Handbook of Data Quality
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Handbook of Data Quality

Research and Practice
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Shazia Sadiq
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This multi-pronged approach to data quality management covers Organization: processes, policies and standards needed to set data quality objectives; Architecture: the technological landscape for deploying them and Computation: required tools and techniques.
Research and Practice in Data Quality Management.- Data Quality Management Past, Present, and Future: Towards a Management System for Data.- Data Quality Projects and Programs.- On the Evolution of Data Governance in Firms: The Case of Johnson & Johnson Consumer Products North America.- Cost and Value Management for Data Quality.- Data Warehouse Quality: Summary and Outlook.- Using Semantic Web Technologies for Data Quality Management.- Data Glitches: Monsters in your Data.- Generic and Declarative Approaches to Data Quality Management.- Linking Records in Complex Context.- A Practical Guide to Entity Resolution with OYSTER.- Managing Quality of Probabilistic Databases.- Data Fusion: Resolving Conflicts from Multiple Sources.- Ensuring the Quality of Health Information: The Canadian Experience.- Shell's Global Data Quality Journey.- Creating an Information Centric Organisation Culture at SBI General Insurance.- Epilogue: The Data Quality Profession.
The issue of data quality is as old as data itself. However, the proliferation of diverse, large-scale and often publically available data on the Web has increased the risk of poor data quality and misleading data interpretations. On the other hand, data is now exposed at a much more strategic level e.g. through business intelligence systems, increasing manifold the stakes involved for individuals, corporations as well as government agencies. There, the lack of knowledge about data accuracy, currency or completeness can have erroneous and even catastrophic results.

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