In the age of Industry 4.0, digitization and automation enable extensive recording of machine, process and logistics data. This presents companies in a wide range of industries with the challenge of securely storing large amounts of data and processing it in a useful way in order to gain valuable information from it, forecast future events as accurately as possible and react accordingly.
The data scientists at RISC Software GmbH have extensive expertise and many years of experience in the fields of data management and data analytics. By using modern methods for smart data analysis and forecasting, the challenge of Big Data can be seen as an important opportunity for process and revenue optimization.
In the area of data engineering, extensive amounts of data from different databases and systems are linked together to create a cross-process database. Data models are then developed from these linked data.
The data models are analysed by applying statistical procedures as well as modern methods in the fields of data analytics, visual analytics and machine learning. In the process, correlations, correlations and patterns are identified, which are used for error and cause analysis as well as for continuous quality monitoring.
Choice of methods
In the area of predictive analytics, mathematical forecast algorithms as well as methods of artificial intelligence are used to produce valid forecasts of future developments and to identify bottlenecks or surpluses at an early stage.
Based on these predictions, Prescriptive Analytics is used to derive recommendations for action, resulting in numerous optimizations such as increased efficiency in production processes or improved product quality.