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
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