The mere collection of data for documentation purposes is increasingly receding into the background. More and more companies want to gain competitive advantages over their rivals by generating added value from the collected data mountains and deriving the right decisions from them. At the same time, however, companies must also face the challenges that digitization brings with it – the increasing flood of data from multiple systems and the high complexity of the data often make it difficult to view and understand business-relevant data.
In order to understand information and make hidden knowledge potential accessible, the efficient use of suitable technological tools and “intelligent” algorithms is necessary. Fundamental to this is the digitised know-how of domain experts and the associated optimisation of business and production processes.
By applying statistical procedures, modern methods from the field of data and visual analytics as well as machine learning, the existing knowledge is analysed in context with the recorded data. This allows anomalies and patterns to be identified and, subsequently, additional information about correlations to be derived for error and cause analysis. Using methods from the field of Artificial Intelligence, knowledge is generated and recommendations for action for experts (expert in the loop) are derived.