Chapter 5Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and VisualizationTurban, Aronson, and Liang Decision Support Systems and Intelligent Systems, Seventh Edition

Learning ObjectivesDescribe the issues in management of data.Understand the concepts and use of DBMS.Learn about data warehousing and data marts.Explain business intelligence/business analytics.Examine how decision making can be improved through data manipulation and analytics.Understand the interaction betwixt the Web and database technologies.Explain how database technologies are used in business analytics.Understand the impact of the Web on business intelligence and analytics.

Information Sharing a Principle Component of the National Strategy for Homeland Security VignetteNetwork of systems that provide knowledge integration and distributionHorizontal and vertical information sharingImproved communicationsMining of data stored in Web-enabled warehouse

Data, Information, KnowledgeDataItems that are the most elementary descriptions of things, events, activities, and transactionsMay be internal or externalInformationOrganized data that has meaning and valueKnowledgeProcessed data or information that conveys understanding or learning applicable to a problem or activity

Data Raw data collected manually or by instrumentsQuality is criticalQuality determines usefulnessContextual data qualityIntrinsic data qualityAccessibility data qualityRepresentation data qualityOften neglected or casually handledProblems exposed when data is summarized

Data

Cleanse dataWhen populating warehouseData quality action planBest practices for data qualityMeasure resultsData integrity issuesUniformityVersionCompleteness checkConformity checkGenealogy or drill-down

DataData IntegrationAccess needed to multiple sourcesOften enterprise-wide Disparate and heterogeneous databasesXML becoming language standard

External Data SourcesWebIntelligent agentsDocument management systemsContent management systemsCommercial databasesSell access to specialized databases

Database Management SystemsSoftware programSupplements operating systemManages dataQueries data and generates reportsData securityCombines with modeling language for construction of DSS

Database ModelsHierarchicalTop down, like inverted treeFields have only one parent, each parent can have multiple childrenFastNetwork Relationships created through linked lists, using pointersChildren can have multiple parentsGreater flexibility, substantial overheadRelationalFlat, two-dimensional tables with multiple access queriesExamines relations between multiple tablesFlexible, quick, and extendable with data independenceObject orientedData analyzed at conceptual levelInheritance, abstraction, encapsulation

Database Models, continuedMultimedia BasedMultiple data formatsJPEG, GIF, bitmap, PNG, sound, video, virtual realityRequires specific hardware for full feature availabilityDocument BasedDocument storage and managementIntelligentIntelligent agents and ANNInference engines

Data WarehouseSubject orientedScrubbed so that data from heterogeneous sources are standardizedTime series; no current statusNonvolatile Read onlySummarizedNot normalized; may be redundantData from both internal and external sources is presentMetadata includedData about dataBusiness metadataSemantic metadata

ArchitectureMay have one or more tiersDetermined by warehouse, data acquisition (back end), and client (front end)One tier, where all run on same platform, is rareTwo tier usually combines DSS engine (client) with warehouseMore economicalThree tier separates these functional parts

Migrating DataBusiness rulesStored in metadata repositoryApplied to data warehouse centrallyData extracted from all relevant sourcesLoaded through data-transformation tools or programsSeparate operation and decision support environmentsCorrect problems in quality before data storedCleanse and organize in consistent manner

Data Warehouse DesignDimensional modelingRetrieval basedImplemented by star schemaCentral fact tableDimension tablesGrainHighest level of detailDrill-down analysis

Data Warehouse DevelopmentData warehouse implementation techniques Top downBottom upHybridFederatedProjects may be data centric or application centricImplementation factorsOrganizational issuesProject issuesTechnical issuesScalableFlexible

Data MartsDependentCreated from warehouseReplicated Functional subset of warehouseIndependentScaled down, less expensive version of data warehouseDesigned for a department or SBUOrganization may have multiple data martsDifficult to integrate

Business Intelligence and AnalyticsBusiness intelligenceAcquisition of data and information for use in decision-making activitiesBusiness analyticsModels and solution methodsData miningApplying models and methods to data to identify patterns and trends

OLAPActivities performed by end users in online systemsSpecific, open-ended query generationSQLAd hoc reportsStatistical analysisBuilding DSS applicationsModeling and visualization capabilitiesSpecial class of toolsDSS/BI/BA front endsData access front endsDatabase front endsVisual information access systems

Data MiningOrganizes and employs information and knowledge from databasesStatistical, mathematical, artificial intelligence, and machine-learning techniques Automatic and fastTools look for patterns Simple models Intermediate modelsComplex Models

Data MiningData mining application classes of problemsClassificationClusteringAssociationSequencingRegressionForecastingOthersHypothesis or discovery drivenIterativeScalable

Tools and TechniquesData miningStatistical methodsDecision treesCase based reasoningNeural computingIntelligent agentsGenetic algorithmsText MiningHidden contentGroup by themesDetermine relationships

Knowledge Discovery in DatabasesData mining used to find patterns in dataIdentification of dataPreprocessingTransformation to common formatData mining through algorithmsEvaluation

Data VisualizationTechnologies supporting visualization and interpretationDigital imaging, GIS, GUI, tables, multidimensions, graphs, VR, 3D, animationIdentify relationships and trendsData manipulation allows real time look at performance data

MultidimensionalityData organized according to business standards, not analystsConceptualFactorsDimensionsMeasuresTimeSignificant overhead and storageExpensiveComplex

Analytic systemsReal-time queries and analysisReal-time decision-makingReal-time data warehouses updated daily or more frequentlyUpdates may be made while queries are activeNot all data updated continuouslyDeployment of business analytic applications

GISComputerized system for managing and manipulating data with digitized mapsGeographically orientedGeographic spreadsheet for modelsSoftware allows web access to mapsUsed for modeling and simulations

Web Analytics/IntelligenceWeb analyticsApplication of business analytics to Web sitesWeb intelligenceApplication of business intelligence techniques to Web sites