This is the first book to be published on the topic of data quality exploration, analytics, and quantitative data cleaning—providing a sound technical grounding in the subject. It shows readers, through examples and practical case studies, how to apply statistics and data mining techniques to their own data quality issues; presents an overview of data quality analytics and techniques for data quality improvement; and gives an iterative framework for the detection, explanation, and quantitative cleaning of data quality problems and anomalies.

Preface

Contents

Introduction

Part I – Principles and Theoretical Background

Part II - Practices

Part III – Supplementary Materials