Business Intelligence and Analytics
Advanced AnalyticsAffinity-Based RecommendationsAgile AnalyticsAnalytical DatabaseAnalytics ArchitectureAnalytics SuiteAnalytics-as-a-Service (AaaS)Application AnalyticsApplication Lifecycle Management (ALM)Behavioral SegmentationBig Data SecurityBusiness Intelligence ArchitectureBusiness Process Modeling (BPM)Call AttributionCloud AnalyticsCloud AutomationCollaborative FilteringColumnar DatabaseConnection StringContextual DataContinuous Intelligence (CI)Conversion AttributionCRM OnboardingCross-Channel TrackingData ActivationData BlendingData CleaningData DiscoveryData ExplorationData GovernanceData IntelligenceData MartData MiningData ModelingData PreparationData ScienceData StewardshipData StorytellingData Warehouse ArchitectureDatabase SecurityDeep LearningDescriptive AnalyticsDevOps Continuous IntegrationEdge AnalyticsEmbedded ReportingEntity Relationship Diagram (ERD)Financial Data ManagementFirmographicsGeo AnalyticsHealthcare AnalyticsHealthcare InformaticsIn-Memory BIInfused AnalyticsInteractive VisualizationKPI DashboardKPI TrackingLocation AnalyticsManaged CloudMarketing Acceleration PlatformMarketing AnalyticsMarketing Qualified LeadMobile AnalyticsMPP DatabaseNatural Language UnderstandingNet Promoter Score (NPS)Next Best Action MarketingOmnichannel RetailingOperational IntelligenceOperational ReportingPredictive Data AnalyticsPredictive MaintenancePredictive SegmentationPrescriptive AnalyticsProduct AnalyticsR AnalyticsReal Time DashboardReal-Time AnalyticsRelational DatabaseReporting PerformanceRetail AnalyticsRetention MarketingSelf-Service Business IntelligenceSQL for Data AnalysisTalent AnalyticsText MiningUnified Data ManagementUnstructured DataVisual Data AnalysisVisual Workflow
A/B TestingActive BuyerAffinity MarketingAffinity-Based RecommendationsAudience BuyingBaby BoomersBack to Back Focus GroupsBrand IdentityClickstream BehaviorCluster AnalysisCRM OnboardingCustomer Cohort AnalysisCustomer Experience Management (CEM)Customer JourneyLifestyle ResearchLifetime ValueRaceRevenue Per VisitorTarget AudienceUser IDUser-Generated Content (UGC)
Display & Native Advertising
Ad MediationAd ViewabilityAutomated bid strategyBehavioral AdvertisingContextual targetingConversion TrackingCost per acquisition (CPA)Display Advertising TrafficImpressionsInteractive Mobile AdsLocation-based Advertising (LBA)Native AdsPath Length ReportPlayable AdsProgrammatic Media BuyingRewarded Video AdsSmart ListsUser IDVideo Companion ImpressionsVideo PublisherView RateView-through conversion windowYield Management
- Email Marketing
Market Research & Traffic Analysis
A.C. Nielsen Retail IndexA/B TestingAd Concept TestingAd Hoc ResearchAdobe AnalyticsAffinity MarketingBack to Back Focus GroupsCluster AnalysisCore Based Statistical Area (CBSA)Cross-Device MarketingData CollectionData MiningDirect ChannelDisplay ChannelEmail ChannelEngagement RateEye TrackingHomogeneous GroupsHypothesis TestingIndexLifestyle ResearchLikert ScaleMarket EffectivenessMarket SegmentationMarket ShareMonthly Unique VisitorsNet Promoter Score (NPS)Neural NetworkNiche MarketingNielsen RatingsObservation BiasObservation ResearchOrganic ChannelPaid Search ChannelPath Length ReportPrimary ResearchQualitative ResearchRaceReferral ChannelSocial Media ChannelWebsite Survey
MarTech and AdTech
Acme DataAdobe TargetAmazon CloudFrontAmazon RedshiftAnalanceAnswerMinerAppDynamicsBaremetricsBomboraBootstrapBrightcoveCisco CMX EngageDasherooDundas BIFivetranFunnelGoogle BigQueryGoogle Data StudioGoogle Marketing PlatformGoogle OptimizeHoneywell Operational IntelligenceIBM Cognos AnalyticsIBM InfoSphere Data ArchitectInspectletJenkinsKissflowKNIME Analytics PlatformLiveRampLookerMarketing Technology StackMarketoMicrosoft Power BIMicrosoft SQL Server ReportingNutanixOptimizelyPendoPowerCenter InformaticaQlikViewSalesforce PardotSAP Crystal ReportsSocialbakersSQLiteStriimTableauTobii DynavoxTrade DeskTrifactaUserExperiorVisual Website Optimizer (VWO)Wolfram MathematicaWooCommerceYellowfin BIYieldify
Mobile App Intelligence
Ad MediationAd ViewabilityAd WhalesAdjustApp Churn RateApp DemographicsApp MonetizationApp RatingApp Store TrafficApplication Lifecycle Management (ALM)AR (Augmented Reality) AdvertisingAverage Revenue Per Paying User (ARPPU)Cross PromotionDaily Active Users (DAU)Dynamic TestingIn-App AdvertisingIn-App BiddingInstant MessagingInteractive Mobile AdsK-FactorLocation-based Advertising (LBA)Mobile Ad FraudMobile AttributionMonthly Active Users (MAU)Playable AdsPush NotificationRewarded Video AdsSDK MediationSearch TermsServer Side MediationYield Management
Paid Search Advertising
Ad deliveryAd extensionsAd MediationAd strengthAd ViewabilityAutomated bid strategyAverage Order Value (AOV)Combined audiencesConversion AttributionConversion TrackingCost per acquisition (CPA)Floodlight TagHeadline TestingImpressionsKeyword ResearchLocation-based Advertising (LBA)Path Length ReportQuality scoreReturn on investment (ROI)SitelinksSmart BiddingSmart ListsUser IDView-through conversion windowYield Management
Social Media Sensing
Ad ViewabilityCommunity ManagerCountry ReachImpressionsInstant MessagingLocation-based Advertising (LBA)MicrobloggingNet Media CostPinterestPlayable AdsReachReputation ManagementRewarded Video AdsRich PinsSnapchatSocial Media ConversionsSocial Media ListeningUser-Generated Content (UGC)Video Companion Impressions
Technology and Innovation
Artificial IntelligenceBig DataBlockchainData WarehouseDeep LearningDisruptive InnovationInternet of ThingsMarketing Technology StackPredictive Data AnalyticsPredictive SegmentationPrescriptive AnalyticsR AnalyticsVoice Assistants
TV Ad Measurement and Insights
Ad ViewabilityAd-supported streaming video on demand (ASVOD)Addressable TVAdvanced TVAutomatic content recognition (ACR)Broadcast Addressable TVHousehold DataLocation-based Advertising (LBA)Multichannel Video Programming Distributor (MVPD)Nielsen RatingsOver-the-top (OTT)Skinny BundleSubscription video on demand (SVOD)Targeting Rating Point (TRP)Television advertisementTelevision Viewer Rating (TVR)TV NetworkUpfrontsVideo on demand (VOD)
Data Warehouse Architecture
Data warehouse architecture refers to the design of an organization’s data collection and storage framework. Because data needs to be sorted, cleaned, and properly organized to be useful, data warehouse architecture focuses on finding the most efficient method of taking information from a raw set and placing it into an easily digestible structure that provides valuable BI insights.
What is the concept of data warehousing?
Data warehousing is the process of constructing and using a data warehouse. A data warehouse is constructed by integrating data from multiple heterogeneous sources that support analytical reporting, structured and/or ad hoc queries, and decision making.
What is data warehouse modeling?
A data model is a graphical view of data created for analysis and design purposes. Data modeling includes designing data warehouse databases in detail, it follows principles and patterns established in Architecture for Data Warehousing and Business Intelligence.
What are the different types of data warehouse architecture?
First of all, it is important to note what data warehouse architecture is changing. Companies are increasingly moving towards cloud-based data warehouses instead of traditional on-premise systems. The new cloud-based data warehouses do not adhere to the traditional architecture; each data warehouse offering has a unique architecture. See the full article for more details.
What is star schema in SQL?
In computing, the star schema is the simplest style of data mart schema and is the approach most widely used to develop data warehouses and dimensional data marts. The star schema consists of one or more fact tables referencing any number of dimension tables.
How can I use data warehouse architecture?
Establishing which type of database your organization needs and how you plan to interact with it is vital when searching for insights. It is also important to evaluate who is going to be examining data and what sources they need when considering your data warehouse architecture. Although the data warehouse vs data mart debate is not always applicable for smaller organizations, those with more teams, departments, and specific needs may benefit from the latter. Data marts’ specific subject-oriented nature makes them crucial aspects of your overall data warehouse architecture.
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