Senior-level sponsorship113
Understand the context114
Identify synergies115
Choose an implementation approach116
Agree the ‘footprint’116
Change management117
Ethical use of data119
Dealing with challenges and issues119
De-risk existing projects120
Securing budget and resources121
Starting implementation122
Summary123
12. THE HUMAN FACTOR – ENSURING PEOPLE SUPPORT DATA QUALITY MANAGEMENT124
People are the solution124
Behaviours and culture125
The employee data agreement126
Strategies for changing data behaviours127
Organisational influences on behaviours129
Summary131
Conclusions132
Bibliography134
Index136
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Figure 1.1 The components of a business activity5
Figure 1.2 A typical life cycle for general data8
Figure 1.3 A typical life cycle for documents10
Figure 2.1 The virtuous circle of data quality16
Figure 2.2 The data triangle19
Figure 3.1 Overview of the Data Zoo29
Figure 6.1 The ISO 8000-61 process model58
Figure 7.1 Capability Level 1 of data quality management61
Figure 7.2 Capability Level 2 of data quality management63
Figure 7.3 Capability Level 3 of data quality management65
Figure 7.4 Capability Level 4 of data quality management66
Figure 7.5 Capability Level 5 of data quality management67
Figure 7.6 Overall capability model for data quality management68
Figure 8.1 The ISO 8000-61 processes by capability level70
Figure 8.2 Conceptual data model example78
Figure 8.3 Logical data model example79
Figure 8.4 The role of measurement criteria in improving data quality management87
Figure 8.5 Example Ishikawa diagram91
Table 1.1 An example data set13
Table 3.1 Comparison between real world and information world behaviours27
Table 5.1 The knowledge areas of the DAMA-DMBOK (2nd edn.)54
Table 5.2 The processes of data quality management as specified by ISO 8000-6155
Table 9.1 A maturity assessment scale for organisational data quality management95
Table 10.1 People-related improvement opportunities102
Table 10.2 Technology-related improvement opportunities102
Table 10.3 Process-related improvement opportunities103
Table 10.4 The impacts of good and bad data106
Table 11.1 Data quality management implementation considerations114
LIST OF FIGURES AND TABLES
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AUTHORS
TIM KING
Tim is a somewhat accidental leader in the subject of data quality. He was in the right place at the right time in 2006 to be appointed by the International Organization for Standardization (ISO) as convenor of the newly created working group, Industrial Data Quality (ISO/TC184/SC4/WG13). He has since learnt from more than 150 participating international experts in the subject to develop ISO 8000, the international standard for data quality.
In fact, Tim had already been building his own relevant expertise by developing and implementing standards for data exchange during the previous 15 years. He is employed by Babcock International, where, alongside his standards work, he has undertaken a large number of consultancy projects to deliver increased value from data. These projects are typically for owners and operators of high-value, complex assets. These organisations have included NATO, Shell, Rolls-Royce, Network Rail, the UK National Nuclear Laboratory and the UK Ministry of Defence.
To support these consultancy projects, Tim has developed approaches for testing the maturity of organisations in managing and exploiting data. He is a Fellow of BCS and also of the Institute of Mechanical Engineers.
Outside work and family life, Tim’s main passion is for the sport of croquet, which he plays at international level.
JULIAN SCHWARZENBACH
Julian is a data manager and ‘data evangelist’ with many years of experience across various industries and organisations in using data to achieve positive organisational outcomes.
Having started his working life as an engineer, Julian’s career has gradually moved to focus on data through roles in organisations in steel fabrication and heavy engineering, automotive component manufacturing, quarrying and water. Consultancy roles have covered industries as varied as rail, water, electricity transmission, social housing, petrochemicals and ancient monuments. Much of Julian’s focus on data management has been as an enabler for effective asset management of infrastructure and maintenance management.
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AUTHORS
Additionally, Julian has been chair of the BCS Data Management Specialist
