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Data Analytics & Business Modeling
level of course unit
2.Semester Master: 1st Study cycleLearning outcomes of course unit
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
• 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
prerequisites and co-requisites
2. Semester: no information
course contents
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)
• 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)
recommended or required reading
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.
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.
assessment methods and criteria
Module exam (Data Analytics & Business Modeling, Risk Management & Monitoring, Forecasting Methods & Scenario Techniques, Mergers & Acquisitions)
language of instruction
Germannumber of ECTS credits allocated
2.5eLearning quota in percent
0course-hours-per-week (chw)
2planned learning activities and teaching methods
• 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.
