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Between the Spreadsheets: Classifying and Fixing Dirty Data [Pehme köide]

  • Formaat: Paperback / softback, 188 pages, kõrgus x laius: 234x156 mm
  • Ilmumisaeg: 23-Sep-2021
  • Kirjastus: Facet Publishing
  • ISBN-10: 1783305037
  • ISBN-13: 9781783305032
  • Formaat: Paperback / softback, 188 pages, kõrgus x laius: 234x156 mm
  • Ilmumisaeg: 23-Sep-2021
  • Kirjastus: Facet Publishing
  • ISBN-10: 1783305037
  • ISBN-13: 9781783305032
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 Walshs 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 and taxonomies, and presents the authors proven COAT methodology, helping ensure an organisations 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.

Arvustused

'If you are teaching data science then all your students should be made aware of this book. When it comes to organisations. I cant see any reason for not making sure that anyone managing an Excel data base has a copy to refer to.... Excellent value for the price' -Martin White, Informer

'I gained many practical tips for using a spreadsheet to clean data, and alternate ways of approaching classification while reading this book - there is hope for cleaner data!' - Mary Silvia Whittaker, SLA Taxonomy 'I have rarely found such a brilliant argument about the importance of COAT - the overall approach to the management of data. The author approaches all her topics with palpable humour and presents them in lively and attractive style. A relevant acquisition for business information departments or their equivalents in public libraries as much as putting it on the desks of the people dealing with all kinds of business data.'

Elena Maceviciute, Swedish School of Library and Information Science

Figures
ix
Tables
xi
Acknowledgements xiii
Abbreviations xv
Introduction xvii
1 The Dangers of Dirty Data
1(36)
What is dirty data?
1(4)
The consequences of dirty data
5(6)
How to ensure data accuracy
11(11)
How to maintain and spot-check your data
22(12)
Conclusion
34(3)
2 Supplier Normalisation
37(24)
What is supplier normalisation?
37(5)
Normalisation best practice and rules
42(3)
Normalising suppliers in Excel
45(12)
Automating normalisation in Excel
57(2)
Conclusion
59(2)
3 Taxonomies
61(8)
What is a taxonomy?
61(1)
Why do I need a taxonomy? Why not use GL codes?
62(1)
What is a good/bad taxonomy?
63(3)
Off-the-shelf versus custom
66(1)
How to build a spend taxonomy
67(1)
Conclusion
68(1)
4 Spend Data Classification
69(30)
What is spend data classification?
69(1)
Classification best practice
70(5)
Classifying data in Excel
75(14)
Updating new data with existing classified data
89(8)
Conclusion
97(2)
5 Basic Data Cleansing
99(12)
Cleansing personal data
99(1)
Cleansing names in Excel
99(5)
Cleansing addresses in Excel
104(5)
Conclusion
109(2)
6 Other Methodologies
111(20)
Alternative tools
111(1)
Omniscope
111(15)
Artificial intelligence (AI), automation and machine learning (ML)
126(3)
Data cleansing tools
129(1)
Conclusion
129(2)
7 The Dirty Data Maturity Model
131(8)
The dirty data maturity model
131(1)
Dirty data
131(2)
Declassed data
133(1)
Distributed data
134(1)
Disordered data
135(2)
Dirt-free data
137(1)
Conclusion
138(1)
8 Data Horror Stories
139(16)
Scenario: Edinburgh children's hospital
139(1)
Scenario: Ted Baker
140(1)
Stories of the common data people
141(8)
Final thoughts
149(2)
Summary
151(1)
Dirty data
151(1)
COAT
151(1)
Normalisation
152(1)
Taxonomies
152(1)
Data classification
152(1)
Data cleansing
152(1)
Data tools
153(1)
Data maintenance
153(1)
And, of course, the horror stories
153(2)
References 155(2)
Index 157
Susan Walsh is Founder and Managing Director of The Classification Guru, a specialist data classification, taxonomy customisation and data cleansing consultancy. Susans 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.