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Between the Spreadsheets: Classifying and Fixing Dirty Data

Autor Susan Walsh
en Limba Engleză Paperback – 21 oct 2021
Dirty data is a problem that costs businesses thousands, if not millions, every year. In organisations large and small across the globe you will hear talk of data quality issues. What you will rarely hear about is the consequences or how to fix it. Between the Spreadsheets: Classifying and Fixing Dirty Data draws on classification expert Susan Walsh’s decade of experience in data classification to present a fool-proof method for cleaning and classifying your data. The book covers everything from the very basics of data classification to normalisation, taxonomies and presents the author’s proven COAT methodology, helping ensure an organisation’s data is Consistent, Organised, Accurate and Trustworthy. A series of data horror stories outlines what can go wrong in managing data, and if it does, how it can be fixed. After reading this book, regardless of your level of experience, not only will you be able to work with your data more efficiently, but you will also understand the impact the work you do with it has, and how it affects the rest of the organisation. Written in an engaging and highly practical manner, Between the Spreadsheets gives readers of all levels a deep understanding of the dangers of dirty data and the confidence and skills to work more efficiently and effectively with it. 
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Specificații

ISBN-13: 9781783305032
ISBN-10: 1783305037
Pagini: 184
Dimensiuni: 156 x 234 x 13 mm
Greutate: 0.29 kg
Ediția:1
Editura: American Library Association
Colecția Facet Publishing

Public țintă

Professional Practice & Development

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

Descriere

This book presents a fool-proof method for cleaning and classifying business data. Covering everything from data classification to normalisation, taxonomies and presenting the author’s proven COAT methodology, it helps organisations ensure their data is Consistent, Organised, Accurate and Trustworthy. A series of data horror stories outlines what can go wrong in managing data, and if it does, how it can be fixed.

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