Data Analytics & Business Modeling

2.Semester Master: 1st Study cycle

The students: • understand the potential, but also the challenges of Big Data for business modeling • can apply selected statistical and quantitative methods for business modeling • can interpret results from data analytics and use them for business modeling • can set up business analytics reporting

2. Semester: no information

Fundamentals: • 4 development stages of business analytics (descriptive analytics, diagnostic analytics, predictive analytics, prescrip-tive analytics) • Change of control processes (reactive-analytical vs. proactive-forecasting; agile, real-time and based on data analy-sis; fact-based, differentiated and fast; cross-company and cross-value-added) • Changing business modeling framework (highly trained specialists; changing roles, organizations, and profiles; infor-mation processes and quality of decisions; use of internal and external data; consistent governance) Analysis methods: • Structural testing analysis methods (regression analysis [linear, non-linear, logistic, exponential, etc.], time series analysis, variance/covariance analysis, discriminant analysis, contingency analysis, structural equation analysis, con-joint analyses) • Structural discovery analysis methods (factor analysis, cluster analysis, neural networks, multidimensional scaling, correspondence analysis, data envelopment analysis) Business Analytics Process: • Problem identification (identification of the need for action, delineation of issues, formulation of tasks) • Exploration (data acquisition, data mining) • Optimization (determination of implementation hurdles and costs, planning and budgeting, development of optimiza-tion concept) • Monitoring (monitoring effectiveness, setting up a monitoring system, defining key performance indicators)

Becker, W., Ulrich, P. & Botzkowski, T. (2016) Data Analytics im Mittelstand, Wiesbaden. Dorschel, J., Hrsg. (2015) Praxishandbuch Big Data: Wirtschaft - Recht - Technik, Wiesbaden. Knauer, D. (2015) Act Big - Neue Ansätze für das Informationsmanagement: Informationsstrategie im Zeitalter von Big Data und digitaler Transformation, Wiesbaden. Jahn, M. (2017) Industrie 4.0 konkret: Ein Wegweiser in die Praxis, Wiesbaden.

Module exam (Data Analytics & Business Modeling, Risk Management & Monitoring, Forecasting Methods & Scenario Techniques, Mergers & Acquisitions)

German

2.5

0

2.0

• The course, which is mostly dialog-oriented, usually consists of the triad of practical relevance, academic structuring, and the independent development of integrative case studies from immediate professional and consulting practice.

2

Director of studies

1

3

integrated lecture

Compulsory

none