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Industrial Data Acquisition – Knowing which solutions really work for –your data sources  sensors actuators controls

Find out which technologies leading companies from our network are already using successfully. We show you real solutions and practical examples that are being used by other companies to optimize their industrial data acquisition – proven, reliable and measurable.

Strong partners, strong solutions

Our experts for data acquisition

Our platform brings together leading technology partners and solution providers to support you in the implementation of your data acquisition projects. Benefit from field-proven solutions and experienced partners from the IIoT community

ALD Vacuum Technologies GmbH Logo

ALD Vacuum Technologies GmbH

Data AcquisitionPredictive analytics

ALD Vacuum Technologies designs, engineers and produces advanced vacuum metallurgy, heat treatment and...

autosen Logo

autosen

Data AcquisitionPredictive analytics

autosen offers sensors, automation technology and realizes potentials of the IIoT for customers. With...

Balluff GmbH Logo

Balluff GmbH

Data AcquisitionData visualization

Balluff is a sensor and automation specialist with a broad portfolio of IIoT-capable hardware,...

Endress+Hauser GmbH+Co. KG Logo

Endress+Hauser GmbH+Co. KG

Data AcquisitionData visualization

Endress+Hauser is a global leader in measurement and automation technology for process and laboratory...

Harting Technology Group Logo

Harting Technology Group

Data Acquisition

HARTING Technology Group has become a global leader supplying connectivity solutions for the three main...

ifm-Unternehmensgruppe Logo

ifm-Unternehmensgruppe

Predictive analyticsData visualization

ifm electronic gmbh with headquarters in Essen develops, produces and distributes sensors, controls,...

igus GmbH Logo

igus GmbH

Data visualizationPredictive analytics

The world is in constant motion. And where movement happens, that's where we come in: improve what...

IOX GmbH Logo

IOX GmbH

Data AcquisitionData transmission

smart.click allows a new way to integrate IoT solutions into business processes. Our NB-IoT & LTE-M...

Perinet GmbH Logo

Perinet GmbH

Data AcquisitionData preprocessing (Edge)

We develop forward-looking electronics to make sensors and actuators network-compatible. The new...

Red Lion Logo

Red Lion

Data preprocessing (Edge)Data visualization

Red Lion is focused on being THE Industrial Data Company™️. We empower industrial organizations around...

Schildknecht AG Logo

Schildknecht AG

Data AcquisitionData preprocessing (Edge)

Schildknecht AG has been developing innovative data transmission solutions for over 40 years and is an...

SICK AG Logo

SICK AG

Data AcquisitionData visualization

As a technology and market leader in the field of sensor intelligence and application solutions, SICK...

Industrial data transmission

The nervous system of digital transformation in the IIoT

Industrial data acquisition forms the basis of every successful digitalization initiative. Without precisely collected and structured data, even the most advanced analysis systems remain ineffective. Find out how modern data acquisition solutions can pave the way for data-driven decision-making in your organization.

What exactly is industrial data acquisition?

For manufacturers, data acquisition in the context of IoT means collecting specific measured values directly from machines, sensors and systems, bundling them and processing them in such a way that they can actually be used in daily operations. It’s about finally seeing what’s really happening in your production – from the simple “Where is the workpiece right now?” to the complex analysis of machine conditions.

Modern IoT data acquisition collects relevant operating data such as temperature, pressure, vibrations or production figures directly at the source. This data makes your processes traceable, helps to detect faults at an early stage and forms the basis for all further digitalization steps – from increasing efficiency to predictive maintenance.

Whereas previously you had to work with separate systems for operating data (PDA) and machine data (MDA), modern IoT data collection combines both into a practical overall system that gives you a complete overview.

In an industrial IoT context, it records relevant operating parameters such as temperature, pressure, vibrations or key production figures directly at the source – via industrial sensors, edge devices or machine control systems (PLC). This data forms the basis for all further digitalization measures, from process optimization to predictive maintenance.

While traditional production data acquisition (PDA) mainly covers organizational aspects and machine data acquisition (MDA) purely technical aspects, modern industrial data acquisition combines both worlds into a holistic system that enables both horizontal networking between machines and vertical integration from field level to company level.

Various field-proven technologies are used in day-to-day production:

  • Sensors that truly work: Robust industrial sensors measure physical parameters directly at the machine — from simple temperature probes to complex vibration analysis.
  • Data collectors that integrate seamlessly: Edge gateways and data acquisition modules retrieve data from your existing machines — even if they vary in age and manufacturer.
  • Communication that’s seamless: Standards like OPC UA, MQTT, or IO-Link ensure data is reliably transferred from the machine to your systems, without you having to worry about the technical details.
  • Local preprocessing that makes sense: Edge computing filters, compresses, and analyzes data right on-site — so you receive only the information you actually need, without data overload.
  • Software that makes data usable: Visualization and analysis tools process your data in a way that makes it clear and actionable — both for your operations and for your customers.

The biggest challenge is often not the technology itself, but finding a system that fits your specific requirements and can be integrated into your existing production environment.

Modern industrial data acquisition is based on various technologies that are combined depending on the application:

  • Sensors and measurement technology: Industrial sensors record physical variables (temperature, pressure, position) and convert them into electrical signals. Selecting the right sensor technology is crucial for data quality and precision.
  • Data acquisition hardware: Edge devices, PLCs and special data acquisition modules collect the sensor data, carry out initial pre-processing and forward it to higher-level systems.
  • Communication protocols: Standards such as OPC UA, MQTT or IO-Link enable uniform data exchange between machines, controllers and IT systems, regardless of the manufacturer.
  • Edge computing: Local pre-processing of data directly on the machine reduces data volumes, lowers latency times and relieves central systems for time-critical applications.
  • Data management software: Special software collects, structures and stores the recorded data and makes it available for further analysis and visualization tools.

The biggest challenge often lies in integrating different technologies into a consistent system – especially in heterogeneous production environments with machines of different generations and from various manufacturers.

Benefits of dat aacquisition

Use your data potential
- for more efficiency & safety

Industrial data acquisition is essential for digital transformation – but only if it is implemented correctly. Find out what critical benefits companies can gain from our network with accurate, field-proven data acquisition solutions and what risks you can avoid by eliminating common mistakes.

Precise process control through real-time data

Industrial data acquisition makes it possible to continuously monitor machine parameters such as temperature, pressure, vibrations or throughput. This allows dynamic adjustment of production processes in real time in order to avoid production bottlenecks and sustainably increase overall equipment effectiveness (OEE).

Minimization of machine downtimes

Thanks to automated data acquisition, anomalies can be detected at an early stage. Algorithms for pattern recognition continuously analyze machine data and identify signs of wear before failures occur. This reduces maintenance costs and prevents unplanned production downtimes.

Standardized data communication

Standardized communication protocols such as OPC UA, MQTT or IO-Link enable the smooth connection of heterogeneous machine parks. This enables companies to merge machine data from different sources centrally and integrate it into higher-level IT systems such as MES or ERP.

Data quality for reliable analyses

The combination of data acquisition and edge computing enables data to be pre-processed at the machine. In this way, irrelevant information is filtered and only high-quality, aggregated data is forwarded to central systems. This reduces latency times and increases the efficiency of data analysis.

Efficient automation with retrofit solutions

Retrofit technologies can also be used to integrate older machines that previously had no digital interfaces into IIoT ecosystems. Sensors and adapters based on protocols like IO-Link or BACnet capture relevant machine parameters and make them usable for digital applications.

Visualize processes and make data-driven decisions

Collected machine data is processed using visualization tools like Grafana or Power BI. Companies receive interactive dashboards that enable them to analyze process indicators such as OEE, MTBF (Mean Time Between Failures), or energy consumption in detail — and make informed, data-driven decisions.

Digitalize successfully – through proven IIoT projects and collaboration

Gain valuable insights into real-world IIoT implementations, learn from the best practices of leading companies, and connect with the right contacts in the community – for well-informed decisions and future-proof strategies.

Discover Best Practices

Use our platform to discover hands-on best practices and industrial use cases. See how other companies are successfully implementing concrete IIoT projects.

Utilize expert knowledge

Talk to us – we listen, understand your challenges, and connect you with the right experts and contacts from our community.

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Practice-oriented. Tested & Up to date.

Examples from our network - How our partners successfully
implement data acquisition

Experience how leading companies are using industrial data acquisition technologies as an essential part of their overall IIoT strategy through practical and proven use cases. Be inspired by concrete applications that show how products and standards can be seamlessly integrated in practice and create measurable added value.

Further building blocks for your successful IIoT project - explained in a practical way

Many IIoT projects fail because the chosen technologies, products or solutions are expensive, inflexible or not scalable in later operation. Projects often only develop over time, meaning that requirements are not fully known at the start – or there is a lack of knowledge about existing options on the market. The result: rework, high costs or a failed business case.

On our platform, you will therefore find consolidated knowledge from the community and tried-and-tested best practices from real industry projects. We will show you which technologies and solutions work for specific use cases – and which pitfalls you should avoid in order to implement your project securely, sustainably and economically successfully.

Find out how other companies in our network are building their technology stack – from data acquisition, data transmission, standardization and security to IoT platforms and analysis.

Data Acquisition

Data acquisition forms the solid foundation of your IoT application. Whether machine, operating or sensor data - accurate and reliable data acquisition enables precise analyses and data-based decisions. Modern solutions capture data directly at the machine, standardized and in real time.

Data preprocessing

The efficient preparation and pre-processing of your raw data ensures that it can be used immediately. Whether edge computing or local pre-processing - reduce the amount of data and significantly improve the performance of your IoT systems.

Data standardization

Data standardization creates the basis for efficient, cross-manufacturer communication and consistent use of data throughout the entire life cycle. Whether through protocols such as OPC UA over MQTT for secure transmission or standardized product data and digital twins - your IIoT projects remain flexible, scalable and economical.

IT/OT integration

The convergence of production technology (OT) and information technology (IT) enables you to achieve a transparent data flow without media disruptions. This allows you to eliminate data silos, speed up decision-making and optimize your operational processes in the long term.

IoT platform

IoT platforms form the central nervous system of your digital infrastructure. As PaaS or SaaS solutions - for example in the form of customer portals for manufacturers - they store, visualize and manage IoT data. As a result, you always have a comprehensive overview of your processes and can make well-founded, data-based decisions.

Data Transmission

Reliable data transmission is essential for every IoT process. Choose between wired (e.g. Ethernet) and wireless technologies (e.g. 5G, LoRaWAN) based on your requirements in order to optimally combine stability and flexibility.

Data Security

Protecting sensitive industrial and process data is a top priority. Modern security concepts ensure that your data is transmitted and stored in encrypted form and that your systems always comply with current regulatory requirements.

Device Management

Efficient management of networked IoT devices is a key component of successful digitalization strategies. From commissioning and updates to decommissioning - structured device management reduces operating costs and significantly increases the security of your IoT infrastructure.

Data visualization

Visualized data enables faster and more precise decisions in your company. Modern dashboards and graphical presentations transform complex data streams into clear, real-time displays and create transparency at all levels of the company.

Data Analysis and Evaluation

The systematic analysis of your IoT data uncovers hidden correlations and identifies optimization potential in your processes. From descriptive statistics to complex analysis procedures - gain valuable insights from your operational data for well-founded business decisions.

Data analysis with ML & AI

Data analysis is the basis for data-driven IIoT applications - from process optimization to AI-supported predictions. While traditional evaluations work with fixed rules, AI algorithms independently recognize patterns and anomalies - for predictive maintenance or quality forecasts, for example. Both approaches complement each other and optimize the potential of your IoT data.

Use case apps

Industry-specific IoT applications address concrete challenges with preconfigured functionalities. From production optimization to asset tracking — these specialized solutions offer rapid time-to-value and can be flexibly adapted to individual requirements.

Technologies that our partners use in projects for data acquisition

Choosing the right standards and technologies determines the success of your data acquisition projects. Industry-leading standards form the foundation for reliable communication between your machines, sensors and IT systems. Our partners rely on established technologies that ensure seamless integration:

Network, exchange ideas, benefit.

Implementing IIoT projects together - with field- proven solutions

Our community brings together industry experts who have already implemented successful IIoT projects – openly, practically and on an equal footing. Gain insights into how other companies have solved challenges, share your use cases and discover new ideas and concrete solutions for your business.

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Solution examples
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Active members
Use Cases

Your use case has already been solved — see for yourself!

Every innovation starts with an idea. Discover proven use cases that support your digital transformation — from predictive maintenance to worker safety.

Condition monitoring

Real-time monitoring of machine and sensor data to reduce downtime.

Predictive Maintenance

Data-driven maintenance to detect failures early and cut costs.

Track & Trace

Seamless tracking of assets and material flows in production and logistics.

Digital Documentation

Automated collection and management of production and operational data.

Practical knowledge you can listen to

Our podcast episodes to listen to — hands-on expertise around data acquisition

In our podcast, we bring together users and manufacturers to discuss real-world challenges of data acquisition — from both technical and business-case perspectives. Tune in for practical insights and proven solution approaches.

IoT Use Case Podcast #118 - ICONICS + Mitsubishi Electric Europe
Efficient manufacturing with data acquisition & analysis

Learn how Smart Manufacturing Kaizen Level (SMKL) optimizes production processes through data acquisition and analysis. Practical insights for decision-makers and engineers!

IoT Use Case Podcast #126 - WIKA
From measuring point to added value - WIKA's path to becoming an IIoT solution provider

Philipp Lausberger provides exclusive insights into the transformation, from sensor technology and data transmission to the integration of smart services.

IoT Use Case Podcast #84 - ifm + MAGNA
Automotive: Maintenance and Condition Monitoring

In this episode, Sebastian Schlicht (Magna) and Tilo Haug (ifm) talk about predictive maintenance, vibration analysis and digital transparency on the shopfloor.

Frequently asked questions about data acquisition

Everything you need to know – from first use to technical integration.

Industrial data acquisition refers to the systematic process of converting physical machine and sensor data into digital formats. In contrast to conventional data acquisition, industrial data acquisition is characterized by real-time acquisition, high data rates, standardized communication protocols and close integration with industrial control systems. While conventional systems often function as stand-alone solutions, industrial data collection enables end-to-end connectivity from the sensor to the machine controller to the cloud platform, thus forming the basis for Industry 4.0 applications.

Several standardized communication protocols are established in industrial data acquisition:

  • OPC UA: Manufacturer-independent standard for machine communication with integrated security and semantic data description
  • MQTT: Lightweight publish/subscribe protocol, ideal for IoT applications with limited bandwidth
  • IO-Link: Point-to-point connection for sensors and actuators with bidirectional communication
  • PROFINET/PROFIBUS: Real-time capable fieldbus protocols for industrial automation
  • Modbus TCP: Simple industrial network protocol for control communication
  • EtherNet/IP: Industrial Ethernet protocol for automation technology

The choice of the right protocol depends on specific requirements for real-time capability, data volume, security, and compatibility with existing infrastructure.

For integrating older machines (retrofit), several proven approaches are available:

  1. Retrofitting with external sensors: Installing sensors to acquire operating conditions (temperature, vibration, current consumption, etc.)
  2. PLC connection: If an older control system is available, it can often be extended with interface modules
  3. Signal tapping: Direct acquisition of electrical signals (e.g. 24V signals or analog values)
  4. Optical signal detection: camera-based systems for reading displays or indicator lights
  5. Retrofit kits: Specially developed retrofit solutions for specific machine types

When retrofitting, it is important to carry out a cost-benefit analysis and identify the relevant data for your use cases in order to avoid overinvestment.

The following security aspects must be considered when implementing industrial data acquisition systems:

  • Access controls: Implementation of multi-level authentication systems and role-based access rights
  • Data encryption: Secure transmission via TLS/SSL and encryption of sensitive data at rest
  • Network segmentation: Separation of the OT network (Operational Technology) from the IT network using firewalls and DMZs
  • Patch management: Regular updates of all components to close known security vulnerabilities
  • Security by design: Incorporation of security aspects already in the planning phase
  • Security audits: Regular checks of the systems for security gaps
  • Emergency plans: Preparation for possible security incidents and their resolution

Due to the critical nature of production-related systems, a comprehensive security concept that combines technical and organizational measures is essential.

Edge computing refers to data processing directly at the point of data origin — close to sensors and machines — rather than in a centralized data center or the cloud. In industrial data acquisition, edge computing plays a crucial role, as it:

  • Reduces latency: Enabling critical process decisions to be made in real time
  • Optimizes bandwidth: Only relevant data is transmitted through pre-processing
  • Increases reliability: Systems continue to operate even during network interruptions
  • Improves data privacy: Sensitive data can be processed locally
  • Reduces costs: Lower cloud storage and data transfer costs

Typical edge computing devices in industrial environments range from compact IPC systems to dedicated edge gateways and intelligent controllers that can handle data preprocessing alongside their control functions.

The data to be collected depends heavily on your specific objectives, but the following data categories are relevant in many production environments:

  • Machine data: Operating states, runtimes, downtimes, cycle times, rotational speeds, temperatures
  • Production data: Quantities produced, scrap rates, quality parameters, lead times
  • Process data: Pressures, temperatures, flow rates, chemical parameters
  • Energy data: Consumption of electricity, gas, compressed air, and water
  • Environmental data: Ambient temperature, humidity, air quality
  • Logistics data: Material inventories, material flows, stock levels
  • Quality assurance data: Measurement values, test results, deviations

It’s important to first focus on the data directly connected to your business goals and that provide measurable value. In most cases, gradually expanding the number of data points collected is more effective than trying to capture everything from the start.

For successful implementation and use of a data acquisition system, various competencies are required:

  • Automation technology: Understanding of PLCs, sensors, and industrial networks
  • IT infrastructure: Knowledge of networking, server systems, and databases
  • Data analysis: Skills in evaluating and interpreting production data
  • Software development: Basic programming knowledge for customization and integration
  • Cybersecurity: Expertise in security concepts for industrial systems
  • Process knowledge: In-depth understanding of your own production processes
  • Project management: Skills for planning and managing complex implementation projects

In practice, a cross-functional team from different departments (production, IT, quality assurance, maintenance) is recommended, along with external support from specialists if needed. Continuous training and knowledge transfer are crucial to benefit from data acquisition in the long term.

Still have questions? We are here for you!

Whether technical details, integration or possible uses – our team will be happy to help you. Contact us and get the answers you need!

Would you like to find out more about data acquisition?

Whether you need initial insights, specific questions or concrete use cases – we can help you! Contact us for individual consultation and solutions.

Madeleine Mickeleit

CEO | IIOT Expert