Data Quality: Empowering Businesses with Analytics and AI

ISBN-13: 9781394165230 / Angielski / Twarda / 2023 / 304 str.

France; Kellogg School of Management Prashanth (ESC Lille

Spis treści

Forewordby Bill InmonPrefaceAbout the BookQuality Principles Applied in This BookOrganization of the BookWho Should Read This Book?ReferencesAcknowledgmentsDefine PhaseChapter 1: IntroductionIntroductionData, Analytics, AI, and Business PerformanceData as a Business Asset or LiabilityData Governance, Data Management, and Data QualityLeadership Commitment to Data QualityKey TakeawaysConclusionReferencesChapter 2: Business DataIntroductionData in BusinessTelemetry DataPurpose of Data in BusinessBusiness Data ViewsKey Characteristics of Business DataCritical Data Elements (CDE)Key TakeawaysConclusionReferencesChapter 3: Data Quality in BusinessIntroductionData Quality DimensionsContext in Data QualityConsequences and Costs of Poor Data QualityData Depreciation and Its FactorsData in IT SystemsData Quality and Trusted InformationKey TakeawaysConclusionReferencesAnalyze PhaseChapter 4: Causes for Poor Data QualityIntroductionData Quality RCA TechniquesTypical Causes of Poor Data QualityKey TakeawaysConclusionReferencesChapter 5: Data Lifecycle and LineageIntroductionBusiness-Enabled DLC StagesIT Business-Enabled DLC StagesData LineageKey TakeawaysConclusionReferencesChapter 6: Profiling for Data QualityIntroductionCriteria for Data ProfilingData Profiling Techniques for Measures of CentralityData Profiling Techniques for Measures of VariationIntegrating Centrality and Variation KPIsKey TakeawaysConclusionReferencesRealize PhaseChapter 7: Reference Architecture for Data QualityIntroductionOptions to Remediate Data QualityDataOpsData ProductData Fabric and Data MeshData EnrichmentKey TakeawaysConclusionReferencesChapter 8: Best Practices to Realize Data QualityIntroductionOverview of Best PracticesBP 1: Identify the Business KPIs and the Ownership of These KPIs and the Pertinent DataBP 2: Build and Improve the Data Culture and Literacy in the OrganizationBP 3: Define the Current and Desired state of Data QualityBP 4: Follow the Minimalistic Approach to Data CaptureBP 5: Select and Define the Data Attributes for Data QualityBP 6: Capture and Manage Critical Data with Data Standards in MDM SystemsKey TakeawaysConclusionReferencesChapter 9: Best Practices to Realize Data QualityIntroductionBP 7: Automate the Integration of Critical Data ElementsBP 8: Define the SoR and Securely Capture Transactional Data in the SoR/OLTP SystemBP 9: Build and Manage Robust Data Integration CapabilitiesBP 10: Distribute Data Sourcing and Insight ConsumptionKey TakeawaysConclusionReferencesSustain PhaseChapter 10: Data GovernanceIntroductionData Governance PrinciplesData Governance Design ComponentsImplementing the Data Governance ProgramData ObservabilityData Compliance - ISO 27001 and SOC2Key TakeawaysConclusionReferencesChapter 11: Protecting DataIntroductionData ClassificationData SafetyData SecurityKey TakeawaysConclusionReferencesChapter 12: Data EthicsIntroductionData EthicsImportance of Data EthicsPrinciples of Data EthicsModel Drift in Data EthicsData PrivacyManaging Data EthicallyKey TakeawaysConclusionReferencesAppendix 1: Abbreviations and AcronymsAppendix 2: GlossaryAppendix 3: Data Literacy CompetenciesAbout the AuthorIndex

O autorze

PRASHANTH SOUTHEKAL, PHD, is a data, analytics, and AI consultant, author, and professor. He has worked and consulted for over 80 organizations including P&G, GE, Shell, Apple, FedEx, and SAP. Dr. Southekal is the author of Data for Business Performance and Analytics Best Practices (ranked #1 analytics books of all time by BookAuthority) and writes regularly on data, analytics, and AI in Forbes and CFO.University. He serves on the Editorial Board of MIT CDOIQ Symposium and is an advisory board member at BGV (Benhamou Global Ventures) a Silicon Valley-based venture capital firm. Apart from his consulting and advisory pursuits, he has trained over 3,000 professionals worldwide in data and analytics. Dr. Southekal is also an adjunct professor of data and analytics at IE Business School (Madrid, Spain). CDO Magazine included him in the top 75 global academic data leaders of 2022. He holds a PhD from ESC Lille (FR), an MBA from the Kellogg School of Management (US), and holds the ICD.D designation from the Institute of Corporate Directors (Canada).