Between the spreadsheets: classifying and fixing dirty data
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
London
Facet Publishing
2021
|
| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 TUM01 Volltext |
| Beschreibung: | 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. - Title from publisher's bibliographic system (viewed on 09 Nov 2021) |
| Beschreibung: | 1 Online-Ressource (xxii, 158 Seiten) |
| ISBN: | 9781783305049 9781783305230 |
| DOI: | 10.29085/9781783305049 |
Internformat
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| 500 | |a 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. - Title from publisher's bibliographic system (viewed on 09 Nov 2021) | ||
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Datensatz im Suchindex
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| author | Walsh, Susan ca. 20./21. Jh |
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| language | English |
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| spelling | Walsh, Susan ca. 20./21. Jh. Verfasser (DE-588)1248540093 aut Between the spreadsheets classifying and fixing dirty data Susan Walsh London Facet Publishing 2021 1 Online-Ressource (xxii, 158 Seiten) txt rdacontent c rdamedia cr rdacarrier 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. - Title from publisher's bibliographic system (viewed on 09 Nov 2021) Database management Databases / Quality control Database searching Electronic spreadsheets Erscheint auch als Druck-Ausgabe, Paperback 978-1-78330-503-2 https://doi.org/10.29085/9781783305049 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Walsh, Susan ca. 20./21. Jh Between the spreadsheets classifying and fixing dirty data Database management Databases / Quality control Database searching Electronic spreadsheets |
| title | Between the spreadsheets classifying and fixing dirty data |
| title_auth | Between the spreadsheets classifying and fixing dirty data |
| title_exact_search | Between the spreadsheets classifying and fixing dirty data |
| title_exact_search_txtP | Between the spreadsheets classifying and fixing dirty data |
| title_full | Between the spreadsheets classifying and fixing dirty data Susan Walsh |
| title_fullStr | Between the spreadsheets classifying and fixing dirty data Susan Walsh |
| title_full_unstemmed | Between the spreadsheets classifying and fixing dirty data Susan Walsh |
| title_short | Between the spreadsheets |
| title_sort | between the spreadsheets classifying and fixing dirty data |
| title_sub | classifying and fixing dirty data |
| topic | Database management Databases / Quality control Database searching Electronic spreadsheets |
| topic_facet | Database management Databases / Quality control Database searching Electronic spreadsheets |
| url | https://doi.org/10.29085/9781783305049 |
| work_keys_str_mv | AT walshsusan betweenthespreadsheetsclassifyingandfixingdirtydata |