This is the first book to be published on the topic of data quality exploration, analytics and quantitative data cleaning. The author provides a sound technical grounding in the subject and shows readers, through examples and practical case studies, how to apply statistics and data mining techniques to their own data quality issues.
An overview of data quality analytics and techniques for data quality improvement is provided, and the author also present an iterative framework for the detection, explanation and quantitative cleaning of data quality problems and anomalies. The book then goes on to describe the methods for data quality measuring, monitoring and improvement and explains how readers can identify the best strategies for cleaning their data and for automating the process of data quality exploration and remediation.
Table of Contents
Preface
Contents
Introduction
Part I Principles and Theoretical Background
Part II – Practices
Part III Supplementary Materials
