× VitalSource eBookVitalSource Bookshelf gives you access to content when, where, and how you want.When you read an eBook on VitalSource Bookshelf, enjoy such features as:. Access online or offline, on mobile or desktop devices.

1. Data Mining In Business Intelligence
2. Data Mining For Business Analytics Concepts Techniques And Applications In Python Pdf
3. Data Mining For Business Intelligence 2nd Edition 2017

Data Mining In Business Intelligence

Bookmarks, highlights and notes sync across all your devices. Smart study tools such as note sharing and subscription, review mode, and Microsoft OneNote integration.

Keep in mind that this path can vary depending on the user's preference. The full command line for uninstalling Visual 2012 Lighting Software is C:Program Files (x86)Visual 2012Programunins000.exe. Visual basic lighting software.

Search and navigate content across your entire Bookshelf library. Interactive notebook and read-aloud functionality. Look up additional information online by highlighting a word or phrase. PrefaceIntroductionWhat This Book IsWhy You Should Be Reading This BookOrganization of the BookOur Approach to Knowledge TransferContact MeAcknowledgementsForewordChapter 1. Business Intelligence and Information ExploitationImproving the Decision-Making ProcessWhy a Business Intelligence Program?Taking Advantage of the Information AssetBusiness Intelligence and Program SuccessBusiness Intelligence DefinedActionable IntelligenceThe Analytics SpectrumTaming the Information ExplosionConsiderationsContinuing Your Business Intelligence EducationEndnotesChapter 2. The Value of Business IntelligenceValue Drivers and Information UsePerformance Metrics and Key Performance IndicatorsUsing Actionable KnowledgeHorizontal Use Cases for Business IntelligenceVertical Use Cases for Business IntelligenceBusiness Intelligence Adds ValueChapter 3.

Data Mining For Business Analytics Concepts Techniques And Applications In Python Pdf

Planning for SuccessIntroductionOrganizational Preparedness for Business Intelligence and AnalyticsInitial Steps in Starting a Business Intelligence ProgramBridging the Gaps Between Information Technology and the Business UsersKnowing the Different Types of Business Intelligence UsersBusiness Intelligence Success Factors: A Deeper DiveMore on Building Your TeamStrategic Versus Tactical PlanningSummaryEndnotesChapter 4. Developing Your Business Intelligence RoadmapA Business Intelligence Strategy: Vision to BlueprintReview: The Business Intelligence and Analytics SpectrumThe Business Intelligence Roadmap: Example PhasingPlanning the Business Intelligence PlanChapter 5. The Business Intelligence EnvironmentAspects of a Business Intelligence and Analytics Platform and StrategyThe Organizational Business Intelligence FrameworkServices and System EvolutionManagement IssuesAdditional ConsiderationsChapter 6. Business Processes and Information FlowAnalytical Information Needs and Information FlowsInformation Processing and Information FlowThe Information Flow ModelPractical UseModeling FrameworksManagement IssuesDeeper DivesChapter 7. Data Requirements AnalysisIntroductionBusiness Uses of InformationMetrics: Facts, Qualifiers, and ModelsWhat is Data Requirements Analysis?Assessing SuitabilitySummaryChapter 8. Data Warehouses and the Technical Business Intelligence ArchitectureIntroductionData Modeling and AnalyticsThe Data WarehouseAnalytical PlatformsOperational Data StoresManagementDo You Really Need a Data Warehouse?SummaryChapter 9.

MetadataWhat is Metadata?The Origin and Utility of MetadataTypes of MetadataSemantic Metadata Processes for Business AnalyticsFurther ConsiderationsUsing Metadata ToolsChapter 10. Data ProfilingEstablishing Usability of Candidate Data SourcesData Profiling ActivitiesData Model InferenceAttribute AnalysisRelationship AnalysisManagement IssuesSummaryChapter 11. Business RulesThe Value Proposition of Business RulesThe Business Rules ApproachThe Definition of a Business RuleBusiness Rule SystemsSources of Business RulesManagement IssuesTo Learn MoreEndnotesChapter 12.

Data QualityGood Decisions Rely on Quality InformationThe Virtuous Cycle of Data QualityTypes of Data FlawsBusiness Impacts of Data FlawsDimensions of Data QualityData Quality AssessmentData Quality RulesContinuous Data Quality Monitoring and ImprovementConsiderations Regarding Data Quality for Business AnalyticsData CleansingSummaryChapter 13. Data IntegrationImproving Data AccessibilityExtraction/Transformation/LoadingData Latency and Data SynchronyData Replication and Change Data CaptureData Federation and VirtualizationData Integration and Cloud ComputingInformation ProtectionMore on Merge/Purge and Record ConsolidationThoughts on Data Stewardship and Governance for IntegrationChapter 14. High-Performance Business IntelligenceThe Need for SpeedThe Value of ParallelismParallel Processing SystemsSymmetric MultiprocessingParallelism and Business IntelligencePerformance Platforms and Analytical AppliancesData Layouts and PerformanceMapReduce and HadoopAssessing Architectural Suitability for Application PerformanceEndnoteChapter 15. Deriving Insight from Collections of DataIntroductionCustomer Profiles and Customer BehaviorCustomer Lifetime ValueDemographics, Psychographics, GeographicsGeographic DataBehavior AnalysisConsideration When Drawing InferencesChapter 16. Creating Business Value through Location-Based IntelligenceThe Business Value of LocationDemystifying Geography: Address Versus LocationGeocoding and Geographic EnhancementFundamentals of Location-Based Intelligence for Operational UsesGeographic Data ServicesChallenges and ConsiderationsWhere to Next?Chapter 17. Knowledge Discovery and Data Mining for Predictive AnalyticsBusiness DriversData Mining, Data Warehousing, Big DataThe Virtuous CycleDirected Versus Undirected Knowledge DiscoverySix Basic Data Mining ActivitiesData Mining TechniquesTechnology ExpectationsSummaryChapter 18. Repurposing Publicly Available DataUsing Publicly Available Data: Some ChallengesPublic DataData ResourcesThe Myth of PrivacyInformation Protection and Privacy ConcernsFinding and Using Open Data SetsChapter 19.

Knowledge DeliveryReview: The Business Intelligence User TypesStandard ReportsInteractive Analysis and Ad Hoc QueryingParameterized Reports and Self-Service ReportingDimensional AnalysisAlerts/NotificationsVisualization: Charts, Graphs, WidgetsScorecards and DashboardsGeographic VisualizationIntegrated AnalyticsConsiderations: Optimizing the Presentation for the Right MessageChapter 20. Emerging Business Intelligence TrendsSearch as a Business Intelligence TechniqueText AnalysisEntity Recognition and Entity ExtractionSentiment AnalysisMobile Business IntelligenceEvent Stream ProcessingEmbedded Predictive Analytic ModelsBig Data AnalyticsConsiderationsEndnoteChapter 21. Quick Reference GuideAnalytics ApplianceBusiness AnalyticsBusiness IntelligenceBusiness RulesDashboards and ScorecardsData CleansingData EnhancementData GovernanceData IntegrationData MartData MiningData ModelingData ProfilingData QualityData WarehouseDimensional ModelingELT (Extract, Load, Transform)ETL (Extract, Transform, Load)Event Stream ProcessingHadoop and MapReduceLocation Intelligence and Geographic AnalyticsMetadata and Metadata ManagementMobile Business IntelligenceOnline Analytical Processing (OLAP)Parallel and Distributed ComputingQuery and ReportingEndnotesBibliographyIndex. Business Intelligence: The Savvy Managers Guide, Second Edition, discusses the objectives and practices for designing and deploying a business intelligence (BI) program. It looks at the basics of a BI program, from the value of information and the mechanics of planning for success to data model infrastructure, data preparation, data analysis, integration, knowledge discovery, and the actual use of discovered knowledge.Organized into 21 chapters, this book begins with an overview of the kind of knowledge that can be exposed and exploited through the use of BI.

Data Mining For Business Intelligence 2nd Edition 2017

It then proceeds with a discussion of information use in the context of how value is created within an organization, how BI can improve the ways of doing business, and organizational preparedness for exploiting the results of a BI program. It also looks at some of the critical factors to be taken into account in the planning and execution of a successful BI program. In addition, the reader is introduced to considerations for developing the BI roadmap, the platforms for analysis such as data warehouses, and the concepts of business metadata.

Other chapters focus on data preparation and data discovery, the business rules approach, and data mining techniques and predictive analytics. Finally, emerging technologies such as text analytics and sentiment analysis are considered.This book will be valuable to data management and BI professionals, including senior and middle-level managers, Chief Information Officers and Chief Data Officers, senior business executives and business staff members, database or software engineers, and business analysts. Key Features.