Home | english  | Impressum | Sitemap | KIT

Business Data Analytics

The research group “Buisness Data Analytics” dedicates its work to research and education on data science, predictive analytics, managerial decision making, as well as on the foundations of dimensionality reduction and probabilistic reasoning in large datasets.


Dr. Alexander Gröschel
+49 (721) 9654-804
groeschel∂fzi de
  Dr.-Ing. Nico Rödder
+49 (721) 9654-814
roedder∂fzi de


Wissenschaftliche Mitarbeiter

Lena Frankenhauser
+49 (721) 9654 809
  Nicolas Haubner
+49 (721) 9654 818


  • Telecommunications
  • Corporate Financial Controlling
  • Elektromobility
  • Public Management


  • Statistical Learning
  • Combinatorial Optimization
  • Data Dimensionality Reduction
  • Feature Extraction and Generation


  • 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) http://css.iism.kit.edu/26_144.php
  • 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)