PRAISE FOR DATA QUALITY

"Incredible! Get ready to be inspired, motivated, and to accelerate on improving your data quality landscape! Regardless of your industry, experience, or role, this book provides the invaluable guidance and framework to start tackling the most critical, common roadblocks encountered with data quality. Spanning the entire data lifecycle, the author has provided trusted, foundational tools with approaches for understandable, everyday business scenarios to demonstrate how organizations can realistically improve their data quality. Incorporating both business performance considerations and technical fundamental best practices together, this timely book is for anyone passionate about truly unlocking the value of their data, engaging and guiding both technical and nontechnical business leaders alike on a critical topic that we should all be passionate about."
--Alena Godin, Chief Data and Analytics Officer, Best Buy, Canada

"You may have the best people, processes, technology, analytics, and AI practice in your organization. But if the underlying data that is used is poor in terms of quality, the outcome achieved will be poor. The quality of data driven business decision making/value creation is dependent on the quality of data used. I have been a data quality practitioner for over 30 years. Undoubtedly, this is one of the best books on data quality I have ever read. It provides comprehensive coverage of the end-to-end data quality lifecycle. What is impressive about this book is the language used is simple and understandable by anyone at all levels in an organization and without any technology jargon or tools. A definite book for those who want to be literate on data quality and for those who want to develop and execute a successful data quality program in their organization to derive business benefits."
--Ram Kumar, Chief Data and Analytics Officer, Cigna International Markets, Singapore

"In his impeccably well-researched book, Data Quality: Empowering Businesses with Analytics and AI, Prashanth Southekal covers just about all the bases--tying data quality to business outcomes, and laying out the many dimensions of data quality, where data quality fits into the overall data and business lifecycle, and a variety of techniques and best practices for identifying and mitigating data quality issues. We'll be seeing this book regularly on the desks of business and data executives and practitioners."
--Douglas Laney, Innovation Fellow, Data and Analytics Strategy, West Monroe, USA

"The problem is known: leveraging the value of available data ... but how? Those promising great insights from analytics often start where this book ends: good data quality. Prashanth Southekal takes up the challenge of addressing what is needed to achieve this. Ensuring good data quality at the source is the only viable approach, knowing that fixing bad data (if possible, at all) is way more expensive and cumbersome. Introducing the Define-Assess-Realize-Sustain Model, the book offers the reader a very structured approach in finding the right, fit-to-purpose, level of data quality and sustaining it. It achieves this by describing the main root causes for poor data quality, offering techniques to identify them, and sharing best practices in data capturing and organization around data governance. This book is a valuable read for every organization that is willing to make the effort to unleash the endless opportunities good data quality entails."
--Astrid von Perbandt, Vice President Group Controlling, LPKF Laser & Electronics AG, Germany

The data economy is increasingly embraced worldwide in every industry as data-driven organizations have demonstrated improved business performance. Every company today is looking at leveraging data, analytics, and AI (Artificial Intelligence) for improved business performance. But most organizations are impaired with poor data quality that is adversely affecting their analytics and AI solutions. In fact, recent studies have found that just 3 percent of the data in an enterprise meets quality standards, 27 percent of data in the companies is flawed, and poor data quality impacts up to 12 percent of revenues.

To provide organizations a competitive advantage from data, Data Quality: Empowering Businesses with Analytics and AI provides readers with practical guidance and proven solutions to derive quality business data. The book examines the four phase D-A-R-S approach to data quality: Define-Assess-Realize-Sustain. Throughout the process, you'll learn to accelerate business results with high-quality data, discover the best data management and data governance practices, avoid common pitfalls, and meet the desired standards of data quality. Specifically, the author describes the 16 most common root causes of data quality degradation and offers 10 practical data management and data governance implementation patterns for remediating and sustaining data quality to drive exceptional business results. Ultimately, you will confirm that your firm's data is capable of powering high-level analytics and AI applications without leading you down blind alleys or dead ends.

An essential roadmap for data analysts, data governance professionals, data scientists, and other decision makers with a stake in deriving new and interesting intelligence from a firm's raw data, Data Quality will earn a place in the libraries of executives, founders, entrepreneurs, and other professionals who seek to ensure that the insights they're drawing from their data are valid and reliable.

Discover how to achieve business goals by relying on high-quality, robust data

In Data Quality: Empowering Businesses with Analytics and AI, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you'll learn techniques to define and assess data quality, discover how to ensure that your firm's data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications.

The author shows you how to:

• Profile for data quality, including the appropriate techniques, criteria, and KPIs
• Identify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization.
• Formulate the reference architecture for data quality, including practical design patterns for remediating data quality
• Implement the 10 best data quality practices and the required capabilities for improving operations, compliance, and decision-making capabilities in the business

An essential resource for data scientists, data analysts, business intelligence professionals, chief technology and data officers, and anyone else with a stake in collecting and using high-quality data, Data Quality: Empowering Businesses with Analytics and AI will also earn a place on the bookshelves of business leaders interested in learning more about what sets robust data apart from the rest.