Corporate Transformation Management PT
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Data Analytics & Business Modeling

level of course unit

2.Semester Master: 1st Study cycle

Learning 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

prerequisites and co-requisites

2. Semester: no information

course contents

• 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.

assessment methods and criteria

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

language of instruction


number of ECTS credits allocated


eLearning quota in percent


course-hours-per-week (chw)


planned 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.

semester/trimester when the course unit is delivered


name of lecturer(s)

Director of studies

year of study


recommended optional program components


course unit code


type of course unit

integrated lecture

mode of delivery


work placement(s)



Situm Mario
Prof. (FH) DDr. Mario Situm, MBA
Director of Studies
+43 5372 71819 147
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+43 5372 71819 500

Internationales Symposium Restrukturierung

Jährlich findet das „Internationale Symposium Restrukturierung“ an der Fachhochschule Kufstein Tirol statt.

Restrukturierungs- und Turnaround-Management

Handbuch für die Praxis:
Restrukturierungs- und Turnaround-Management, 2. Auflage, Exler (Hrsg.)

Mehr Infos:

Restrukturierungs-Qualitätssiegel für Kufsteiner Masterstudiengang

Der berufsbegleitende Masterstudiengang Unternehmensrestrukturierung & -sanierung ist nach einjähriger Begutachtung offiziell TMA-zertifiziert – das renommierte Qualitätssiegel des Verbandes der deutschen Restrukturierungsexperten (TMA).

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