Executing Data Quality Projects: Ten Steps to Quality Data and Trusted InformationTM

Danette McGilvray

2008年9月 · Elsevier

關於這本電子書

Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her “Ten Steps approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach—in which she has trained Fortune 500 clients and hundreds of workshop attendees—applies to all types of data and to all types of organizations.

* Includes numerous templates, detailed examples, and practical advice for executing every step of the “Ten Steps approach.* Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices.* A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online.

關於作者

Danette McGilvray has devoted more than 25 years to helping people around the world enhance the value of the information assets on which their organizations depend. Focusing on bottom-line results, she helps them manage the quality of their most important data, so the resulting information can be trusted and used with confidence—a necessity in today’s data-dependent world. Her company, Granite Falls Consulting, excels in bridging the gap between an organization’s strategies, goals, issues, and opportunities and the practical steps necessary to ensure the “right-level quality of the data and information needed to provide products and services to their customers. They specialize in data quality management to support key business processes, such as analytics, supply chain management, and operational excellence. Communication, change management, and human factors are also emphasized because they affect the trust in and use of data and information. Granite Falls’ “teach-a-person-how-to-fish approach helps organizations meet their business objectives while enhancing skills and knowledge that can be used to benefit the organization for years to come. Client needs are met through a combination of consulting, training, one-on-one mentoring, and executive workshops, tailored to fit any situation where data is a component. Danette first shared her extensive experience in her 2008 book, Executing Data Quality Projects: Ten Steps to Quality Data and Trusted InformationTM (Morgan Kaufmann), which has become a classic in the data quality field. Her Ten StepsTM methodology is a structured yet flexible approach to creating, assessing, improving, and sustaining data quality. It can be applied to any type of organization (for profit, government, education, healthcare, non-profit, etc.), and regardless of country, culture, or language. Her book is used as a textbook in university graduate programs. The Chinese translation was the first data quality book available in that language. The 2021 second edition (Elsevier/Academic Press) updates how-to details, examples, and templates, while keeping the basic Ten Steps, which have held the test of time. With her holistic view of data and information quality, she truly believes that data quality can save the world. She hopes that this edition can help a new generation of data professionals, in addition to inspiring those who already care about or have been responsible for data and information over the years. You can reach Danette at [e-mail]. Connect with her on LinkedIn and follow her on Twitter at Danette_McG. To see how Granite Falls can help on your journey to quality data and trusted information, and for free downloads of key ideas and tem¬plates from the book, see www.gfalls.com.