Between the Spreadsheets: Classifying and Fixing Dirty Data
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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Between the Spreadsheets: Classifying and Fixing Dirty Data
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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Between the Spreadsheets: Classifying and Fixing Dirty Data

Between the Spreadsheets: Classifying and Fixing Dirty Data

by Susan Walsh
Between the Spreadsheets: Classifying and Fixing Dirty Data

Between the Spreadsheets: Classifying and Fixing Dirty Data

by Susan Walsh

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Overview

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. 

Product Details

ISBN-13: 9781783305032
Publisher: American Library Association
Publication date: 10/21/2021
Pages: 184
Product dimensions: 6.14(w) x 9.21(h) x 0.50(d)

About the Author

Susan Walsh is Founder and Managing Director of The Classification Guru, a specialist data classification, taxonomy customisation and data cleansing consultancy. Susan’s work provides clarity and accuracy to data to help businesses work more effectively, find cost savings through spend and time management, support better and more informed business decisions and deliver strong ROI. Susan has classified data across a number of different sectors, countries and languages, as well as managing and training teams to do the same. She is author of numerous articles on data issues and is a 2021 TEDx speaker and British Data Awards finalist.

Table of Contents

Figures ix

Tables xi

Acknowledgements xiii

Abbreviations xv

Introduction xvii

1 The Dangers of Dirty Data 1

What is dirty data? 1

The consequences of dirty data 5

How to ensure data accuracy 11

How to maintain and spot-check your data 22

Conclusion 34

2 Supplier Normalisation 37

What is supplier normalisation? 37

Normalisation best practice and rules 42

Normalising suppliers in Excel 45

Automating normalisation in Excel 57

Conclusion 59

3 Taxonomies 61

What is a taxonomy? 61

Why do I need a taxonomy? Why not use GL codes? 62

What is a good/bad taxonomy? 63

Off-the-shelf versus custom 66

How to build a spend taxonomy 67

Conclusion 68

4 Spend Data Classification 69

What is spend data classification? 69

Classification best practice 70

Classifying data in Excel 75

Updating new data with existing classified data 89

Conclusion 97

5 Basic Data Cleansing 99

Cleansing personal data 99

Cleansing names in Excel 99

Cleansing addresses in Excel 104

Conclusion 109

6 Other Methodologies 111

Alternative tools 111

Omniscope 111

Artificial intelligence (AI), automation and machine learning (ML) 126

Data cleansing tools 129

Conclusion 129

7 The Dirty Data Maturity Model 131

The dirty data maturity model 131

Dirty data 131

Declassed data 133

Distributed data 134

Disordered data 135

Dirt-free data 137

Conclusion 138

8 Data Horror Stories 139

Scenario: Edinburgh children's hospital 139

Scenario: Ted Baker 140

Stories of the common data people 141

Final thoughts 149

Summary 151

Dirty data 151

COAT 151

Normalisation 152

Taxonomies 152

Data classification 152

Data cleansing 152

Data tools 153

Data maintenance 153

And, of course, the horror stories 153

References 155

Index 157

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