Data Analysis and Evaluation

Industrial data analysis and evaluation describes the process of systematically examining data from machines, sensors, systems, and processes to derive insights, patterns, forecasts, and decision support. It transforms raw data into actionable intelligence – from simple trend analyses to root cause diagnosis for production disruptions.

Data analysis sits in the IIoT stack above the visualization layer and below overarching AI applications. It ranges from descriptive analysis ('What happened?') through diagnostic evaluation ('Why did it happen?') to predictive analysis ('What will happen?'). AI-based pattern recognition has its own dedicated building block.

The quality of analysis stands or falls with the quality of the data foundation. Unstructured, inconsistent, or incomplete data leads to false conclusions. This is why the data acquisition, preprocessing, and standardization building blocks are the necessary foundation for any reliable analysis.

Which types of analysis are concretely used in practice?

These analysis types are deployed in real IIoT projects from our network – from simple KPI evaluation to root cause diagnosis.

Descriptive analysis & KPI reporting

OEE, scrap rate, energy consumption, and machine availability are aggregated and visualized over time periods. What has happened becomes measurable – as a basis for operational control.

Diagnostic analysis & root cause investigation

For quality deviations or unplanned downtime, process parameters, material batches, and operating history are correlated to identify the root cause.

Time series analysis and trend evaluation

Vibration, temperature, and pressure curves are analyzed over time. Early trend deviations are detected before they lead to failures.

Correlation analysis between process and quality data

Which machine parameters influence product quality? Statistical correlation analyses uncover hidden relationships and make process optimization data-driven.

OEE analysis and downtime evaluation

Plant availability, performance, and quality are evaluated using the OEE methodology. Downtime reasons are classified and prioritized – for targeted improvement measures.

Energy analysis and CO₂ evaluation

Energy consumption is analyzed at machine, line, and site level. Load peaks, base load consumption, and savings potential become visible – for ESG reporting and efficiency measures.

Why do so many data sets go unused?

In industrial practice, data analysis rarely fails due to technology – but due to these organizational and structural hurdles.

Poor data quality as the root problem

Missing timestamps, inconsistent units, measurement gaps, and duplicate entries make raw data directly unusable for analysis. Garbage in, garbage out.

Missing contextualization of machine data

A temperature value alone says nothing. Only with machine ID, shift start, order number, and material batch does an analyzable context emerge.

Data silos prevent cross-system analyses

When OT data sits in controllers, order data in ERP, and quality data in MES in isolation, cross-site evaluations and root cause analyses are impossible.

No defined KPIs and analysis objectives

Without clearly defined questions and objectives, analysis becomes a playground without value. Which decisions should the analysis support? This question must come before the tool.

Lack of analytical competencies in the production environment

Data analysis requires both process understanding and statistical competence. This combination is rare in production companies – external support or targeted upskilling is needed.

What does structured data analysis concretely deliver?

Companies in our network report: those who consistently analyze data make better decisions – faster, with less risk, and measurable outcomes.

Faster root cause identification for disruptions

What used to take hours of detective work is achieved in minutes with structured data analysis. Production downtime shortens, repeat failures become less frequent.

Data-driven quality assurance instead of sampling

Continuous process data analysis detects quality deviations early – before scrap is produced. Scrap rates decrease, recall risks reduce.

Well-founded investment decisions

Which machine is the bottleneck? Which site has the highest OEE? Which line causes the most scrap? Data analysis replaces gut feeling with facts.

Continuous process improvement based on data

Trend analyses and benchmarks systematically reveal improvement potentials. Continuous improvement measures are prioritized and their success tracked measurably.