Business Data Analytics

The research group "Business Data Analytics" focuses on the opportunities and challenges of a world that is increasingly influenced by digital data. Our goal is always to extract substantial added value from data and information and bring it to use. This requires a holistic view of the use cases, technically and economically. We research both analytics/AI methods and processes, their practical application and integration into the corporate or social context. Our strengths on the technical side include Analytics, Machine Learning, and Method Hybridization. On the economic side we have competencies in Business Case Identification, Business Model Development, and Incentive Engineering. We successfully collaborate with many partners across industries with companies and in publicly funded projects.

Head of Area

Wolfgang Badewitz
+49 (721) 9654 823

badewitz∂fzi.de

Members

Tobias Kölbel

tobias.koelbel∂kit.edu

Marvin Motz
+49 (721) 608-48344
marvin.motz∂kit.edu

Alexander Grote
+49 (721) 608-48344
alexander.grote∂kit.edu

Felix Sterk
+49 (721) 608-48387
felix.sterk∂kit.edu

Thimo Schulz
+49 (721) 608-48382
thimo.schulz∂kit.edu

Domains

Automotive
E-Finance
Industry 4.0
Mobility
E-Learning
Telecommunications
Data Marketplaces

Methods

Statistical Learning
Combinatorial Optimization
Advanced Analytics
Case Study Research
Incentive Engineering
Method Hybridization

Projects:

Former Projects:

OCROSS
ReKoNet
Self-Sovereign & Trustworthy Marketplaces
Data Quality Management im Corporate Financial Controlling
Accuracy improvement of vast amounts of heterogeneous judgmental cash flow forecasts using analytical debiasing methods and combination with model forecasts (in collaboration with Bayer AG)
Concise representation of cash flow forecasting- and revisioning-behavior processing analytical-orthogonal and Bayes-based metrics in corporate financial controlling (in collaboration with Bayer AG)
Non-addictive Information Systems
Design of robust and concise metrics to represent and cluster the purchasing and usage history of telecommunication customers used in campaign management (in collaboration with a global telecommunications company)
Techniques to decompose constraint matrices in packing problems and step-wise generation of variance-preserving, pseudo-perpendicular constraints aimed at transforming high-dimensional MIP into lower-dimensional problem representations that allow for more efficient and scalable problem solving (in cooperation with Siemens AG)
Productive 4.0
Accuracy improvement of vast amounts of heterogeneous judgmental cash flow forecasts using analytical debiasing methods and combination with model forecasts (in collaboration with Bayer AG)
Concise representation of cash flow forecasting- and revisioning-behavior processing analytical-orthogonal and Bayes-based metrics in corporate financial controlling (in collaboration with Bayer AG)
Modelling and prediction of user behavior related electric vehicle high-voltage battery aging, based on heterogeneous field-data (in collaboration with a large German OEM)
Design of robust and concise metrics to represent and cluster the purchasing and usage history of telecommunication customers used in campaign management (in collaboration with a global telecommunications company)
Development of novel analytical approaches in the context of Geographic Information Systems (GIS) that allow a faster and more reliable consideration of vast amounts of heterogeneous and unreliable data in disaster and emergency management (BMBF-founded Project; program: Big Data
Techniques to decompose constraint matrices in packing problems and step-wise generation of variance-preserving, pseudo-perpendicular constraints aimed at transforming high-dimensional MIP into lower-dimensional problem representations that allow for more efficient and scalable problem solving (in cooperation with Siemens AG)