Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (TM), 2nd Edition. Execute Data Quality Projects, Second Edition presents a structured yet flexible approach to creating, improving, maintaining and managing data and information quality in any organization.
Studies show that data quality issues cost businesses billions of dollars every year, with bad data associated with waste and inefficiency, compromised credibility with customers and suppliers, and organizational inability to make informed decisions. Help is here! This book describes a proven ten-step approach that combines a conceptual framework for understanding information quality with techniques, tools and instructions for putting the approach into practice - with the end result of reliable data and information. high quality products that are so important to today's world. data is - dependent organizations.
The ten-step approach applies to all types of data and all types of organizations - for-profit in any industry, non-profit, government, education, health, science, research and medicine . This book contains many templates, detailed examples, and practical tips for completing each step. At the same time, readers are encouraged to choose relevant steps and apply them in different ways to better cope with the many situations they face. The layout allows for quick reference with a user-friendly format that highlights key concepts and definitions, key control points, communication activities, best practices, and disclaimers. The actual customer and user experience of the Ten Steps provides real-life examples of the results of the steps, as well as the case studies highlighted in the sidebar titled Ten Steps in Action.
In this book, projects are used as a vehicle for data quality work and the term generally includes: 1) targeted projects to improve data quality, such as migrating data from existing systems, l '' integration of data due to mergers and acquisitions, or unraveling of data due to organizational fragmentation and 3) ad hoc use of data quality steps, techniques or activities as part of day-to-day work . The ten-step approach can also be used to enrich an organization's standard SDLC (whether sequential or agile), and it complements general improvement methods such as Six Sigma or Lean. No two data quality projects are the same, but the flexibility of the ten steps means that the methodology can be applied to all.
The new second edition highlights topics such as Artificial Intelligence and Machine Learning, Internet of Things, Data Security and Protection, Analytics, Legal and Regulatory Requirements, Data Science, big data, data lakes and cloud computing, and their reliance on data. and information on why data quality is more relevant and important now than ever.
If you and your company want to see data as more than just one of your most valuable assets, Danette makes it clear how to start treating data as it is and offers the most robust and resilient approach. His years of experience wraps this book with a practical, structured methodology and the guidance needed to help any business achieve the data quality it needs to succeed in the information age.
-Douglas B. Laney, Data and Analytics Strategist and Author of Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage.
The need for high quality data has never been so great! Managers have to run their organizations, employees have to do their jobs, and we all have to take care of our families. All the more difficult in view of a global pandemic and its consequences. Data could be our best and most powerful weapon. McGilvray's Ten Steps is a proven guide to addressing underlying issues. I've long been a huge fan of the first edition of Executing Data Quality Projects. The second edition has some great updates to help people and teams fix the issues that really matter.
-Tom Redman, the data document, data quality solutions.
Big books are not on your shelf, flawless and beautiful, without even a crease. The best books take up valuable, dog-eared, highlighted office space. According to this standard, Danette McGilvray's book Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information ™ is utterly devastated and never further removed. The power of the content and techniques she has brought together in one volume is evidence of the book itself: by applying the principles discussed in it, the author has compiled a collection of knowledge and tools to help readers every step of their data quality journey . It is not a book that you read once and put on the shelf, but a loyal companion that accompanies you in everyday life.
-Anthony J. Algmin, Founder, Algmin Data Leadership.
In my specialty, IT security, I haven't been exposed to the term “data quality” much. After learning about this, I am convinced that data quality is essential for IT security and that security professionals will only successfully defend systems if they integrate them into their practice. For starters, I recommend reading McGilvray's book Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information ™. I literally tell people that this book has changed my (professional) life. Not only did he do a great job conveying basic data quality concepts so that even a novice like me can understand, digest, and apply them, but the Ten Steps themselves, the very meat of the book, are surprisingly useful. The emphasis on practicality and contextualization creates a framework that can be used in almost any setting possible to improve the quality of an organization's data.
-Seth James Nielson, PhD, Founder and Senior Scientist, Crimson Vista, Inc.
There is nothing like learning from a practitioner. An architect can think about a design, draw a plan, and write a book about how his buildings meet human needs. But you can never hit a nail with a hammer. But if someone writes, not just from what he knows, but from what he has done, then you have something now. The second edition of Danette's data quality book fits this description. Not only did Danette write a great book on data quality in 2008, she learned more, made changes, moved on, and then decided to start over. The second edition is just as important and excellent as the first. This is required reading for a data practitioner and should be on your shelf - and used.
-John Ladley, Data Thought Leader and Practitioner, Advisor and Mentor to Business and Data Leaders.
I've known Ten Steps and Danette for 10 years. Over the decade, many data practitioners in China have applied the method to real data quality and governance projects and programs. Companies benefit from better data quality. The ten-step process itself has evolved, and I believe that more data, more people, and more organizations will get more value from thinking and learning.
If you and your business want to see data as more than one of your most valuable assets, Danette makes it clear how to start managing data and offers the most robust and resilient approach. Years of experience complement this book with the methodology and practical, structured guidance needed to help any business achieve the data quality it needs to be successful in the Information Age.
-Douglas B. Laney, data and analytics strategist and author of Infonomics: How to monetize, manage and measure information as an asset for competitive advantage.
The need for high quality data has never been greater! Managers have to run their organizations, employees have to do their jobs and we all have to take care of our families. All the more difficult in view of a global pandemic and its consequences. Data could be our best and most powerful weapon. McGilvray's Ten Steps are a proven guide to solving underlying issues. I have been a big fan of the first edition of Executing Data Quality Projects for a long time. The second edition contains great updates to help people and teams solve the issues that really matter.
-Tom Redman, The Data Document, The Data Quality Solutions.
Big books aren't on your shelf, crisp and beautiful, even without wrinkles. The best books take up valuable, showcased office space. According to this standard, Danette McGilvray's book Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information ™ is totally devastated and will never be deleted again. The power of content and techniques that she has brought together in one volume is proof of the book itself: by applying the principles discussed therein, the author has compiled a body of knowledge and tools to help readers every step of the way on their data quality journey. It is not a book that you read once and put on a shelf, but a faithful companion who accompanies you on a daily basis.
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