Data quality: empowering businesses with analytics and AI
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Newark
Wiley
[2023]
|
| Schlagworte: | |
| Online-Zugang: | HWR01 UEI03 |
| Beschreibung: | 1 Online-Ressource (xxvi, 271 Seiten) Illustrationen |
| ISBN: | 9781394165247 |
Internformat
MARC
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| 505 | 8 | |a Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Acknowledgments -- Part I Define Phase -- Chapter 1 Introduction -- Introduction -- Data, Analytics, AI, and Business Performance -- Data as a Business Asset or Liability -- Data Governance, Data Management, and Data Quality -- Leadership Commitment to Data Quality -- Key Takeaways -- Conclusion -- References -- Chapter 2 Business Data -- Introduction -- Data in Business -- Telemetry Data -- Purpose of Data in Business -- Business Data Views -- Key Characteristics of Business Data -- Critical Data Elements (CDEs) -- Key Takeaways -- Conclusion -- References -- Chapter 3 Data Quality in Business -- Introduction -- Data Quality Dimensions -- Context in Data Quality -- Consequences and Costs of Poor Data Quality -- Data Depreciation and Its Factors -- Data in IT Systems -- Data Quality and Trusted Information -- Key Takeaways -- Conclusion -- References -- Part II Analyze Phase -- Chapter 4 Causes for Poor Data Quality -- Introduction -- Data Quality RCA Techniques -- Typical Causes of Poor Data Quality -- Key Takeaways -- Conclusion -- References -- Chapter 5 Data Lifecycle and Lineage -- Introduction -- Business-Enabled DLC Stages -- IT Business-Enabled DLC Stages -- Data Lineage -- Key Takeaways -- Conclusion -- References -- Chapter 6 Profiling for Data Quality -- Introduction -- Criteria for Data Profiling -- Data Profiling Techniques for Measures of Centrality -- Data Profiling Techniques for Measures of Variation -- Integrating Centrality and Variation KPIs -- Key Takeaways -- Conclusion -- References -- Part III Realize Phase -- Chapter 7 Reference Architecture for Data Quality -- Introduction -- Options to Remediate Data Quality -- DataOps -- Data Product -- Data Fabric and Data Mesh -- Data Enrichment -- Key Takeaways -- Conclusion -- References | |
| 505 | 8 | |a Chapter 8 Best Practices to Realize Data Quality -- Introduction -- Overview of Best Practices -- BP 1: Identify the Business KPIs and the Ownership of These KPIs and the Pertinent Data -- BP 2: Build and Improve the Data Culture and Literacy in the Organization -- BP 3: Define the Current and Desired State of Data Quality -- BP 4: Follow the Minimalistic Approach to Data Capture -- BP 5: Select and Define the Data Attributes for Data Quality -- BP 6: Capture and Manage Critical Data with Data Standards in MDM Systems -- Key Takeaways -- Conclusion -- References -- Chapter 9 Best Practices to Realize Data Quality -- Introduction -- BP 7: Rationalize and Automate the Integration of Critical Data Elements -- BP 8: Define the SoR and Securely Capture Transactional Data in the SoR/OLTP System -- BP 9: Build and Manage Robust Data Integration Capabilities -- BP 10: Distribute Data Sourcing and Insight Consumption -- Key Takeaways -- Conclusion -- References -- Part IV Sustain Phase -- Chapter 10 Data Governance -- Introduction -- Data Governance Principles -- Data Governance Design Components -- Implementing the Data Governance Program -- Data Observability -- Data Compliance - ISO 27001, SOC1, and SOC2 -- Key Takeaways -- Conclusion -- References -- Chapter 11 Protecting Data -- Introduction -- Data Classification -- Data Safety -- Data Security -- Key Takeaways -- Conclusion -- References -- Chapter 12 Data Ethics -- Introduction -- Data Ethics -- Importance of Data Ethics -- Principles of Data Ethics -- Model Drift in Data Ethics -- Data Privacy -- Managing Data Ethically -- Key Takeaways -- Conclusion -- References -- Appendix 1: Abbreviations and Acronyms -- Appendix 2: Glossary -- Appendix 3: Data Literacy Competencies -- About the Author -- Index -- EULA. | |
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Datensatz im Suchindex
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| author | Southekal, Prashanth H. |
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| contents | Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Acknowledgments -- Part I Define Phase -- Chapter 1 Introduction -- Introduction -- Data, Analytics, AI, and Business Performance -- Data as a Business Asset or Liability -- Data Governance, Data Management, and Data Quality -- Leadership Commitment to Data Quality -- Key Takeaways -- Conclusion -- References -- Chapter 2 Business Data -- Introduction -- Data in Business -- Telemetry Data -- Purpose of Data in Business -- Business Data Views -- Key Characteristics of Business Data -- Critical Data Elements (CDEs) -- Key Takeaways -- Conclusion -- References -- Chapter 3 Data Quality in Business -- Introduction -- Data Quality Dimensions -- Context in Data Quality -- Consequences and Costs of Poor Data Quality -- Data Depreciation and Its Factors -- Data in IT Systems -- Data Quality and Trusted Information -- Key Takeaways -- Conclusion -- References -- Part II Analyze Phase -- Chapter 4 Causes for Poor Data Quality -- Introduction -- Data Quality RCA Techniques -- Typical Causes of Poor Data Quality -- Key Takeaways -- Conclusion -- References -- Chapter 5 Data Lifecycle and Lineage -- Introduction -- Business-Enabled DLC Stages -- IT Business-Enabled DLC Stages -- Data Lineage -- Key Takeaways -- Conclusion -- References -- Chapter 6 Profiling for Data Quality -- Introduction -- Criteria for Data Profiling -- Data Profiling Techniques for Measures of Centrality -- Data Profiling Techniques for Measures of Variation -- Integrating Centrality and Variation KPIs -- Key Takeaways -- Conclusion -- References -- Part III Realize Phase -- Chapter 7 Reference Architecture for Data Quality -- Introduction -- Options to Remediate Data Quality -- DataOps -- Data Product -- Data Fabric and Data Mesh -- Data Enrichment -- Key Takeaways -- Conclusion -- References Chapter 8 Best Practices to Realize Data Quality -- Introduction -- Overview of Best Practices -- BP 1: Identify the Business KPIs and the Ownership of These KPIs and the Pertinent Data -- BP 2: Build and Improve the Data Culture and Literacy in the Organization -- BP 3: Define the Current and Desired State of Data Quality -- BP 4: Follow the Minimalistic Approach to Data Capture -- BP 5: Select and Define the Data Attributes for Data Quality -- BP 6: Capture and Manage Critical Data with Data Standards in MDM Systems -- Key Takeaways -- Conclusion -- References -- Chapter 9 Best Practices to Realize Data Quality -- Introduction -- BP 7: Rationalize and Automate the Integration of Critical Data Elements -- BP 8: Define the SoR and Securely Capture Transactional Data in the SoR/OLTP System -- BP 9: Build and Manage Robust Data Integration Capabilities -- BP 10: Distribute Data Sourcing and Insight Consumption -- Key Takeaways -- Conclusion -- References -- Part IV Sustain Phase -- Chapter 10 Data Governance -- Introduction -- Data Governance Principles -- Data Governance Design Components -- Implementing the Data Governance Program -- Data Observability -- Data Compliance - ISO 27001, SOC1, and SOC2 -- Key Takeaways -- Conclusion -- References -- Chapter 11 Protecting Data -- Introduction -- Data Classification -- Data Safety -- Data Security -- Key Takeaways -- Conclusion -- References -- Chapter 12 Data Ethics -- Introduction -- Data Ethics -- Importance of Data Ethics -- Principles of Data Ethics -- Model Drift in Data Ethics -- Data Privacy -- Managing Data Ethically -- Key Takeaways -- Conclusion -- References -- Appendix 1: Abbreviations and Acronyms -- Appendix 2: Glossary -- Appendix 3: Data Literacy Competencies -- About the Author -- Index -- EULA. |
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| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Electronic eBook |
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| id | DE-604.BV048831589 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:35:29Z |
| indexdate | 2024-07-10T09:47:13Z |
| institution | BVB |
| isbn | 9781394165247 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034097167 |
| oclc_num | 1371326660 |
| open_access_boolean | |
| owner | DE-2070s DE-945 |
| owner_facet | DE-2070s DE-945 |
| physical | 1 Online-Ressource (xxvi, 271 Seiten) Illustrationen |
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| publishDate | 2023 |
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| publisher | Wiley |
| record_format | marc |
| spelling | Southekal, Prashanth H. Verfasser (DE-588)1148770135 aut Data quality empowering businesses with analytics and AI Prashanth H. Southekal Newark Wiley [2023] © 2023 1 Online-Ressource (xxvi, 271 Seiten) Illustrationen txt rdacontent c rdamedia cr rdacarrier Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Acknowledgments -- Part I Define Phase -- Chapter 1 Introduction -- Introduction -- Data, Analytics, AI, and Business Performance -- Data as a Business Asset or Liability -- Data Governance, Data Management, and Data Quality -- Leadership Commitment to Data Quality -- Key Takeaways -- Conclusion -- References -- Chapter 2 Business Data -- Introduction -- Data in Business -- Telemetry Data -- Purpose of Data in Business -- Business Data Views -- Key Characteristics of Business Data -- Critical Data Elements (CDEs) -- Key Takeaways -- Conclusion -- References -- Chapter 3 Data Quality in Business -- Introduction -- Data Quality Dimensions -- Context in Data Quality -- Consequences and Costs of Poor Data Quality -- Data Depreciation and Its Factors -- Data in IT Systems -- Data Quality and Trusted Information -- Key Takeaways -- Conclusion -- References -- Part II Analyze Phase -- Chapter 4 Causes for Poor Data Quality -- Introduction -- Data Quality RCA Techniques -- Typical Causes of Poor Data Quality -- Key Takeaways -- Conclusion -- References -- Chapter 5 Data Lifecycle and Lineage -- Introduction -- Business-Enabled DLC Stages -- IT Business-Enabled DLC Stages -- Data Lineage -- Key Takeaways -- Conclusion -- References -- Chapter 6 Profiling for Data Quality -- Introduction -- Criteria for Data Profiling -- Data Profiling Techniques for Measures of Centrality -- Data Profiling Techniques for Measures of Variation -- Integrating Centrality and Variation KPIs -- Key Takeaways -- Conclusion -- References -- Part III Realize Phase -- Chapter 7 Reference Architecture for Data Quality -- Introduction -- Options to Remediate Data Quality -- DataOps -- Data Product -- Data Fabric and Data Mesh -- Data Enrichment -- Key Takeaways -- Conclusion -- References Chapter 8 Best Practices to Realize Data Quality -- Introduction -- Overview of Best Practices -- BP 1: Identify the Business KPIs and the Ownership of These KPIs and the Pertinent Data -- BP 2: Build and Improve the Data Culture and Literacy in the Organization -- BP 3: Define the Current and Desired State of Data Quality -- BP 4: Follow the Minimalistic Approach to Data Capture -- BP 5: Select and Define the Data Attributes for Data Quality -- BP 6: Capture and Manage Critical Data with Data Standards in MDM Systems -- Key Takeaways -- Conclusion -- References -- Chapter 9 Best Practices to Realize Data Quality -- Introduction -- BP 7: Rationalize and Automate the Integration of Critical Data Elements -- BP 8: Define the SoR and Securely Capture Transactional Data in the SoR/OLTP System -- BP 9: Build and Manage Robust Data Integration Capabilities -- BP 10: Distribute Data Sourcing and Insight Consumption -- Key Takeaways -- Conclusion -- References -- Part IV Sustain Phase -- Chapter 10 Data Governance -- Introduction -- Data Governance Principles -- Data Governance Design Components -- Implementing the Data Governance Program -- Data Observability -- Data Compliance - ISO 27001, SOC1, and SOC2 -- Key Takeaways -- Conclusion -- References -- Chapter 11 Protecting Data -- Introduction -- Data Classification -- Data Safety -- Data Security -- Key Takeaways -- Conclusion -- References -- Chapter 12 Data Ethics -- Introduction -- Data Ethics -- Importance of Data Ethics -- Principles of Data Ethics -- Model Drift in Data Ethics -- Data Privacy -- Managing Data Ethically -- Key Takeaways -- Conclusion -- References -- Appendix 1: Abbreviations and Acronyms -- Appendix 2: Glossary -- Appendix 3: Data Literacy Competencies -- About the Author -- Index -- EULA. Datenanalyse (DE-588)4123037-1 gnd rswk-swf Datenmanagement (DE-588)4213132-7 gnd rswk-swf Datenqualität (DE-588)1036653315 gnd rswk-swf Datenanalyse (DE-588)4123037-1 s Datenqualität (DE-588)1036653315 s Datenmanagement (DE-588)4213132-7 s DE-604 Erscheint auch als Druck-Ausgabe Southekal, Prashanth Data Quality Newark : John Wiley & Sons, Incorporated,c2023 9781394165230 Erscheint auch als Online-Ausgabe, PDF 9781394165254 |
| spellingShingle | Southekal, Prashanth H. Data quality empowering businesses with analytics and AI Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- Acknowledgments -- Part I Define Phase -- Chapter 1 Introduction -- Introduction -- Data, Analytics, AI, and Business Performance -- Data as a Business Asset or Liability -- Data Governance, Data Management, and Data Quality -- Leadership Commitment to Data Quality -- Key Takeaways -- Conclusion -- References -- Chapter 2 Business Data -- Introduction -- Data in Business -- Telemetry Data -- Purpose of Data in Business -- Business Data Views -- Key Characteristics of Business Data -- Critical Data Elements (CDEs) -- Key Takeaways -- Conclusion -- References -- Chapter 3 Data Quality in Business -- Introduction -- Data Quality Dimensions -- Context in Data Quality -- Consequences and Costs of Poor Data Quality -- Data Depreciation and Its Factors -- Data in IT Systems -- Data Quality and Trusted Information -- Key Takeaways -- Conclusion -- References -- Part II Analyze Phase -- Chapter 4 Causes for Poor Data Quality -- Introduction -- Data Quality RCA Techniques -- Typical Causes of Poor Data Quality -- Key Takeaways -- Conclusion -- References -- Chapter 5 Data Lifecycle and Lineage -- Introduction -- Business-Enabled DLC Stages -- IT Business-Enabled DLC Stages -- Data Lineage -- Key Takeaways -- Conclusion -- References -- Chapter 6 Profiling for Data Quality -- Introduction -- Criteria for Data Profiling -- Data Profiling Techniques for Measures of Centrality -- Data Profiling Techniques for Measures of Variation -- Integrating Centrality and Variation KPIs -- Key Takeaways -- Conclusion -- References -- Part III Realize Phase -- Chapter 7 Reference Architecture for Data Quality -- Introduction -- Options to Remediate Data Quality -- DataOps -- Data Product -- Data Fabric and Data Mesh -- Data Enrichment -- Key Takeaways -- Conclusion -- References Chapter 8 Best Practices to Realize Data Quality -- Introduction -- Overview of Best Practices -- BP 1: Identify the Business KPIs and the Ownership of These KPIs and the Pertinent Data -- BP 2: Build and Improve the Data Culture and Literacy in the Organization -- BP 3: Define the Current and Desired State of Data Quality -- BP 4: Follow the Minimalistic Approach to Data Capture -- BP 5: Select and Define the Data Attributes for Data Quality -- BP 6: Capture and Manage Critical Data with Data Standards in MDM Systems -- Key Takeaways -- Conclusion -- References -- Chapter 9 Best Practices to Realize Data Quality -- Introduction -- BP 7: Rationalize and Automate the Integration of Critical Data Elements -- BP 8: Define the SoR and Securely Capture Transactional Data in the SoR/OLTP System -- BP 9: Build and Manage Robust Data Integration Capabilities -- BP 10: Distribute Data Sourcing and Insight Consumption -- Key Takeaways -- Conclusion -- References -- Part IV Sustain Phase -- Chapter 10 Data Governance -- Introduction -- Data Governance Principles -- Data Governance Design Components -- Implementing the Data Governance Program -- Data Observability -- Data Compliance - ISO 27001, SOC1, and SOC2 -- Key Takeaways -- Conclusion -- References -- Chapter 11 Protecting Data -- Introduction -- Data Classification -- Data Safety -- Data Security -- Key Takeaways -- Conclusion -- References -- Chapter 12 Data Ethics -- Introduction -- Data Ethics -- Importance of Data Ethics -- Principles of Data Ethics -- Model Drift in Data Ethics -- Data Privacy -- Managing Data Ethically -- Key Takeaways -- Conclusion -- References -- Appendix 1: Abbreviations and Acronyms -- Appendix 2: Glossary -- Appendix 3: Data Literacy Competencies -- About the Author -- Index -- EULA. Datenanalyse (DE-588)4123037-1 gnd Datenmanagement (DE-588)4213132-7 gnd Datenqualität (DE-588)1036653315 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4213132-7 (DE-588)1036653315 |
| title | Data quality empowering businesses with analytics and AI |
| title_auth | Data quality empowering businesses with analytics and AI |
| title_exact_search | Data quality empowering businesses with analytics and AI |
| title_exact_search_txtP | Data quality empowering businesses with analytics and AI |
| title_full | Data quality empowering businesses with analytics and AI Prashanth H. Southekal |
| title_fullStr | Data quality empowering businesses with analytics and AI Prashanth H. Southekal |
| title_full_unstemmed | Data quality empowering businesses with analytics and AI Prashanth H. Southekal |
| title_short | Data quality |
| title_sort | data quality empowering businesses with analytics and ai |
| title_sub | empowering businesses with analytics and AI |
| topic | Datenanalyse (DE-588)4123037-1 gnd Datenmanagement (DE-588)4213132-7 gnd Datenqualität (DE-588)1036653315 gnd |
| topic_facet | Datenanalyse Datenmanagement Datenqualität |
| work_keys_str_mv | AT southekalprashanthh dataqualityempoweringbusinesseswithanalyticsandai |