Data resource quality

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-GM

Poor 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.