Handbook of Data Quality : Research and Practice / edited by Shazia Sadiq.

Format:

Book

Contributor:

Sadiq, Shazia.

Series:

Gale eBooks

Language:

English

Subjects (All):

Database management.

Information storage and retrieval systems.

Electronic data processing--Management.

Electronic data processing.

Data structures (Computer science).

Information theory.

Information technology--Management.

Information technology.

Database Management.

Information Storage and Retrieval.

IT Operations.

Data Structures and Information Theory.

Business IT Infrastructure.

Local Subjects:

Database Management.

Information Storage and Retrieval.

IT Operations.

Data Structures and Information Theory.

Business IT Infrastructure.

Physical Description:

1 online resource (xii, 438 pages) : illustrations (some color)

Edition:

1st ed. 2013.

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.

Language Note:

English

Summary:

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. The individual chapters present both an overview of the respective topic in terms of historical research and/or practice and state of the art, as well as specific techniques, methodologies and frameworks developed by the individual contributors. Researchers and students of computer science, information systems, or business management as well as data professionals and practitioners will benefit most from this handbook by not only focusing on the various sections relevant to their research area or particular practical work, but by also studying chapters that they may initially consider not to be directly relevant to them, as there they will learn about new perspectives and approaches.

Contents:

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.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

9783642362576

3642362575

OCLC:

843180440