Zum Hauptinhalt springen
Nicht aus der Schweiz? Besuchen Sie lehmanns.de
Between the Spreadsheets - Susan Walsh

Between the Spreadsheets

Classifying and Fixing Dirty Data

(Autor)

Buch | Softcover
216 Seiten
2025 | Second Edition
Facet Publishing (Verlag)
978-1-78330-784-5 (ISBN)
CHF 64,55 inkl. MwSt
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.
‘Clear, concise, engaging and entertaining. Highly recommended for anyone involved with data in any capacity.' Information Professional

Dirty data is a problem that costs businesses thousands, if not millions, every year. And with the increasing use of AI and Generative AI, it’s only getting worse. 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 best practices on how to fix it.

Fully revised and updated throughout, this new edition of Between the Spreadsheets draws on classification expert Susan Walsh’s years of hands-on experience in data 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 and taxonomies, and presents the author’s proven COAT framework, 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 as well as new advice on using GenAI and why it is so important to have clean data before using it.

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, 2nd Edition 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.

Susan Walsh is Founder and Managing Director of The Classification Guru, a specialist data classification, taxonomy customisation 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 normalisation tool, Samification.

Introduction



The Dangers of Dirty Data
Supplier Normalisation
Taxonomies
Spend Data Classification
Basic Data Cleansing
Before and After: Real-Life Data Cleaning Case Studies
The Myth Exposed: Data Cleaning and GenAI
Other Methodologies
The Dirty Data Maturity Model
Data Horror Stories

Summary

Erscheinungsdatum
Verlagsort London
Sprache englisch
Maße 156 x 234 mm
Themenwelt Informatik Datenbanken Data Warehouse / Data Mining
Mathematik / Informatik Informatik Office Programme
Sozialwissenschaften Kommunikation / Medien Buchhandel / Bibliothekswesen
Wirtschaft Betriebswirtschaft / Management Unternehmensführung / Management
ISBN-10 1-78330-784-6 / 1783307846
ISBN-13 978-1-78330-784-5 / 9781783307845
Zustand Neuware
Informationen gemäß Produktsicherheitsverordnung (GPSR)
Haben Sie eine Frage zum Produkt?
Mehr entdecken
aus dem Bereich
eine Einführung mit Python, Scikit-Learn und TensorFlow

von Oliver Zeigermann; Chi Nhan Nguyen

Buch | Softcover (2024)
O'Reilly (Verlag)
CHF 27,85
Von den Grundlagen bis zum Produktiveinsatz

von Anatoly Zelenin; Alexander Kropp

Buch (2025)
Hanser (Verlag)
CHF 69,95