OCDQ Blog

This excellent book is required reading - and not just for information technology professionals.

Data and information are not just "the lifeblood of the Information Age", but must be treated as every company's most strategic asset, and should be managed as aggressively and professionally as other assets.

The current economic crisis is rife with examples of this unavoidable truth, including the 8 trillion dollar impact of the subprime mortgage meltdown, and the 50 billion dollar ponzi scheme by Bernie Madoff.

Simply reading this book will not magically fix anything. However, reading this book will help you understand the complexity and severity of not taking data and information quality seriously.

Also, this book contains practical advice and recommendations for moving forward. Therefore, read this book and learn from it, but most importantly, put its ideas into action in your organization!

David Loshin is an industry luminary within the fields of Data Quality, Master Data Management (MDM), and Business Intelligence. This book is a comprehensive resource that will prove immensely useful to anyone planning a MDM initiative.

Since the book's release, I have also had the privilege of attending numerous lectures and conference workshops delivered by David that draw upon the book's material and effortlessly relate it to the needs of diverse audiences.

Bottom-line: this book is an invaluable guide on MDM, and may very well be the only book that you truly NEED to purchase regarding this subject.

Click to View the Official Master Data Management Website

Although I have an extensive background in data quality, I had never formally used the term "data governance" with my clients. Therefore, I began researching what data governance is and how it specifically relates to data quality. Thankfully, I found a great resource in Steve Sarsfield's excellent book, which explained:

"Data governance is about changing the hearts and minds of your company to see the value of information quality...data governance is a set of processes that ensures that important data assets are formally managed throughout the enterprise...at the root of the problems with managing your data are data quality problems...data governance guarantees that data can be trusted...putting people in charge of fixing and preventing issues with data...to have fewer negative events as a result of poor data."

I highly recommend this book.

It will prove to be an invaluable resource, both for your organization and for your career.

Click to View the Data Governance and Data Quality Insider Blog by Steve Sarsfield

Data Quality Assessment is a must read for anyone who needs to understand, correct, or prevent data quality issues in their organization. Skipping theory and focusing purely on what is practical and what works, this text contains a proven approach to identifying, warehousing, and analyzing data errors - the first step in any data quality program.

Master techniques in:

• Data profiling and gathering meta data
• Identifying, designing, and implementing data quality rules
• Organizing rule and error catalogues
• Ensuring accuracy and completeness of the data quality assessment
• Constructing the dimensional data quality scorecard
• Executing a recurrent data quality assessment

Click to View data quality curriculum by Arkady Maydanchik at eLearningCurve

Whatever business you're in, you're ultimately in the customer business. No matter what your product, customers pay the bills. But the strategic importance of customer relationships hasn't brought companies much closer to a single, authoritative view of their customers.

Written from both the business and technical perspectives, Customer Data Integration shows companies how to deliver an accurate, holistic, and long-term understanding of their customers through CDI.

Click to View the Inside the Biz Blog with Jill Dyché

Click to View the Inside IT Blog with Evan Levy

This book helps practitioners gain a deeper understanding, at an applied level, of the issues involved in improving data quality through editing, imputation, and record linkage.

The first part of the book deals with methods and models.  Here, the focus is on the Fellegi-Holt edit-imputation model, the Little-Rubin multiple-imputation scheme, and the Fellegi-Sunter record linkage model.  Brief examples are included to show how these techniques work.

In the second part of the book, the authors present real-world case studies in which one or more of these techniques are used.  They cover a wide variety of application areas.  These include mortgage guarantee insurance, medical, biomedical, highway safety, and social insurance as well as the construction of list frames and administrative lists.

Readers will find this book a mixture of practical advice, mathematical rigor, management insight and philosophy.  The long list of references at the end of the book enables readers to delve more deeply into the subjects discussed.

As someone who has spent over 15 years in computer science disciplines applying technology in a business context in order to implement new systems for Global 500 companies in a wide variety of industries, I can confidently say that this book provides an honest portrayal of the many small but crucial aspects of system failures that too many people in our industry don't want to admit.

However, this book is far from a doom and gloom review of disastrous projects and failed system implementations. Instead, this book contains numerous examples and compelling case studies, which serve as a very practical guide for how to recognize, and more importantly, overcome the common mistakes that can prevent new systems from being successful.

Phil Simon writes about these complex challenges in a clear and comprehensive style that is easily approachable and applicable to diverse audiences - both academic and professional, as well as readers with either a business or a technical orientation.