Between the Spreadsheets: Classifying and Fixing Dirty Data

Length: 188 pages
Edition: 1
Language: English
Publisher: Facet Publishing
Publication Date: 2021-11-10
ISBN-10: 1783305037
ISBN-13: 9781783305032
Sales Rank: #557159 (See Top 100 Books)

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.

Cover
Praise for Between the Spreadsheets
Title Page
Copyright
Contents
Figures
Tables
Acknowledgements
Abbreviations
Introduction
1 The Dangers of Dirty Data
    What is dirty data?
    The consequences of dirty data
    How to ensure data accuracy
    How to maintain and spot-check your data
    Conclusion
2 Supplier Normalisation
    What is supplier normalisation?
    Normalisation best practice and rules
    Normalising suppliers in Excel
    Automating normalisation in Excel
    Conclusion
3 Taxonomies
    What is a taxonomy?
    Why do I need a taxonomy? Why not use GL codes?
    What is a good/bad taxonomy?
    Off-the-shelf versus custom
    How to build a spend taxonomy
    Conclusion
4 Spend Data Classification
    What is spend data classification?
    Classification best practice
    Classifying data in Excel
    Updating new data with existing classified data
    Conclusion
5 Basic Data Cleansing
    Cleansing personal data
    Cleansing names in Excel
    Cleansing addresses in Excel
    Conclusion
6 Other Methodologies
    Alternative tools
    Omniscope
    Artificial intelligence (AI), automation and machine learning (ML)
    Data cleansing tools
    Conclusion
7 The Dirty Data Maturity Model
    The dirty data maturity model
    Dirty data
    Declassed data
    Distributed data
    Disordered data
    Dirt-free data
    Conclusion
8 Data Horror Stories
    Scenario: Edinburgh children’s hospital
    Scenario: Ted Baker
    Stories of the common data people
    Final thoughts
Summary
    Dirty data
    COAT
    Normalisation
    Taxonomies
    Data classification
    Data cleansing
    Data tools
    Data maintenance
    And, of course, the horror stories
References
Index