Abstract—This paper demonstrates how the soft systems methodology can be used to improve the delivery of a module in data warehousing for fourth year information technology students. Graduates in information technology needs to have academic skills but also needs to have good practical skills to meet the skills requirements of the information technology industry. In developing and improving current data warehousing education modules one has to find a balance in meeting the expectations of various role players such as the students themselves, industry and academia. The soft systems methodology, developed by Peter Checkland, provides a methodology for facilitating problem understanding from different world views. In this paper it is demonstrated how the soft systems methodology can be used to plan the improvement of data warehousing education for fourth year information technology students. Keywords—Data warehousing, education, soft systems methodology, stakeholders, systems thinking. I. INTRODUCTION TUDENTS in information technology (IT) study data warehousing (DW) as part of their fourth and final year. Most students experience some kind of paradigm shift when doing this module. They start thinking about IT from a wider organisational perspective. The DW module must be designed in a way to facilitate the development of the students’ understanding. It also needs to cater for the expectations of various role players. A holistic understanding is therefore required. The systems thinking movement developed from the need to have a more holistic understanding of problem situations. This paper aims to show how the problem situation of module planning in data warehousing can be improved by using systems thinking ideas and specifically the soft systems methodology (SSM). The paper provides a discussion on data warehousing and the specific characteristics of DW education in section II. Section III provides background on systems thinking and the SSM. As the aim of this paper is to demonstrate how the SSM can be used in DW module planning, section IV provides a discussion on the aspects of SSM applied to this problem situation. Conclusions on the advantages of SSM in this situation are given in section V. Roelien Geode is with the Vanderbijlpark Campus of the North-West University, South-Africa (e-mail: roelien.goede@nwu.ac.za). Estelle Taylor is with the Potchefstroom Campus of the North-West University, South-Africa (e-mail: estelle.taylor@nwu.ac.za). II. DATA WAREHOUSING AND DATA WAREHOUSING EDUCATION The aim of this section is to present some background knowledge on DW, including the differences between data warehouses and transactional systems, to demonstrate the shift in thinking required by the students to master the field. The section starts with a brief discussion on the historical development of DW methodology. The section ends with a brief description of the current DW module at the North-West University in South Africa where this research was done. Data warehousing developed in the 1990s from the need to integrate data from different information sources in large corporations to support decision making. Inmon wrote what later became the pivotal monograph in the field in 1996. He defines a data warehouse as: “A subject oriented integrated, non-volatile, and time variant collection of data in support of management decisions.” [1]. He advocates a data-driven methodology where data in the organisation are integrated into a central data store and accessed by end-users through star joins. This process starts with data and ends with requirements. He is sometimes criticized for neglecting the business perspective. This is only partly true since he tries to empower the business user in taking full responsibility for using the data in the data warehouse [1]. In reaction to this data-driven approach Ralph Kimball developed a requirements driven approach to data warehousing, where the development is driven by a business sponsor aiming to seek information to address key business problems [2]. Although the order of activities might differ in the two methodologies the activities are similar. Fig. 1 depicts the main activities in the Kimball methodology: Fig. 1 Kimball lifecycle [4] A Soft Systems Methodology Perspective on Data Warehousing Education Improvement R. Goede, and E. Taylor S World Academy of Science, Engineering and Technology International Journal of Computer and Information Engineering Vol:6, No:11, 2012 1416 International Scholarly and Scientific Research & Innovation 6(11) 2012 ISNI:0000000091950263 Open Science Index, Computer and Information Engineering Vol:6, No:11, 2012 publications.waset.org/6675/pdf