Synopses & Reviews
Customers are the heart of any business, and corporations are increasing the amount of customer data they collect at 40% per year. However, just because we have the data doesn't mean we know how to use it. If misinterpreted, large volumes of data can actually encourage false conclusions that result in bad decisions and lost opportunities.
For example, customers often transact business through different sales channels, departments, or branches of a corporation. The records of these transactions are not always merged, often remaining in separate databases. To management, it appears that all of these records are for different customers. The result is that the business is not able to realize the full value of customers because of a poor relationship (e.g. long-time customer treated as a new customer), lost cross-sell and up-sell opportunities, and operational inefficiencies (e.g. incorrect reports, multiple mailings to the same person). These kinds of errors lead to steep financial losses.
Entity Resolution and Information Quality teaches you the process of locating and linking together information about the same person, place, or thing - eliminating duplications - and making crucial business decisions based on the results. It serves as an authoritative, vendor-independent technical reference for practitioners: architects, technical analysts, and solution developers. In short this book gives you the know-how at an applied level to aggregate data from disparate sources and form those accurate customer profiles the useful for marketing and sales stakeholders. It is an invaluable guide for succeeding in today's info-centric society.
* Has a strong industry perspective with lots of practical system design advice that creates a competitive advantage for organizations.
* Written by a recognized expert on data integration who acts as Co-Director of the MIT Information Quality Program's Working Group.
* Includes a companion site that houses synthetic customer data for applicatory exercises, and access to a Java-based Entity Resolution program.
Review
"This book is comprehensive, timely, and on the leading edge of the topic. In addition to being comprehensive and systematic, the book has two distinct characteristics: (1) it addresses the issue of entity relationships, which go beyond entity matching. This novel approach generates much richer information about entities; (2) it discusses not only techniques, but also systems that implement the techniques. This system-oriented approach helps the reader to see how to apply the techniques for problem solving."
-Dr. Hongwei (Harry) Zhu - Assistant Professor of Information Technology in the College of Business and Public Administration, Old Dominion University
Synopsis
Entity Resolution and Information Quality presents topics and definitions, and clarifies confusing terminologies regarding entity resolution and information quality. It takes a very wide view of IQ, including its six-domain framework and the skills formed by the International Association for Information and Data Quality {IAIDQ).
The book includes chapters that cover the principles of entity resolution and the principles of Information Quality, in addition to their concepts and terminology. It also discusses the Fellegi-Sunter theory of record linkage, the Stanford Entity Resolution Framework, and the Algebraic Model for Entity Resolution, which are the major theoretical models that support Entity Resolution. In relation to this, the book briefly discusses entity-based data integration (EBDI) and its model, which serve as an extension of the Algebraic Model for Entity Resolution. There is also an explanation of how the three commercial ER systems operate and a description of the non-commercial open-source system known as OYSTER. The book concludes by discussing trends in entity resolution research and practice. Students taking IT courses and IT professionals will find this book invaluable.
- First authoritative reference explaining entity resolution and how to use it effectively
- Provides practical system design advice to help you get a competitive advantage
- Includes a companion site with synthetic customer data for applicatory exercises, and access to a Java-based Entity Resolution program.
Synopsis
Customers and products are the heart of any business, and corporations collect more data about them every year. However, just because you have data doesn’t mean you can use it effectively. If not properly integrated, data can actually encourage false conclusions that result in bad decisions and lost opportunities. Entity Resolution (ER) is a powerful tool for transforming data into accurate, value-added information. Using entity resolution methods and techniques, you can identify equivalent records from multiple sources corresponding to the same real-world person, place, or thing.
This emerging area of data management is clearly explained throughout the book. It teaches you the process of locating and linking information about the same entity - eliminating duplications - and making crucial business decisions based on the results. This book is an authoritative, vendor-independent technical reference for researchers, graduate students and practitioners, including architects, technical analysts, and solution developers. In short, Entity Resolution and Information Quality gives you the applied level know-how you need to aggregate data from disparate sources and form accurate customer and product profiles that support effective marketing and sales. It is an invaluable guide for succeeding in today’s info-centric environment.
- First authoritative reference explaining entity resolution and how to use it effectively
- Provides practical system design advice to help you get a competitive advantage
- Includes a companion site with synthetic customer data for applicatory exercises, and access to a Java-based Entity Resolution program.
Synopsis
Learn how to integrate and use your customer and product information data to stay ahead of your competition!
Table of Contents
Chapter 1 Principles of Entity Resolution Chapter 2 Principles of Information Quality Chapter 3 Entity Resolution Models Chapter 4 Entity-Based Data Integration Chapter 5 Entity Resolution Systems Chapter 6 The OYSTER Project Chapter 7 Trends in Entity Resolution Research and Applications