Between the Spreadsheets by Walsh, Susan;

Rövid leírás:

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.

Hosszú leírás:

‘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.

Tartalomjegyzék:

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