-called brownfield environments. In such factories, measurement infrastructure is typically already in place: machines are equipped with PLC controllers, SCADA systems are operational, and energy meters, sensors, and other measurement devices are installed across production lines.
The challenge is that data from these sources rarely makes its way into a single, centralized system. Instead, readings remain local, accessible only through specific devices or applications, while proprietary communication protocols used by equipment manufacturers significantly hinder integration.
In many plants, manual data collection or exporting data to spreadsheets is still common practice. This limits monitoring to selected points in time and makes it impossible to analyze how processes actually unfold. As a result, even basic information about machine performance or resource consumption is neither continuously available nor readily usable for analysis.
Another challenge is the lack of infrastructure designed with data flow in mind. In many plants, there is no unified communication network or standardized method for exchanging information between systems. Data generated by sensors—both analog and digital—must be aggregated and processed before it becomes useful. In addition, in large industrial facilities, expanding wired infrastructure can be difficult or cost-prohibitive, further limiting the ability to extend measurement coverage.
A good example of these challenges is how energy measurements are handled in many plants. Measurement points typically reflect the energy distribution structure—they are located at switchboards, power sections, or circuits—rather than the actual flow of the technological process.
In practice, this means that energy consumption data is not directly linked to specific operations, machines, or production stages. As a result, answering a seemingly simple question—such as how much energy a given process consumes at each stage—can be quite difficult and often requires additional calculations or manual interpretation of the data.
This is why data acquisition should not be approached as a standalone technical implementation, but rather as an integral part of the plant’s broader information architecture.
Read more on Industrial IoT:
OEE: A basic KPI for optimizing manufacturing processes
Industrial Internet of Things (IoT) trends for 2026
Industrial data acquisition for energy management—how we do it at Fabrity
Internet of Things (IoT) security: A challenge for 2026
The LoRaWAN technology for industrial settings: Four practical use cases for 2026
How we approach industrial telemetry in practice
At Fabrity, we take a pragmatic approach to data acquisition, always starting with what is already in place at the facility. Our goal is not to rebuild the existing infrastructure, but to organize and leverage it to create a coherent telemetry layer.
That’s why the first step is always an on-site assessment and a review of available measurements. We identify where data is already accessible and where gaps exist. Based on this, we design an architecture that enables data collection in the least invasive way possible—without interfering with the OT network or control logic.
Our first priority is always to access data directly from existing systems such as PLCs or SCADA. This is the most natural approach, allowing us to leverage already available information without the need for additional hardware. In practice, however, such access is not always possible, so we complement it with other methods. Where needed, we use remote I/O modules to capture signals without interfering with control systems, as well as IoT gateways that aggregate data from multiple sources and enable local processing through edge computing.
An equally important aspect of this approach is how data is transmitted. Depending on the use case, we rely on both simple communication protocols, such as Modbus RTU/TCP, and more advanced standards that support interoperability and data modeling, including OPC UA, IO-Link, and PROFINET. In machine environments, it is also worth considering semantic standards like MTConnect, which help reduce the challenges associated with proprietary data formats.
For large-scale facilities or challenging installation conditions, we turn to wireless solutions such as LoRaWAN. This allows us to extend measurement coverage even in areas where running cables would be impractical, cost-prohibitive, or disruptive to ongoing production.
A particularly illustrative example of this approach is energy consumption in production. Meter readings alone provide limited value if they are not linked to how the process actually operates.
That’s why, alongside the data acquisition layer, we map the production structure—lines, operations, and stages—and then assign measurement points to them. Where direct physical measurement is not possible, we also use virtual meters to reconstruct consumption based on existing data. This makes it possible to attribute energy usage to specific processes, rather than just to power supply circuits.
Finally, the data is fed into higher-level systems—either on-premises or in the cloud—where it can be further analyzed and used in day-to-day operations. An important aspect here is the approach to real-time data. Contrary to common assumptions, millisecond-level readings are not always necessary. In many energy and operational monitoring use cases, a one-second interval is sufficient. However, the required data collection frequency should always be driven by the specific use case. Energy reporting has different requirements than vibration monitoring, and both differ from control systems operating at sub-second timescales.
The telemetry and data acquisition layer is not an end in itself. Its true value emerges only when the collected and structured data begins to feed systems that support production planning, maintenance, and energy management.
This is the point at which data transforms into a practical tool for supporting operational and business decisions.
Nexen Suite as the next step after industrial telemetry
A well-structured data acquisition process provides a direct foundation for implementing Nexen Suite—an intelligent platform for managing the factory of the future. Nexen is an Industry 4.0 solution that brings together IIoT, AI/ML, and computer vision within a single ecosystem, delivering full visibility into production processes and enabling real-time, data-driven decision-making. As a result, data collected at the telemetry stage evolves beyond raw technical readings and begins to actively support the optimization of production, quality, and safety.
At the core of the platform is the Nexen MPS module, responsible for production planning and management. By integrating data from ERP systems with the real-time status of machines, it enables precise scheduling, workload analysis, and continuous tracking of order execution. Combined with OEE analysis and machine performance history, it provides full control over production efficiency and allows for rapid response to deviations as well as the elimination of bottlenecks.
This approach is further enhanced by modules supporting maintenance, energy management, and data analytics. Nexen CMMS enables a shift toward predictive maintenance by analyzing sensor data and generating alerts before failures occur.
Nexen EMS provides full visibility into energy and utility consumption in relation to the production process and has been designed in line with ISO 50001 requirements. It enables monitoring of the EnPI indicator, which is essential for assessing energy performance and tracking improvements over time.
The Vision and Analytics modules extend the platform’s capabilities with image analysis and advanced data analytics, making it possible to identify trends, detect anomalies, and uncover optimization opportunities across the entire plant.
From telemetry data to action
Telemetry and data acquisition are only the beginning. Their real value emerges when data starts to support concrete decisions—operational, technical, and business. This is when a factory gains not only visibility into its processes, but also the ability to actively shape and optimize them.
If you’re looking to improve how data supports energy management, maintenance, or production planning, it’s worth starting with a structured approach to telemetry and data acquisition.
We can help assess your current setup, identify gaps, and define the right next steps. Drop us a line at sales@fabrity.pl to discuss your needs.
