Georg Juelke is a lead consultant with msg systems ag in Munich, Germany, one of Germany’s leading technology and business consultancies, where he designs, builds and implements data warehouses for large domestic and international clients and advises companies on making better use of their informational capital. His particular interests relate to the transformative power of digitalization with focus on machine learning and the integration of semantic data market to align market supply and demands. He previously worked for Capgemini in the Netherlands in a similar capacity and held the post of Vice President of Business Intelligence at Adecco, the world’s largest staffing and recruitment company. Working out of the UK and Spain, he headed the design and development of Adecco’s global client and candidate analytics platform. He holds an MA in Literature and Psychology from the Ludwig Maximilian University in Munich, Germany and sat at the board of editors for the SAIS Review (School for Advanced International Studies) at the John Hopkins University in Washington D.C.
Aligning human capital supply with market demand
The intersection of demand and supply in consultancy services consists of semantic information in the form of texts, unstructured data, usually in the form of résumés, profiles and job descriptions.
The specific financial and business risks of IT consultancies equate to the challenges of a multi-level inventory; either having too large of an inventory with costly non-productive resources (i.e. consultants on the bench – as a consequence of diminishing or changing demand) or lost business resulting from demand that is not met with adequate resources.
We build a solution that consumes and converts semantic information into a corporate and market analysis.
Machine Learning and Artificial Intelligence
The design and development of such a system needs to solve a sequence of specific challenges with a variety of AI approaches:
1. Text extraction and text mining
2. Classification of important concepts via machine learning models
3. Transferring human knowledge and expertise into an analytical system using knowledge graphs (ontology), classifications and data curation.