Oct 11, 2022 · In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some …

Oct 29, 2010 · Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. Includes an in-depth look at the use of data quality tools, including business case templates, and tools for …

In this book, Barr Moses, Lior Gavish, and Molly Vorwerck, from the data observability company Monte Carlo, explain how to tackle data quality and trust at scale by leveraging best practices and technologies used by some of the world's most innovative companies.

Feb 1, 2023 · 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.

Sep 18, 2020 · We compiled this directory of the best data quality books based on Amazon user reviews, rating, and ability to add business value.

O’Reilly’s Data Quality Fundamentals is the definitive guide and first book published by O’Reilly to educate the market on how best-in-class data teams design and architect technical systems to achieve trustworthy and reliable data at scale.

Each chapter explains a key element of data quality management, from linking strategy and data together to profiling and designing business rules which reveal bad data. The book outlines a suite of tried-and-tested reports that highlight bad data and allow you to develop a …

A guide for assessing an organization's data quality practice and a roadmap for implementing a viable data and information quality management program, based on rigorous research and drawing on real-world examples. All organizations today confront data quality problems, both systemic and structural.

Batini and Scannapieco present a comprehensive and systematic introduction to the wide set of issues related to data quality.

Data quality experts from research and industry agree that a unified framework for data quality management should bring together organizational, architectural and computational approaches.