How can measurement data be made usable across departments in industrial plants?

Cross-departmental usability of measurement data requires centralized, high-resolution data acquisition and consistent storage without data silos. With ibaPDA, process, control, and sensor data from multiple sources are synchronously captured and stored long-term in ibaHD-Server, a central data historian. Using ibaAnalyzer, this data can be flexibly analyzed and prepared for different departments, including retrospective KPI calculations. In addition, ibaDatCoordinator enables structured data distribution into IT and OT systems.

How can data silos be eliminated in industrial process analytics?

Data silos typically arise from separated systems used by production, maintenance, and engineering, each maintaining its own datasets. A centralized measurement data architecture using ibaPDA for acquisition and the ibaHD-Server for long-term storage consolidates all relevant data sources. This enables combined analysis of vibration, process, and quality data without information loss. ibaAnalyzer supports direct drill-down from aggregated KPIs to raw signal level.

How is KPI drill-down to raw data implemented in industrial systems?

Effective KPI drill-down requires high-resolution, time-synchronized storage of all raw data. With ibaPDA, this data is continuously acquired and archived in ibaHD-Server, enabling every KPI to be traced back to its underlying measurements. ibaAnalyzer allows interactive navigation from aggregated metrics down to individual signals and timestamps. This enables transparent root-cause analysis without information loss.

What is the role of centralized data storage in industrial data analytics?

Centralized data storage is essential for unifying data from heterogeneous sources such as control systems, sensors, and fieldbuses. ibaHD-Server acts as a central historian platform where all process data is stored long-term and in a time-synchronized manner. This enables the combination of different datasets, for example joint analysis of process and quality data. ibaPDA ensures continuous, high-resolution data acquisition.

How can industrial KPI analyses be created flexibly without programming effort?

Flexible KPI analysis requires tooling that supports both real-time and offline evaluation without programming. With ibaAnalyzer, users can configure custom calculations, visualizations, and reports directly based on raw data. ibaPDA provides the underlying high-resolution measurement data that can be reused for any future analysis. This allows new analytical requirements to be implemented without modifying the data acquisition setup.

Democratizing Measurement Data – An Approach to Cross-Departmental Value Creation

Application

In today’s digitized industrial plants, recording machine and sensor data has become standard practice. Operators and users leverage this data to detect anomalies, better understand processes, and much more. However, since different user groups pursue diverse objectives, data collection and analysis typically occur within department-specific silos. To fully unlock the potential of measurement data, it must be democratized – made holistically accessible and usable across all user groups.

Data Democratization Design Paradigms Conclusion Functions Customers Success Stories More Applications

Unlock the Full Potential of Measurement Data

Data has long since become the most valuable resource of the 21st century; it holds immense worth even in modern industrial environments.

Through advanced data analytics, ongoing processes can be visualized and made comprehensible. The possibilities are vast: analyses and key performance indicator (KPI) calculations enable, for example, reductions in unplanned downtime, improvements in plant efficiency, and assurance of product quality.

Yet, in many organizations, data handling remains fragmented along departmental and application-specific lines, resulting in isolated data silos. Since this limits the full exploitation of data potential, breaking down these silos is imperative. This process is known as data democratization — where data is captured once and made accessible to authorized personnel across the organization. For data-driven enterprises, this step is essential and fosters fact-based communication across departments, enabling holistic process evaluation. However, this can only succeed if relevant data is shared in appropriate formats and via suitable channels.

For measurement systems, several design paradigms are crucial: to capture data from diverse sources, such as controllers from different manufacturers, bus systems, cameras, or HMI screens, comprehensive connectivity to the process is required.

Furthermore, data must be acquired at high resolution and persistently stored long-term within the measurement system. Since different departments typically require different KPIs, this approach enables flexible analyses based on raw data. At the point of data acquisition, it need not yet be determined which analyses will be performed.

The captured data must be stored centrally in a historical data server. This facilitates linking various datasets, for example, joint analysis of vibration data alongside production data can yield deeper insights. Crucially, analyses and KPI calculations can also be supplemented retrospectively. It is not sufficient to provide KPIs alone. Instead, it is necessary to enable a “drilldown” from KPIs back to the raw data, allowing comprehensive root cause analyses without any loss of information.

To minimize barriers for different user groups, an easy-to-use tool ecosystem for data analysis must be available. Users should be able to configure calculations and visualizations in dashboards without requiring programming skills.

Fundamental Design Paradigms of Measurement Systems

Comprehensive Process Connectivity for High-Resolution Data Acquisition: Ensure seamless integration with various data sources to capture high-resolution sensor data across the entire process.
Long-Term Persistence of High-Resolution Measurement Data: Store measurement data reliably over extended periods to enable flexible, retrospective analysis.
Unified Data Foundation for Cross-Functional Use: Create a single data repository to facilitate shared access and collaboration across all departments.
KPI Drilldown to Raw Data for Root Cause Analysis: Enable detailed root cause investigations by linking aggregated KPIs back to the original raw data.

Conclusion

Overall, democratizing measurement data offers numerous benefits: different data sources can be quickly correlated, and highly detailed raw data enables flexible, customized analyses. This in turn supports fact-based communication across diverse user groups, helping to prevent disruptions and failures. Data democratization thus drives increased value creation throughout the entire organization.

Measurement data must not belong to a single department or be hidden in data silos – instead, they must represent the entire process and be stored in an openly accessible manner.

Dr. Andreas Quick
Head of Product Management, iba AG

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Frequently Asked Questions (FAQ)

How can measurement data be made usable across departments in industrial plants?: Cross-departmental usability of measurement data requires centralized, high-resolution data acquisition and consistent storage without data silos. With ibaPDA, process, control, and sensor data from multiple sources are synchronously captured and stored long-term in ibaHD-Server, a central data historian. Using ibaAnalyzer, this data can be flexibly analyzed and prepared for different departments, including retrospective KPI calculations. In addition, ibaDatCoordinator enables structured data distribution into IT and OT systems.
How can data silos be eliminated in industrial process analytics?: Data silos typically arise from separated systems used by production, maintenance, and engineering, each maintaining its own datasets. A centralized measurement data architecture using ibaPDA for acquisition and the ibaHD-Server for long-term storage consolidates all relevant data sources. This enables combined analysis of vibration, process, and quality data without information loss. ibaAnalyzer supports direct drill-down from aggregated KPIs to raw signal level.
How is KPI drill-down to raw data implemented in industrial systems?: Effective KPI drill-down requires high-resolution, time-synchronized storage of all raw data. With ibaPDA, this data is continuously acquired and archived in ibaHD-Server, enabling every KPI to be traced back to its underlying measurements. ibaAnalyzer allows interactive navigation from aggregated metrics down to individual signals and timestamps. This enables transparent root-cause analysis without information loss.
What is the role of centralized data storage in industrial data analytics?: Centralized data storage is essential for unifying data from heterogeneous sources such as control systems, sensors, and fieldbuses. ibaHD-Server acts as a central historian platform where all process data is stored long-term and in a time-synchronized manner. This enables the combination of different datasets, for example joint analysis of process and quality data. ibaPDA ensures continuous, high-resolution data acquisition.
How can industrial KPI analyses be created flexibly without programming effort?: Flexible KPI analysis requires tooling that supports both real-time and offline evaluation without programming. With ibaAnalyzer, users can configure custom calculations, visualizations, and reports directly based on raw data. ibaPDA provides the underlying high-resolution measurement data that can be reused for any future analysis. This allows new analytical requirements to be implemented without modifying the data acquisition setup.