DATA QUALITY : the enabler for analytics and ai success.: the enabler for analytics and ai success. (2023)
- Record Type:
- Book
- Title:
- DATA QUALITY : the enabler for analytics and ai success.: the enabler for analytics and ai success. (2023)
- Main Title:
- DATA QUALITY : the enabler for analytics and ai success.
- Other Names:
- SOUTHEKAL, PRASHANTH
- Contents:
- Foreword by Bill Inmon Preface About the Book Quality Principles Applied in This Book Organization of the Book Who Should Read This Book? References Acknowledgments 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 (CDE) 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 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 ReferencesForeword by Bill Inmon Preface About the Book Quality Principles Applied in This Book Organization of the Book Who Should Read This Book? References Acknowledgments 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 (CDE) 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 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 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: 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 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 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 … (more)
- Publisher Details:
- S.l. : JOHN WILEY
- Publication Date:
- 2023
- Extent:
- 1 online resource
- Subjects:
- 658.05
Artificial intelligence
Business -- Data processing
Data protection
Electronic data processing -- Quality control - Languages:
- English
- ISBNs:
- 9781394165247
1394165242
9781394165254
1394165250 - Related ISBNs:
- 1394165234
9781394165230 - Access Rights:
- Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
- Access Usage:
- Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD.DS.769033
- Ingest File:
- 19_011.xml