Many decisions in professional and private life are taken on the basis of data that come from all sorts of information systems. Decision Support Systems (DSS) and Business Intelligence & Analytics are about the way we can use data stored in those information systems to generate new and useful information, that can support executive managers in taking business decisions. For example, information that identifies potential risks (or new opportunities) for the company, like: excessive costs, improper inventory levels, bad scores on key performance indicators, high customer churn, potential fraud, etc. In this course, we will introduce concepts and computerized methods for, data- and knowledge-driven, decision-making.
After completing this course, students will be able to:
- identify the need for computerized support of managerial decision-making, and the conceptual foundations and technologies of the DSS methodology.
- comment on descriptive and reporting analytics. In addition, students have to develop a performance dashboard for decision and risk analysis.
- use the data mining process for the development of analytics on business data. Comment on the objectives and benefits of data mining.
- evaluate neural networks algorithms and/or other data mining models. In addition, students are able to analyse data sets using neural networks for predictive analytics.
- explain the characteristics of knowledge management and apply its concepts in a business setting.
- work with dedicated software for DSS:
- MS Access for database management;
- MS PowerBI for performance dashboard development;
- Software to develop a rule-based knowledge system;
- and software for data mining.
The grading is determined for 60% by a written exam and for 40% by a practical part. The practical part includes:
- The development of a performance dashboard with visual analytics for business decision support;
- The development of a knowledge-based (expert) system, that gives advice related to a decision-making problem.
- The development of a data mining model in support of business decision-making.
Development entails: system design, data analysis, the writing of a paper, and presentations.
The mark for the practical part and written exam, should both be at least 4.5 out of 10. Partial results (exam, practical part) are not valid in the next year.
Management and Information Systems (330093)
The course consists of three main domains related to computerized techniques for decision support:
- Descriptive analytics: Here we explain how data is organized in a data warehouse as a prior step to decision analysis. From the data warehouse, business reports, queries, alerts, and trends can be developed using various reporting tools and techniques, such as OLAP and performance dashboards;
- Predictive analytics: Here we discuss the data mining process and how to predict economic and business variables by mining data with neural networks and/or other data mining models.
- Prescriptive analytics: Here we give an introduction to artificial intelligence, knowledge-based (expert) systems (KBS), and knowledge management. KBS's can be used to identify and assess risks in various business domains and applications.
Type of instructions
Lectures, computer labs, and group assignments/projects.
Type of exams