Service Analytics A

Recommendations:

The lecture is addresed to students with interests and basic knowledge in the topics of Operations Research, decritptive and inductive statistics.

• The Geometry of Multivariate Statistics, Wickens, T. D., Psychology Press, 2014.
• Data Mining: Concepts and Techniques, Han, J., Pei, J., Kamber, M., Elsevier, 2011.
• Data Mining and Analysis, Zaki, M. J., Meira Jr, W., Meira, W., Cambridge University Press, 2014.
• An Introduction to Statistical Learning with Applications in R, James, G. et al., Springer, 2013.
• Forecasting – Principles and Practice, Hyndman, R. J., Athanasopoulos, G., OTexts, 2018.
• Fundamentals of Predictive Text Mining, Weiss S. M. et al., Springer, 2015.
  

Paper:

• How Big Data can make Big Impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 2015.
• Business Intelligence and Analytics: from Big Data to Big Impact, Chen, H. et al., MIS quarterly, 2012.
• Building Watson – An Overview of the DeepQA Project, Ferrucci, D. et al., AI Magazine, 2010.
  

Further readings will be provided in the lecture.

Today's service-oriented companies are starting to optimize the way services are planned, operated, and personalized by analyzing vast amounts of data from customers, IT-systems, or sensors. As the statistical learning and business optimization world continues to progress, skills and expertise in advanced data analytics and data and fact-based optimization become vital for companies to be competitive. In this lecture, relevant methods and tools will be considered as a package, with a strong focus on their inter-relations. Students will learn to analyze and structure large amounts of potentially incomplete and unreliable data, to apply multivariate statistics to filter data and to extract key features, to predict future behavior and system dynamics, and finally to formulate data and fact-based service planning and decision models.

More specifically, the lessons of this lecture will include:

• Co-Creation of Value Across Enterprises
• Instrumentation, Measurement, Monitoring of Service Systems
• Descriptive, predictive, and prescriptive Analytics
• Usage Characteristics and Customer Dynamics
• Big Data, Dimensionality Reduction, and Real-Time Analytics
• System Models and What-If-Analysis
• Robust Mechanisms for Service Management
• Industry Applications of Service Analytics

Tutorials
Students will conduct lecture accompanying, guided exercises throughout the semester.

The total workload for this course is approximately 135.0 hours. For further information see German version.

Participants are able to structure large sets of available data and to use that data for planning, operation, personalization of complex services, in particular for IT services. They learn a step-by-step approach starting with analyzing possibly incomplete data, techniques of multivariate statistics to filter data and to extract data features, forecast techniques, and robust planning and control procedures for enterprise decision support.

The assessment consists of a written exam (60 min) (according to §4(2), 1 of the examination regulation).