Information Quality and Governance for Business Intelligence

  • 11h 32m
  • John Talburt, William Yeoh, Yinle Zhou (eds)
  • IGI Global
  • 2014

Business intelligence initiatives have been dominating the technology priority list of many organizations. However, the lack of effective information quality and governance strategies and policies has been meeting these initiatives with some challenges.

Information Quality and Governance for Business Intelligence presents the latest exchange of academic research on all aspects of practicing and managing information using a multidisciplinary approach that examines its quality for organizational growth. This book is an essential reference tool for researchers, practitioners, and university students specializing in business intelligence, information quality, and information systems.

About the Editors

Dr. William Yeoh is a Lecturer in School of Information and Business Analytics at Deakin University. He received his PhD in Business Intelligence from the University of South Australia and he has taught within Australia, Malaysia and Hong Kong. He is a researcher in the areas of business intelligence and information quality. He has published numerous research papers in peer-reviewed journals and conference proceedings. He is a frequent invited speaker in practitioners’ conferences. He has been providing consultancy services to a number of organisations including He was the Deputy Dean for Research and Development (R&D) in Faculty of Information and Communication Technology at University Tunku Abdul Rahman in Malaysia, and was also a Visiting Scholar at University of Arkansas in Little Rock.

Dr. John R. Talburt is a Professor of Information Science at the University of Arkansas at Little Rock (UALR) where he is the Coordinator for the Information Quality Graduate Program and the Executive Director of the UALR Center for Advanced Research in Entity Resolution and Information Quality (ERIQ). He is also the Chief Scientist for Black Oak Partners, LLC, an information quality solutions company based in Little Rock, Arkansas. Prior to his appointment at UALR he was the leader for research and development and product innovation at Acxiom Corporation, a global leader in information management and customer data integration. Professor Talburt is an inventor for several patents related to customer data integration and the author of numerous articles on information quality and entity resolution, and is the author of Entity Resolution and Information Quality (Morgan Kaufmann, 2011). He also holds the IAIDQ Information Quality Certified Professional (IQCP).

Dr. Yinle Zhou is an IBM software architect and data scientist in the InfoSphere MDM development group in Austin, Texas, and also serves as an Affiliate Member of the Graduate Faculty at University of Arkansas at Little Rock (UALR). Dr. Zhou holds a PhD in Integrated Computing with Emphasis in Information Quality (IQ) from UALR where her doctoral research focused on modeling the management of entity identity information in entity resolution systems. She also holds a Master of Science in Information Quality from UALR, a Bachelor of Business Administration in Electronic Commerce from Nanjing University in China, and the Information Quality Certified Professional (IQCP) credential issued by the International Association for Information and Data Quality (IAIDQ). Her research and publications are in areas of information quality, identity management, entity and identity resolution, and social computing.

In this Book

  • Foreword
  • A Conceptual Model of Metadata's Role in BI Success
  • Understanding the Influence of Business Intelligence Systems on Information Quality—The Importance of Business Knowledge
  • Subjective Information Quality in Data Integration—Evaluation and Principles
  • A Case Study on Data Quality, Privacy, and Entity Resolution
  • Business Intelligence for Healthcare—A Prescription for Better Managing Costs and Medical Outcomes
  • IT Architecture and Information Quality in Data Warehouse and Business Intelligence Environments
  • Information Quality Assessment for Asset Management Systems
  • Trends and Research of Wikis' Quality and Governance Based on Bibliometric and Content Analysis
  • Social Media Tools for Quality Business Information
  • Improving Spatial Data Quality Through Spatial ETL Processes
  • Principled Reference Data Management for Business Intelligence
  • Effective Measurement of DQ/IQ for BI
  • Data Profiling and Data Quality Metric Measurement as a Proactive Input into the Operation of Business Intelligence Systems
  • Agile Information Management Governance—Can You Scale it to the Enterprise?
  • Challenges of Structure and Organization in Medium-Sized Content
  • A Case Study to Improve Data Vendor Selection
  • Strategies for Large-Scale Entity Resolution Based on Inverted Index Data Partitioning
  • A Dual-Database Trusted Broker System for Resolving, Protecting, and Utilizing Multi-Sourced Data
  • Business Intelligence Architecture in Support of Data Quality
  • The Value of Data Quality
  • Data Protection and BI—A Quality Perspective
  • Compilation of References