Hands-On Entity Resolution

Entity resolution is a crucial analytic technique that identifies multiple data records that refer to the same real-world entity. This book provides practical understanding and techniques to scale up data matching processes and improve the accuracy of reconciliations, using real-world data examples. It covers challenges in deduplicating and joining datasets, extracting, cleansing, and preparing datasets, text matching algorithms, techniques for deduplicating and joining datasets at scale, matching datasets containing persons and organizations, evaluating data matches, optimizing and tuning data matching algorithms, and entity resolution using cloud APIs.

Format: Paperback / softback
Length: 200 pages
Publication date: 13 February 2024
Publisher: O'Reilly Media

Entity resolution is a crucial analytical technique that empowers users to identify multiple data records that correspond to the same real-world entity. This practical guide is designed for product managers, data analysts, and data scientists, offering valuable insights on how to enhance data value through cleansing, analysis, and resolution. Author Michael Shearer guides readers in scaling up their data matching processes and improving the accuracy of reconciliations. By leveraging open-source Python libraries and cloud APIs, individuals will learn how to remove duplicate entries within a single source and join disparate data sources when common keys are unavailable. Through real-world data examples, this book fosters a practical understanding to expedite the delivery of real business value.

Entity resolution plays a pivotal role in building rich and comprehensive data assets that unveil relationships for marketing and risk management purposes, essential for maximizing the potential of machine learning (ML) and artificial intelligence (AI). This book addresses various challenges associated with deduplicating and joining datasets, including extracting, cleansing, and preparing datasets for matching. It explores text matching algorithms to identify equivalent entities and techniques for deduplicating and joining datasets at scale, encompassing datasets containing persons and organizations. The book also delves into evaluating data matches, optimizing and tuning data matching algorithms, and leveraging cloud APIs for entity resolution. Additionally, it discusses matching using privacy-enhancing technologies, ensuring data security and compliance. By mastering entity resolution, users can unlock the full potential of their data and drive meaningful business outcomes.

Weight: 354g
Dimension: 175 x 234 x 15 (mm)
ISBN-13: 9781098148485