Advanced Job Analytics at Daimler

With approximately 289,300 employees worldwide, Daimler wanted a solution to facilitate and support an active HR department. By using advanced analytics and data science to get insights out of the data - specifically, running a semantic analysis of 3,800 positions - they were able to understand similarities and differences between jobs, know which qualifications were important, and cluster positions to enhance transparency and facilitate more efficient HR processes.

The process started with reading in data from job advertisements and descriptions, as well as selecting data from different Daimler divisions - which was relevant for extracting domain-specific knowledge. The data was then cleaned, including detecting language (English or German) and removing special characters, followed by multiple pre-processing steps such as tagging and applying stop-word filters, and transformation steps such as quantization and clustering. In terms of application, the outputs included multi-dimensional scaling and word clouds.

KNIME is the data analytics tool of choice due to it being fast and versatile and able to easily join many different data sources. It's also fully transparent, enabling other users to see what is going on at each stage, and furthermore, workflows are reproducible. KNIME removes the need for manual data entry - resulting in less errors, and offers a wide variety of advanced analytic features and functionality.