Data Screening
& Analysis

FAQ

What is data analysis?

Data Analysis is the process of examining, cleaning, transforming and interpreting data to extract useful information, patterns, trends and insights. Data analysis can be applied to various types of data, including text data, numerical data, images, audio signals and videos. The results can be diverse, such as text classification, trend and correlation detection in tables, image recognition and classification or anomaly detection in audio signals.

What steps does Data Analysis involve?

Data Analysis helps to gain valuable insights from raw data and usually involves six basic steps:

• Data collection: In this step, data is collated from various sources and data sets to create a comprehensive database.
• Data cleansing: An examination of the data is carried out to correct incorrect values, add missing data and identify and deal with outliers.
• Data pre-processing: The data is prepared for analysis, possibly by formatting, aggregating, transforming or scaling it.
• Data analysis: In this step, various statistical or machine learning methods are applied to recognise trends or correlations.
• Interpretation: The results of the analysis are interpreted by experts in order to understand their meaning and gain insights from them.
• Validation: Validation is carried out by comparing the results of the analysis with other available information or by applying test procedures.

What is the importance of Data Quality and how can it be improved?

Data Quality is very important for Data Analysis as it influences the accuracy and reliability of the results. It can be improved through measures such as cleansing erroneous data, validation for consistency and plausibility, standardization, regular review of data sources, integration of different data sources, implementation of data quality management and staff training. These steps help to ensure reliable and meaningful data for informed decision-making.

What are typical use cases of Data Analysis in Production?

• Predictive Quality: Data Analysis can be used to identify correlations between production data and quality data to derive predictive and optimization models.
• Predictive Maintenance: Data Analysis can predict when and where maintenance is likely to be required, enabling proactive rather than reactive maintenance planning.
• Optical Inspection: Data Analysis for image data can be used to create machine vision models, which can be used to optically inspect components in real-time and in-process.
• Trend Analysis: Trend Analysis uses time series to identify developments in production performance or quality metrics and to predict trends.
• Supply Chain Optimization: Data Analysis can uncover inefficiencies in the supply chain and suggest improvements to reduce costs and increase on-time delivery.

What are the benefits of Data Analysis in Production?

Data analysis in production offers numerous benefits, including increasing efficiency by optimizing processes, improving product quality and reducing costs. It enables well-founded decisions, predictive maintenance to avoid downtime, optimized production planning and strengthens competitiveness. It also promotes better quality control, supports sustainability goals and increases customer satisfaction with on-time deliveries and high-quality products.