Additional information
by Uma Gupta (Author), San Cannon (Author)
Data governance looks deceptively simple on paper. In reality, it is complex. And it is increasingly recognized as a key foundational element necessary to advance analytics and improve operations for organizations of all types across industry.
In this practical guide, data experts Uma Gupta and San Cannon look to demystify data governance through pragmatic advice based on real-world experience and cutting-edge academic research. Using case studies, the authors provide insight into how to address the myriad of data governance challenges facing organizations today, with useful and sensible actions and practices. The chapters focus on filling in the blanks of other data governance information, as well as providing both basic and forward-thinking suggestions for some of today's greatest challenges. It is peppered throughout with practical tips for data strategy, data literacy, and data quality.
If you are new to data governance, or a seasoned practitioner looking to understand how to better address new issues, this book can help guide you through core elements such as communication, culture, and change management.
Author Biography
Uma G. Gupta, Ph.D., is the Director of Business Analytics at the University of South Carolina at Upstate. Author of more than seventy papers, four textbooks in information technology, and editor of a book on testing expert systems, Uma is frequently a keynote speaker on the impact of technology on personal and organizational productivity. She is the Founder of a non-profit that helps school children and families in poverty.
Sandra "San" Cannon, Ph.D., is the first Chief Data Officer at the University of Rochester. She is responsible for the University's enterprise-wide data strategy, data policy and process development, and data support services. In addition, she teaches data science courses and conducts research on data organization and dissemination, data quality metrics and outcomes, research support processes, and applications of data science techniques. Prior to joining the University, San worked in data management and analytics in the Federal Reserve System for more than 20 years.
