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 organizations 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 normalization and taxonomies, and presents the author's proven COAT framework, helping ensure an organization's data is Consistent, Organized, 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 organization. Written in an engaging and highly practical manner, Between the Spreadsheets, Second 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.
This is an essential read for anyone working with data who is looking to have cleaner and more accurate data in order to improve efficiency.
Introduction
The Dangers of Dirty Data
Supplier Normalisation
What is a Taxonomy?
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
Conclusion
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.
