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The right data pre-processing is the basis for successful IIoT projects and a measurable ROI. Whether edge gateways, data integration tools or specialized platforms – we show you tried-and-tested solutions that break down data silos and transform your raw data into valuable knowledge. Find out how companies from our network use holistic strategies to avoid typical pitfalls and implement their digitalization efficiently and securely.
Our platform brings together leading technology partners and solution providers to support you in implementing your data transmission projects. Benefit from tried-and-tested solutions and experienced specialists from the IIoT community who know which transmission technologies work reliably in demanding industrial environments.
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In the real world of manufacturing, sensors and machines constantly generate enormous amounts of data – usually much more than is really relevant for decision-making. Industrial data pre-processing means filtering, aggregating and transforming this raw data directly at the point of origin before it is forwarded.
Instead of transmitting every single measured value unchanged, the data is intelligently processed by edge computing devices directly on the machine: Outliers are detected, signals are smoothed, statistical parameters are calculated and only the truly meaningful information is forwarded. The result is higher-quality data that can be used immediately and is significantly more economical in terms of data volume.
In modern production environments, edge devices perform data pre-processing in real time – from simple filters and averaging to complex analyses of frequency spectra in vibration data. This local intelligence forms the bridge between the physical machine and the higher-level analysis and control systems.
Edge computing relieves the burden on central systems, reduces transmission costs and enables real-time reactions. Instead of sending all raw data unfiltered to a cloud or data center, initial intelligent processing takes place directly on the machine or at the gateway. This allows outliers to be identified, statistical key figures to be formed and only relevant information to be forwarded. In industrial practice, this leads to more stable networks, faster response times and reduced costs – and makes data-driven services economically viable in the first place.
Data pre-processing is the decisive step in generating real added value from the constantly growing stream of data. With intelligent processing methods directly at the point of origin, you can tap into numerous benefits – and lay the foundation for robust, scalable and secure IIoT solutions.
Modern edge computing solutions enable data to be processed directly at the point of origin in real time. Filtering and analysis before transmission drastically reduces response times - an essential prerequisite for time-critical applications in production and process control, where every millisecond counts.
Edge-based pre-processing concepts enable data to be analyzed and filtered directly at the source. This means that only relevant information is forwarded, which significantly reduces bandwidth usage. The costs for data transmission and storage are significantly reduced, while network performance increases at the same time.
The systematic cleansing of data, normalization and standardization of raw data eliminates inconsistencies and significantly improves data quality. This leads to more precise analyses and increases the reliability of AI models and forecasts - the basis for well-founded business decisions and optimization measures.
Pre-processing at the edge significantly reduces the load on central servers and cloud infrastructures. Computing-intensive tasks are decentralized, allowing cloud resources to be used more efficiently and costs to be reduced. This architecture enables greater scalability and better performance of the overall system.
Local pre-processing allows sensitive data to be anonymized or aggregated directly at the point of origin. Only relevant, already processed information leaves the production environment - a decisive advantage for the protection of company and business secrets and for compliance with data protection regulations.
The automation of data pre-processing eliminates error-prone manual processes and ensures consistent results. Standardized workflows ensure reproducible results across different datasets, significantly reducing effort and accelerating the delivery of actionable insights.
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.
Use our platform to discover hands-on best practices and industrial use cases. See how other companies are successfully implementing concrete IIoT projects.
Talk to us – we listen, understand your challenges, and connect you with the right experts and contacts from our community.
Find out how leading companies are using modern data transmission technologies to drive their digital transformation. Each of these examples shows how the right transmission technology as an integral part of a holistic IIoT concept contributes to success.
Find out how Bergi-Plast uses Optalio’s MONITORING.SUITE to reduce machine downtimes, minimize waste and sustainably increase product quality – all through intelligent data pre-processing and real-time analyses.
Find out how STEGO uses the scalable IoT platform from achtBytes to reduce unused compressor runtimes by 75% and detect leaks immediately – thanks to continuous pressure data acquisition, automatic alarming and virtual edge processing without additional hardware.
Experience how INNOWATECH and Data Coffee use a plug-and-play solution to process runtime data locally and make it available on the move – for optimized maintenance, regulatory safety and maximum transparency in water treatment.
Find out how ERAS and Cumulocity combine real-time telemetry, edge computing and AI analytics with the Apex Insights platform. The solution not only supports the optimization of racing cars, but also a wide range of applications in industry, the energy sector, healthcare and logistics.
Many IIoT projects fail not because of the idea, but because of the implementation: a lack of scalability, high operating costs and unclear requirements lead to expensive rework and a failed business case.
On our platform, you will find tried-and-tested technologies, best practices from real industry projects and the collective knowledge of our community. We show you how to avoid typical mistakes with the right technology stack – from data acquisition to AI evaluation – and how to set up your IIoT project economically and future-proof.
Discover how leading companies from our network successfully structure their projects – modular, interoperable and data-secure.
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.
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.
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 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.
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 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.
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.
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.
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.
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 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.
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.
Choosing the right technologies determines the success of your IIoT data pre-processing projects. Industry-proven standards and platforms form the foundation for efficient, secure and scalable processing of industrial data streams – from the machine to the cloud. Our experts will show you which technologies have proven themselves in practice and how they can be optimally combined.
Our specialists analyze your requirements and identify the perfect transmission solution for your individual challenges — fast, precise, and independent of any manufacturer.
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.
Every innovation starts with an idea. Discover proven use cases that support your digital transformation — from predictive maintenance to worker safety.
Real-time monitoring of machine and sensor data to reduce downtime.
Data-driven maintenance to detect failures early and cut costs.
Seamless tracking of assets and material flows in production and logistics.
Automated collection and management of production and operational data.
In our podcast, we bring together users and manufacturers to discuss concrete challenges in data transmission — from both a technical and business-case perspective. Tune in for practical insights and proven solution approaches.
Edge computing enables direct data processing at the source with several decisive advantages: Minimal latency times speed up reactions and decision-making processes. The bandwidth is optimized as less raw data has to be transferred to central systems. Even with an unstable Internet connection, processes remain reliable because Edge devices cache important data. In addition, sensitive production data remains on site, which increases cyber security and makes it easier to meet regulatory requirements.
Edge computing processes data directly at the source (sensors, machines, edge devices) with minimal latency.
Fog computing forms a decentralized layer between the edge and the cloud. Fog nodes (local gateways, on-premise servers) receive pre-processed data, analyze it and forward only relevant information.
Cloud computing means central processing in remote data centers – almost infinitely scalable, but with higher latency and dependency on the network connection.
In practice, a hybrid approach is often chosen, combining the strengths of all levels.
Edge computing lowers data transmission costs by drastically reducing the volume to be transmitted. Intelligent filtering, aggregation and compression of the raw data at the edge can reduce the data volume by up to 95%. Only truly relevant information is transmitted and stored centrally. This leads to considerable savings in network and storage costs as well as cloud usage fees.
Edge computing is primarily used where real-time processing or data reduction are crucial:
Edge computing fundamentally improves data security, as less unfiltered raw data has to be transmitted via external networks. However, the distributed edge devices themselves require comprehensive protection. Implement end-to-end encryption for data transmission and storage, strict access controls with multi-level authentication and physical protection of devices. Central orchestration, regular software updates and a consistent security concept across all levels are essential for consistent protection.
Whether technical details, integration or possible uses – our team will be happy to help you. Contact us and get the answers you need!
Whether you need initial insights, specific questions or concrete use cases – we can help you! Contact us for individual consultation and solutions.
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