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Motivation for the study
Data-driven decision support systems are very expensive to develop. This is due to various factors, including the transformation of different data sources to a single platform and a high level of managerial involvement. The magnitude of the data sources involved requires large capacity hardware resources that are expensive. Another cost factor is that due to the nature of the processes simulated in the datadriven decision support system, involvement of senior management in the problem definition is essential. Markus (2000:43) argues that the implementation of enterprise
systems in large corporations have been known to cost over $500 million and because systems are much more tightly integrated than before, failure of one system will have extremely negative consequences for other systems in the organisation.
Data warehouses are typical data-driven decision support systems. Data warehouses integrate various data sources to supply management information in organisations. Many data warehouse projects take longer than originally planned and cost much more than initially budgeted for. The Cutter Consortium reported that 41% of practitioners surveyed have experienced data warehouse failures (Anonymous, 2003:1). Inmon (reported in Ferranti, 1998:1) argues that companies make costly mistakes that cause delays. However, the benefits of successful data warehouses are so significant that the academic research community should search for methods to improve the success rate of data warehouse projects.
The author of this thesis was drawn to systems thinking as a basis for improving data warehouse quality because of the holistic nature of systems thinking. Kevin Strange of Gartner wrote (2001:1): “With respect to the analytical (business intelligence) side of customer relationship management, at least 65% of the efforts are implemented in an unintegrated fashion, based on a function (different efforts by different departments), rather than on a more strategic initiative – the sum is larger than the parts.” Many authors (Mimno, 2001; Eckerson, 2003) argue that business objectives should be central to data warehouse planning and development. This is congruent with the systems approach proposed by Churchman (1968). He advocates that subsystems should work together to achieve the objectives of the system and the University of Pretoria etd – Goede, R (2005) objectives of the subsystem should relate to that of the system. A question worth investigating is whether systems thinking can point practitioners to more successful data warehouse development practices.
This thesis is such a research initiative. The aim of the research is to develop a framework for the explicit use of specific systems thinking methodologies in data warehousing practices. It is assumed that data warehousing practitioners do not know systems thinking methodologies (a valid assumption according to the case study data reported in chapter 5). From data warehousing literature (Kimball et al., 1998; Inmon, 1996), it is clear that the practices of data warehousing professionals can be mapped to systems thinking methodologies. The researcher decided to make
this mapping explicit in order to propose methods, in the form of a framework, to improve data warehouse quality.
CHAPTER 1 INTRODUCTION
1.1 Motivation for the study
1.2 Concepts central to this thesis
1.2.1 Systems thinking
1.2.2 Data-driven decision support systems
1.2.3 Relationship between systems thinking and data-driven decision support systems
1.3 Structure of the thesis
1.4 Research objectives
1.5 Limitations of the study
1.6 Methodology
1.7 Chapterisation
CHAPTER 2 RESEARCH METHODOLOGY
2.1 Introduction
2.2 Philosophy and social research
2.2.1 Positivism
2.2.2 Interpretivism (phenomenological approaches)
2.2.3 Critical social theor
2.2.4 Models applied to information systems
2.3 Methodologies in social research
2.3.1 Positivistic social research methodology
2.3.2 Interpretive social research methodology
2.3.2.1 The fundamental principle of the hermeneutic circle
2.3.2.2 The principle of contextualisation
2.3.2.3 The principle of interaction between the researchers and the subjects
2.3.2.4 The principle of abstraction and generalisation
2.3.2.5 The principle of dialogical reasoning
2.3.2.6 The principle of multiple interpretations
2.3.2.7 The principle of suspicion
2.3.3 Critical social research methodology
2.3.3.1 Abstraction
2.3.3.2 Totality
2.3.3.3 Essence
2.3.3.4 Praxis
2.3.3.5 Ideology
2.3.3.6 Structure
2.3.3.7 History
2.3.3.8 Deconstruction and reconstruction
2.3.3.9 Intervention
2.4 Social research practice
2.4.1 Positivistic social research practice
2.4.2 Interpretive social research practice
2.4.2.1 Case study research practices
2.4.2.2 Ethnography
2.4.2.3 Grounded theory
2.4.2.4 Giddens’ structuration Theory
2.4.3 Critical social research practice
2.4.3.1 Action research
2.5 Research considerations with regard to this study
2.5.1 Philosophical considerations with regard to this study
2.5.2 Methodological considerations with regard to this study
2.5.2.1 Linking methodological aspects of this study to interpretive and critical social methodology
2.5.3 Practical considerations with regard to this study
2.5.3.1 Data collection
2.5.3.2 Applicability of grounded theory (GT) for this study
2.5.3.3 Theory generation and generalisation
2.6 Summary
CHAPTER 3 SYSTEMS THINKING
3.1 Introduction
3.2 Systems thinking and the systems approach
3.2.1 The emergence of systems thinking
3.2.1.1 Reductionism as scientific method
3.2.1.2 Expansionism
3.2.2 Definition of a system
3.2.3 Different classes of systems
3.2.4 Systems as described by the systems approach of Churchman
3.2.4.1 The total system objectives
3.2.4.2 The system’s environment
3.2.4.3 The resources of the system
3.2.4.4 The components of the system
3.2.4.5 The management of a system
3.2.5 Systems thinking and the systems approach
3.2.5.1 The input-output systems approach
3.2.5.2 Objectives of systems thinking
3.2.5.3 Developments in systems thinking
3.2.6 Application of the systems approach
3.3 Philosophical foundations of systems thinking in organisations
3.3.1 Philosophers that influenced systems thinking
3.3.1.1 Karl Popper (1902-1994)
3.3.1.2 Jürgen Habermas (1929-)
3.3.1.3 Herman Dooyeweerd (1894-1977)
3.3.2 Two dimensions of thought in philosophy of system design
3.3.2.1 The subjective – objective dimension
3.3.2.2 The order – conflict dimension
3.3.3 Four paradigms of thought in philosophy of system design
3.3.3.1 Functionalism (objective – order)
3.3.3.2 Social relativism (subjective – order)
3.3.3.3 Radical structuralism (objective – conflict)
3.3.3.4 Neohumanism (subjective – conflict)
3.3.4 Paradigm differences in system development
3.3.4.1 Functionalism (objective – order)
3.3.4.2 Social relativism (subjective – order)
3.3.4.3 Radical structuralism (objective – conflict)
3.3.4.4 Neohumanism (subjective – conflict)
3.3.5 The problem environment: Organisational structures
3.4 Systems thinking methodologies1
3.4.1 Ontological views of syste
3.4.1.1 Hard systems thinking
3.4.1.2 Soft systems thinking
3.4.1.3 Critical systems thinking
3.4.1.4 Disclosive systems thinking
3.4.1.5 Summary
3.4.2 Epistemological views of systems development
3.4.2.1 Construction
3.4.2.2 Evolution
3.4.2.3 Intervention
3.5 Systems practice
3.5.1 Hard systems methodologie
3.5.2 Soft systems methodologies2
3.5.2.1 Introduction to the soft systems methodology2
3.5.2.2 Systems thinking and the SSM
3.5.2.3 The SSM as enquiring process
3.5.2.4 The stream of logic-base enquiry
3.5.2.5 The stream of cultural enquiry
3.5.2.6 Other soft systems methodologies2
3.5.3 Critical systems methodologies2
3.5.3.1 Total systems intervention
3.5.3.2 Critical heuristics of social systems design
3.5.4 Disclosive systems methodology2
3.6 Systems practice in information systems development
3.6.1 Hard systems methodologies1&2 and information system development
3.6.2 Soft systems methodologies1&2 and information system development
3.6.2.1 The SSM and information systems development
3.6.2.2 Soft information systems development methods
3.6.3 Critical systems methodologies1&2 and information systems development
3.6.4 Disclosive systems methodologies1&2 and information systems development
3.7 Summary
CHAPTER 4 DATA WAREHOUSING
CHAPTER 5 CASE STUDY REPORT
CHAPTER 6 FRAMEWORK
REFERENCES
