This Data Governance and Maturity Training helps you understand Data Governance and assess your Data Maturity. It uses the IEC/ISO 38505 governance of data standards, written for organizations that want to develop and implement a data strategy. For example, this strategy may be part of a plan to deliver new services or products to their customers. Introduction to Data Governance and Assessing Data Maturity Benefits In this course, you will: Learn the different Data Terminology: OLTP and OLAP, Dashboards, and Scorecards. Understand what data is and the history of data. Learn about the different data maturity levels. Understand how to make decisions with data, report on the data, and distribute the data. Understand when to dispose of data and why. Training Prerequisites Attendees should have a basic understanding of what data is and the function of data analysis. You can gain this from attending course Learning Tree course 1290, Introduction to Data Analytics. Certification Information Learning Tree End of Course Exam included Data Governance and Maturity Training Outline Chapter 1: A World of Data What Is Data?Health and Census DataTraditional and Modern Data-Heavy IndustriesData Quality DisastersHow Good and Bad Data Impacts Your BusinessWhy Poor-Quality Data ExistsData and the Digital RevolutionFive Key Drivers for Data AwarenessCauses of Poor Data QualityComponents of a Data SolutionData Products: TerminologyData Products: OLTP and OLAPData Products: Dashboards and ScorecardsData Products: Extract, Transform, Load, and Reporting Tools“Big” DataData and Digital Chapter 2: Introducing Data Governance & Data Management Data Governance Or Data Management?Data Governance StandardsOther Definitions of Data GovernanceData Governance ResponsibilitiesData ManagementISO Standards In This CourseISO/DIS 37000 Wheel: The Centrality Of GovernanceISO/DIS 37000*:Guidance for the Governance of OrganizationsISO/IEC 38500:2015Governance of IT for The OrganizationData GovernanceThe Definitions and Focus of Data GovernanceOther Definitions of Data GovernanceSix Use Cases for Data GovernanceData Governance: Areas of FocusWho Manages Data Governance? Chapter 3: Working with Data Trust IssuesImpact On DirectorsOpportunities For Data-Driven BusinessThe Risk of Unintended OutcomesRegulation Of DataMaturity Assessment: Creating the VisionData Maturity LevelsLevel 1: Data AwareLevel 2: Data CapableLevel 3: Data AdeptLevel 4: Data InformedLevel 5: Data PioneerDiscussion Point: Driving Data Initiatives ForwardWhat Are Business Drivers?Top-Down Data AnalysisEnterprise Information ArchitectureDesigning Standardized Business MetadataBottom-Up Source Data AnalysisSource Data SelectionData Selection IssuesExtract, Transform, and Load (ETL)Examples: Specifying ETL RulesData TechnologiesEnabling Technologies That Generate DataMoving Data to a Data WarehouseDeveloping the Data WarehouseDenormalizationOLTP vs. OLAPOLAP TerminologyValue of OLAPIdentifying Enterprise Systems and DataHow Is Enterprise Data Stored?Unstructured DataTechnical InfrastructureIssues: Structured and Unstructured Data Chapter 4: Principles & Components of Data Governance Principles & Components of Data GovernanceThe Principles of Data GovernanceComponents of Data GovernanceThe Data Governance ParadoxThe Data Governance Paradox: The Hidden Data FactoryBarriers to Data GovernanceWhen Data Governance FailsSetting Governance Goals and ObjectivesCreating Your Data Governance Vision Chapter 5: Building a Data Strategy Your Key Data ChallengesData Management MetricsBusiness CultureEffective Business CommunicationSelling Data GovernanceBusiness Culture Change and Data GovernanceCreating a Cultural Change PlanCommunications Strategies for a Cultural Change PlanUsing a Motivation ModelUsing a Motivation ModelTools & TechnologyBuilding a Data Governance Business CaseWhat Are Business Drivers?Building a Data Governance Business Case:Costs and BenefitsBuilding a Data Governance Business Case:Implementation PlanFive Data Governance ModelsPros & Cons of Each ModelUsing The Right Model for Your Organization Chapter 6: Building or Adopting a Data Governance Framework Data Governance FrameworkData Governance Framework Examples: DGIData Governance Framework Examples: McKinseyData Governance Framework: OverviewThe Data Governance Framework: VisionThe Data Governance Framework: StrategyThe Data Governance Framework: Organization & PeopleImplementing a Data Governance FrameworkWhat Does a Data Governance Framework Provide?The Benefits of a Data Governance FrameworkDGF Deliverables & Actions ChecklistData Governance Framework Chapter 7: Data Management Processes The Data Governance RoadmapData Governance Capability MappingThe Data Governance Roadmap ChecklistHints & Tips for Developing RoadmapsOrganizing Practical Data ManagementIdentifying and Designing Data Management ProcessesData Ownership & StewardshipWhat are Stakeholders?Types of Requirements ErrorsWhy Do We Care?Defining Organizational Goals and ObjectivesIdentifying Your Key DataData Management and MeasurementQuality Measures for DataBaselines and Improvement TargetsData Governance & Management ToolsetData Improvement PlansThe Role of IT in Data Governance & ManagementIssue Logging Chapter 8: Data Management & Accountability Dealing with ConnectivityIssues With a Connected WorldAddressing Issues with the Connected WorldExamples of Issues with the Connected WorldThe Data Accountability MapData AccountabilityData Collection Governance IssuesCollecting DataThe Quality of Data CollectedStoring DataDecideThe Governance Of Decision MakingBias in the Decision-Making ProcessAutomated Decision-MakingReporting on DataStrategy for Reporting on DataData Classification for ReportsDistribution of DataAspects of Data SecurityModels For Data Distribution:Example: ENISA* Traffic Light ProtocolWhy Dispose of Data?Candidates For Data DisposalPolicy For Data DisposalCreating Reports to Identify Disposable DataExternal Triggers For Data DisposalPermanent RemovalArchiving DataBalancing Value, Risk, and ConstraintMy Summary
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