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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.With these changes, traditional approaches to data management in general, and data quality control specifically, are challenged. There is an evident need to incorporate data quality considerations into the whole data cycle, encompassing managerial/governance as well as technical aspects.Data quality experts from research and industry agree that a unified framework for data quality management should bring together organizational, architectural and computational approaches. Accordingly, Sadiq structured this handbook in four parts: Part I is on organizational solutions, i.e. the development of data quality objectives for the organization, and the development of strategies to establish roles, processes, policies, and standards required to manage and ensure data quality. Part II, on architectural solutions, covers the technology landscape required to deploy developed data quality management processes, standards and policies. Part III, on computational solutions, presents effective and efficient tools and techniques related to record linkage, lineage and provenance, data uncertainty, and advanced integrity constraints. Finally, Part IV is devoted to case studies of successful data quality initiatives that highlight the various aspects of data quality in action.

Handbook of Data Quality

POST SECONDARY FOUR YEAR

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COLJR

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HANDBOOK OF DATA QUALITY

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Advisory Panel.
Dedication.
Preface.
Contents.
1: Prologue: Research and Practice in Data Quality Management.
2: Organizational Aspects of Data Quality.
3: Data Quality Management Past, Present, and Future: Towards a Management System for Data.
4: Data Quality Projects and Programs.
5: Cost and Value Management for Data Quality.
6: On the Evolution of Data Governance in Firms: The Case of Johnson &#x0026; Johnson Consumer Products North America.
7: Architectural Aspects of Data Quality.
8: Data Warehouse Quality: Summary and Outlook.
9: Using Semantic Web Technologies for Data Quality Management.
10: Data Glitches: Monsters in Your Data.
11: Computational Aspects of Data Quality.
12: Generic and Declarative Approaches to Data Quality Management.
13: Linking Records in Complex Context.
14: A Practical Guide to Entity Resolution with OYSTER.
15: Managing Quality of Probabilistic Databases.
16: Data Fusion: Resolving Conflicts from Multiple Sources.
17: Data Quality in Action.
18: Ensuring the Quality of Health Information: The Canadian Experience.
19: Shell&#x0027;s Global Data Quality Journey.
20: Creating an Information-Centric Organisation Culture at SBI General Insurance.
21: Epilogue: The Data Quality Profession.
About the Authors.
Index.

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Handbook of Data Quality