Danette McGilvray

Executing Data Quality Projects (eBook, ePUB)

Ten Steps to Quality Data and Trusted InformationTM

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: Elsevier Science & Techn.
• Seitenzahl: 352
• Erscheinungstermin: 1. September 2008
• Englisch
• ISBN-13: 9780080558394
• Artikelnr.: 38124243

Autorenporträt

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 Information(TM) (Morgan Kaufmann), which has become a classic in the data quality field. Her Ten Steps(TM) 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 gfalls.com.

Inhaltsangabe

1. Overview2. Key Concepts3. The Ten Steps4. Structuring Your Project5. Other Techniques and Tools6. A Few Final Words

Rezensionen

"My esteemed colleague describes a practical approach for planning and managing information quality. I recommend you read, understand, and apply the learnings found here." --Larry P. English, President and Principal, Information Impact International, creator of the TIQM Quality System. Conceiver and co-Founder of the International Association for Information and Data Quality"In a subject that is long on talk and short on practical advice for implementation, Danette McGilvray is a refreshing exception. If you want to know HOW to execute data quality projects, read this book -- everything you need to know is in here." --David Plotkin, Data Quality Manager, California State Automobile Association"This book is a gem. Tested, validated and polished over a distinguished career as a practitioner and consultant, Danette's Ten Steps methodology shines as a unique and much needed contribution to the information quality discipline. This practical and insightful book will quickly become the reference of choice for all those leading or participating in information quality improvement projects. Experienced project managers will use it to update and deepen their knowledge, new ones will use it as a roadmap to quickly become effective. Managers in organizations that have embraced generic improvement methodologies such as six sigma, lean or have developed internal ones would be wise to hand this book to their Black Belts and other improvement leaders." --C. Lwanga Yonke, Information Quality Practitioner."Danette s book takes a pragmatic and practical approach to achieving the desired state of data quality within an organization. It is a "must-read" for any organization starting out on the road to data quality." --Susan Stewart Goubeaux, Director, Business Intelligence, FHLBanks Office of Finance"Data quality" has become one of those hackneyed phrases in our industry that everyone supports, but only a few organizations have achieved to the degree they need to move forward in their industries. What is required is a guide to explain to the business people who want better data just how to get it. This book is just such a guide. While the individual steps should not be a great surprise, her organization makes them immediately actionable to a degree previous books have not. In short, this is definitely required reading for anyone embarking on a data quality project." --David Hay, President, Essential Strategies"Danette has taken what has previously been presented in the abstract and made an excellent, concrete guide toward improving data quality." --John Ladley, President of IMCue Solutions"Using this methodology, you will never lose your way on your data quality project! This book is peppered with tips, guidelines, templates, cross-references, and call-out icons. Plus, there are many easy-to-follow examples for the most common types of data quality projects." --Larissa T. Moss, President, Method Focus Inc."This book presents a valuable reference for not just data professionals, but also project managers and business representatives interested in or responsible for establishing, maintaining, and/or improving data and information quality. What sets this book apart from others in the field is the business impact-driven approach to assessing and improving data quality, and the specific steps and techniques it provides every step of the way." --Mehmet Orun, Senior Manager / Principal Architect, Data Services CoE, Fortune 250 Company"Comprehensive" is the first word I would use to describe this book. It addresses so many nuances of every aspect of data quality assessment and improvement--things that would go unmentioned by more superficial treatments. Bravo!" --Michael Scofield, Manager, Data Asset Development, ESRI, Inc.