Data Resource Quality

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Michael Brackett provides a wake-up call for information technology managers around the world. We can continue with our current practices, slowing down our ability to compete, or we can buckle down and focus on the basics of improving data quality. This book provides a look at the fundamentals of good data management practice. Use it well."-From the Foreword by Ron Shelby, Chief Information Officer, e-GMPoor data quality impacts every facet of today's private enterprises and public organizations. The deplorable condition of this critical resource in organizations around the world lowers productivity, impedes the creation of decision support systems (such as data warehousing), and hinders the development of e-commerce and other strategic initiatives. The future success of organizations will greatly depend on how well they design and maintain their data resources. Written by a world expert in data resources, Data Resource Quality features the ten most fundamental and frequently exhibited bad habits that contribute to poor data quality, and presents the strategies and best practices for effective solutions. With this information, IT managers will be better equipped to implement an organization-wide, integrated, subject-oriented data architecture and within that architecture build a high-quality data resource. The result: reduced data disparity and duplication, increased productivity, and improved data understanding and utilization. Covering both data architecture and data management issues, the book describes the impact of poor data practices, demonstrates more effective approaches, and reveals implementation pointers for quick results. Readers will find coverage of such vital data quality issues as: The need for formal data names and comprehensive data definitions Proper data structures, covering the entity-relation diagram and the combined three-tier and five-schema structure Precise data integrity rules Robust data documentation Reasonable data orientation, including business subject, business client, and single-architecture orientation Acceptable data availability issues, covering backup, recovery, and privacy Adequate data responsibility, discussing authorized stewardship, centralized control, and procedures Expanded data vision for improved business support More appropriate data recognition leading to better data targeting within the organization With these strategies for successful data resource development, IT managers will be able to set a proper course for an efficient and profitable long-term data resource solution. 0201713063B04062001