Information Quality Management Capability Maturity Model

Transcription

1 Saša Baškarada Information Quality Management Capability Maturity Model

2 VIEWEG+TEUBNER RESEARCH Ausgezeichnete Arbeiten zur Informationsqualität Herausgeber: Dr. Marcus Gebauer, Rüdiger Giebichenstein Die Deutsche Gesellschaft für Informations- und Datenqualität e.v. (DGIQ) fördert und unterstützt alle Aktivitäten zur Verbesserung der Informationsqualität in Gesellschaft, Wirtschaft, Wissenschaft und Verwaltung. Zu diesem Zweck befasst sie sich mit den Voraussetzungen und Folgen der Daten- und Informationsqualität. Sie fördert zudem durch Innovation und Ausbildung die Wettbewerbsfähigkeit der Unternehmen sowie die des unternehmerischen und akademischen Nachwuchses in Deutschland. Die vorliegende Schriftenreihe präsentiert ausgezeichnete studentische Abschlussarbeiten in der Daten- und Informationsqualität. Veröffentlicht werden hierin die Siegerarbeiten des jährlich stattfindenden Information Quality Best Master Degree Award.

3 Saša Baškarada Information Quality Management Capability Maturity Model With a foreword by Professor Andy Koronios VIEWEG+TEUBNER RESEARCH

4 Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the Internet at Dissertation University of South Australia, 2008 Das Buch wurde mit freundlicher Unterstützung der KPMG AG Wirtschaftsprüfungsgesellschaft erstellt. KPMG ist eine der führenden Wirtschaftsprüfungs- und Beratungsgesellschaften weltweit, die im Rahmen von Information Risk Management weitreichende Expertise im Bereich Daten- und Informationsqualität hat. 1st Edition 2009 All rights reserved Vieweg+Teubner GWV Fachverlage GmbH, Wiesbaden 2009 Editorial Office: Dorothee Koch Anita Wilke Vieweg+Teubner is part of the specialist publishing group Springer Science+Business Media. No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the copyright holder. Registered and/or industrial names, trade names, trade descriptions etc. cited in this publication are part of the law for trade-mark protection and may not be used free in any form or by any means even if this is not specifically marked. Cover design: KünkelLopka Medienentwicklung, Heidelberg Printing company: STRAUSS GMBH, Mörlenbach Printed on acid-free paper Printed in Germany ISBN

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6 Foreword Modern enterprises have recently paid greater attention to data and information as a valuable strategic resource. The vast volumes of data currently being generated as well as governance requirements, such as Sarbanes-Oxley, Basil II and other regulatory mandates, have made it imperative for organisations to pay attention to the way that they govern and manage their IT resources in general, and data and information in particular. Furthermore, enterprises have realised that information is indeed one of their most valuable and strategic resources, and that through greater aggregation and analysis of data, improved business insight can lead to significantly greater competitive advantages. In this environment, with greater attention to information as a valuable resource and the continued voracious generation of data, information quality initiatives are now emerging as must do projects, to address the questionable quality of information and thus decision making in contemporary enterprises. It is, however, often assumed that a data quality initiative is a once-off activity. Indeed, many organisations have difficulties deciding where to start and how to approach their data quality initiatives. This book provides a detailed guide for organisations to understanding the issues of data quality as well as a toolset for measuring the data quality maturity and preparedness of their enterprise. This can then lead to a more effective initiative for improving the level of data quality and thus enhancing the value of their precious information resources. Professor Andy Koronios

7 Preface The awareness of the importance of information quality to effective decision making and successful business operations is growing world-wide. However, even though the majority of organisations are affected by information quality problems, only the minority of organisations have any information quality management programs in place. Furthermore, assessing, improving, and managing information quality has proven to be a notoriously difficult undertaking. There is thus a need for a holistic approach to the assessment and enhancement of information quality management practices. In view of that, this research builds on contemporary information quality management theories as well as established total quality management theories to produce a capability maturity model for information quality management process assessment and improvement. Particular focus has been placed on the domain of engineering asset management organisations primarily dealing with the management of engineering assets have been selected for the purpose of constructing and validating the model through the application of case study methodology. Given that this research explores an under-researched problem in information quality management and aims at building a deeper understanding of the information quality management capability maturity concept, the interpretive research paradigm has been adopted. Accordingly, several qualitative and quantitative research methodologies are employed. Six exploratory case studies and a four round Delphi study are employed to gain a better understanding of the research problem, identify the maturity indicators, and build the preliminary Information Quality Management Capability Maturity Model (IQM-CMM). The model is then applied in seven international case studies for further enhancement and external validation. This research makes a significant contribution to existing information quality management theory by identifying a large number of relevant maturity indicators and mapping them to a staged evolutionary capability maturity model. The final

8 X Preface product is a set of evaluation tools and methodologies, which are intended to assist with the identification of problems in information management as well as information quality management practices. Thus, IQM-CMM may provide organisations with a measure of their maturity in information quality management, along with recommendations for increasing the level of maturity. This may result in enhanced information quality, organisational learning and decision making. Dr. Sa a Ba karada

9 Contents 1Introduction Background to the Research Research Problem and Research Questions Justification for the Research Aim of this Research The Scope and Key Assumptions Methodology Findings Outline of the Book Conclusion 12 2 Literature Review Introduction Total Quality Management Gurus Critical Success Factors for TQM Six Sigma Summary Quality Awards and Frameworks The Deming Prize Japan Quality Award Singapore Quality Award Malcolm Baldrige National Quality Award Canadian Framework for Business Excellence Australian Business Excellence Framework European Foundation for Quality Management Excellence Model Summary Quality Standards and Best Practices ISO

10 XII Contents ISO/IEC Capability Maturity Model Capability Maturity Model Integration Control Objectives for Information and Related Technology Information Technology Infrastructure Library ANSI/GEIA The Level of Adoption Summary Data and Information Quality From Data to Information Definitions Implications Causes The Product Analogy Assessments Management Maturity Models Summary Engineering Asset Management Conclusion 86 3 Research Methodology Introduction Philosophy of Science Defining Science Theories Causality Rationalism vs. Empiricism Inductivism Popper s Falsificationism Conventionalism Scientific Research Programmes 98

11 Contents XIII Scientific Paradigms Justification for the Qualitative Interpretive Research Approach Research Design Stage 1: Exploratory Phase Stage 2: Theory Building Phase Stage 3: Theory Testing and Enhancing Phase Data Collection Theoretical Sampling Interviewing Data Analysis Constant Comparative Analysis Hermeneutics Pattern Matching Explanation Building Within-Case Analysis Cross-Case Analysis Criteria for Judging the Quality of Qualitative Interpretive Research Construct Validity Internal Validity External Validity Reliability Ethical Considerations Limitations Conclusion Analysis of Data Introduction Exploratory Stage Profiles of the Exploratory Case Study Organisations Candidate Maturity Indicators Theory Building Stage Constructing the Staged Capability Maturity Model 144

12 XIV Contents Constructing the IQM-CMM Diagnostic Tool Theory Testing and Enhancing Stage Assessment Method Profiles of the Explanatory Case Study Organisations Maturity Ratings of the Explanatory Case Study Organisations Cross-Case Analysis Theory Enhancements Discussion Conclusions Conclusions Introduction Conclusions about the Research Questions and Hypotheses Implications for Theory Implications for Practice Limitations Future Research Conclusion 235 References 237 Appendix 287

13 List of Figures Figure 1.1: The Impact of Poor Information Quality 7 Figure 1.2: Research Methodology 9 Figure 1.3: High Level Description of IQM-CMM 10 Figure 2.1: Literature Review Stage of the Research Methodology 13 Figure 2.2: Japan Quality Award 31 Figure 2.3: Steps toward Mature Processes 34 Figure 2.4: History of CMMs 46 Figure 2.5: CobiT 4.0 Processes Addressed by CMMI Figure 2.6: CobiT 4.0 Processes Addressed by ITIL 52 Figure 2.7: TDQM Methodology 68 Figure 2.8: The Key Steps in a Strategic Data Quality Methodology 72 Figure 2.9: Improvement Activities in Data Quality Maturity Levels 76 Figure 2.10: Enterprise Information Management Adoption Model 78 Figure 2.11: Elements of Comprehensive Asset Management 86 Figure 3.1: Representation of Knowledge 88 Figure 3.2: The Research Triangle 101 Figure 3.3: Methodologies and their Related Paradigms 104 Figure 3.4: A Framework for Positivist and Interpretive Research 105 Figure 3.5: Research Methodology 110 Figure 3.6: IQM Capability Maturity Framework 110 Figure 3.7: The Hermeneutic Circle as Applied in this Research 129 Figure 3.8: Multiple Case Study Method 130 Figure 3.9: Information Systems Research Framework 131 Figure 4.1: Exploratory Stage of the Research Methodology 140 Figure 4.2: Information Lifecycle Model 141 Figure 4.3: The Theory Building Stage of the Research Methodology 144 Figure 4.4: IQM-CMM 148 Figure 4.5: Level 2 (REACTIVE) 150

14 XVI List of Figures Figure 4.6: Level 3 (MEASURING) 151 Figure 4.7: Level 4 (MANAGED) 152 Figure 4.8: Level 5 (OPTIMISING) 153 Figure 4.9: The Hierarchical Structure of IQM-CMM 154 Figure 4.10: Theory Testing and Enhancing Stage 157 Figure 4.11: Case G IQM-CMM Appraisal Results 163 Figure 4.12: Case H IQM-CMM Appraisal Results 165 Figure 4.13: Case I IQM-CMM Appraisal Results 166 Figure 4.14: Case J IQM-CMM Appraisal Results 167 Figure 4.15: Case K IQM-CMM Appraisal Results 169 Figure 4.16: Case L IQM-CMM Appraisal Results 170 Figure 4.17: Case M IQM-CMM Appraisal Results 171 Figure 4.18: Information Flow (Case G) 186 Figure 5.1: IQM-CMM 226 Figure 5.2: The Hierarchical Structure of IQM-CMM 228

15 List of Tables Table 2.1: Juran's Quality Trilogy 19 Table 2.2: Seven Tools of Quality 20 Table 2.3: Quality Management Maturity Grid 21 Table 2.4: Commonalities among the Quality Gurus 22 Table 2.5: Six Sigma DMAIC Process 27 Table 2.6: The Categories of the Deming Prize 30 Table 2.7: SQA Award Criteria Weightage 31 Table 2.8: Baldrige Categories and Items 33 Table 2.9: Canadian Framework Road Map to Business Excellence 35 Table 2.10: The Learning Cycle 36 Table 2.11: Australian Business Excellence Framework 37 Table 2.12: Comparison of Quality Model Criteria 39 Table 2.13: Main Components of the ISO 9000:2000 Series 42 Table 2.14: CobiT Maturity Attribute Table 49 Table 2.15: ITIL s Service Management Process Maturity Framework 53 Table 2.16: Data Terminologies 58 Table 2.17: Data Quality Dimensions 60 Table 2.18: Product Manufacturing Analogy for IP Creation 64 Table 2.19: Approaches to Improving Data Quality 69 Table 2.20: CALDEA 74 Table 2.21: Meta Group s Information Maturity Model 75 Table 2.22: Data Management Practice Assessment Levels 77 Table 2.23: Gartner's Data Quality Maturity Model 79 Table 2.24: Gartner s EIM Maturity Model 80 Table 3.1: Major IS Research Paradigms 106 Table 3.2: Criteria for Selecting the Case Study Research Method 113 Table 3.3: Exploratory Case Study Organisations 115 Table 3.4: Exploratory Case Studies Interview Details 115

16 XVIII List of Tables Table 3.5: Explanatory Case Study Organisations 122 Table 3.6: Explanatory Case Studies Interview Details 123 Table 4.1: Profiles of the Exploratory Case Study Organisations 140 Table 4.2: Partial Summary of the Delphi Study Results (rounds 1 and 2) 146 Table 4.3: Partial Summary of the Delphi Study Results (rounds 3 and 4) 147 Table 4.4: Mapping IQM-CMM Maturity Indicators to CMMI PAs 154 Table 4.5: Quality Ratings 161 Table 4.6: Profiles of the Explanatory Case Study Organisations 162 Table 4.7: Case G IQM-CMM Appraisal Summary 164 Table 4.8: Case H IQM-CMM Appraisal Summary 165 Table 4.9: Case I IQM-CMM Appraisal Summary 166 Table 4.10: Case J IQM-CMM Appraisal Summary 168 Table 4.11: Case K IQM-CMM Appraisal Summary 169 Table 4.12: Case L IQM-CMM Appraisal Summary 170 Table 4.13: Case M IQM-CMM Appraisal Summary 172