Managing Data Quality. Tim King

      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 Group since 2010 and represented BCS in the development of a pair of big data-inspired standards developed by the British Standards Institution (BSI). Julian managed projects to develop asset information guidance and demand analysis guidance for the Institute of Asset Management. His standards development work has included the PAS 1192 standards suite for building information modelling (BIM) and their subsequent translation to the ISO 19650 series. He also contributed to ISO 8000, BS 10102-1 (Big data: Guidance on data-driven organizations) and BS 10102-2 (Big data: Guidance on data-intensive projects).

      Julian regularly delivers conference presentations on data- and asset-related topics and has chaired a number of data-related conferences.

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      The authors would like to thank all the people and organisations whose challenges and approaches to data have created the anecdotes and solutions that have inspired much of the content of this book. We gratefully acknowledge the experts who work in ISO/TC184/SC4/WG13 (Industrial Data) and developed ISO 8000-61, which provides the core focus of this book. Data and Process Advantage Limited have allowed reuse of the ‘Data Zoo’ concept to help illustrate the behavioural aspects of data quality. Thank you to Ian Rush for the inspiration behind the ‘Do your data trust you?’ example.

      Acknowledgements

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      BIM building information modelling

      BSI British Standards Institution

      CDO chief data officer

      CIO chief information officer

      CTO chief technology officer

      DMBOK Data Management Body of Knowledge

      EDMS electronic document management system

      GDPR General Data Protection Regulation

      HUMS health and usage monitoring system

      IoT Internet of Things

      ISO International Organization for Standardization

      IT information technology

      JPEG Joint Photographic Experts Group

      MDM master data management

      NASA National Aeronautics and Space Administration

      NHS National Health Service

      PAF Postcode Address File

      PoD Prophet of Doom

      SEP Somebody Else’s Problem

      USPS United States Postal Service

      ABBREVIATIONS

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      Glossary

      Accuracy: Agreement between a data item and the entity that it represents. For reference, accuracy should be checked to ensure that: each data item links to a specific entity; each entity has a data entry related to it.

      Attribute: Data field used to record the characteristics of an entity. Single unit of data that in a certain context is considered indivisible (ISO/TS 21089:2018).

      Chief data officer (CDO): An individual appointed at senior level in an organisation to facilitate the effective specification, acquisition, exploitation and governance of data. CDO also can refer to chief digital officer; however, this role is typically