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Between the Spreadsheets de Susan Walsh

Between the Spreadsheets

Autor Susan Walsh
en Limba Engleză Hardback – 18 sep 2025
Everyone talks about data quality issues, but not the consequences. From the top to the bottom of an organisation, everyone should understand the impact of dirty data and how to spot it. Being an entirely revised new edition, this book will show you how.
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Specificații

ISBN-13: 9781783307852
ISBN-10: 1783307854
Pagini: 216
Dimensiuni: 159 x 238 x 18 mm
Greutate: 0.44 kg
Editura: Facet Publishing

Recenzii

"If you are teaching data science then all your students should be made aware of this book. When it comes to organizations. I can't see any reason for not making sure that anyone managing an Excel data base has a copy to refer to ... Excellent value for the price."
—Martin White, Informer
"I gained many practical tips for using a spreadsheet to clean data, and alternate ways of approaching classification while reading this book—there is hope for cleaner data!"
—Mary Silvia Whittaker, SLA Taxonomy
"The need for protection against the insidious effects of dirty data, which is broadly defined as anything incorrect, is the basis for the COAT metaphor. COAT stands for consistent, organized, accurate, and trustworthy. Throughout the book, Walsh argues that these four data qualities are interdependent and essential for a well-functioning business ... The real-life examples help solidify the importance of context, communication, and, of course, COATs when it comes to data work. While not written for librarians, Between the Spreadsheets is a good title for anyone whose job involves working with or presenting data. In the era of data-driven decision making, that encompasses a large percentage of the library profession."
—Journal of Electronic Resources Librarianship

Notă biografică

Susan Walsh is Founder and Managing Director of The Classification Guru, a specialist data classification, taxonomy customization and data cleansing consultancy. With over 13 years of experience in data, Susan is a world-renowned thought leader, data expert and speaker. She has been featured in the DataIQ 100 most influential people in data as well as winner of the 2022 & 2023 DataIQ Data Champion of the Year, a finalist for The Great British Businesswoman Awards, and Practitioner of the Year at the Big Data Awards. Susan has classified and cleaned data across a number of different sectors, countries, and languages for over 100 clients worldwide, and created and recently launched a self-service supplier normalization tool, Samification.

Cuprins

Introduction
  1. The Dangers of Dirty Data
  2. Supplier Normalisation
  3. What is a Taxonomy?
  4. Spend Data Classification
  5. Basic Data Cleansing
  6. Before and After: Real-Life Data Cleaning Case Studies
  7. The Myth Exposed: Data Cleaning and GenAI
  8. Other Methodologies
  9. The Dirty Data Maturity Model
  10. Data Horror Stories
Conclusion