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2025-01-21 07:12本頁面
【正文】 ation and Growth Patterns.Decision Support Application Customer ProfilingCustomerLoyalty? Customer Reward Program Analysis。? Outgoing and Ining Traffic Analysis。? Call Volume Analysis。? Call Pattern Analysis。? Price and Rate Plan Analysis.EXAMPLE? Product Offering and Service Packaging? Cross Selling of ProductDecision Support Application Customer ProfilingNetworkUtilization? Roaming Analysis。? Response to Complain Analysis.EXAMPLE? Proactively anticipate growing trends of problems? Anticipate and increase Quality Of ServiceDecision Support Application Customer ProfilingServicePlanning? Service Item Utilization。? Customer Ranking Analysis。Decision Support SystemMonopolistic Market Liberalized MarketOpen MarketHow Can Decision Support Help ?Increase Competition From External CarrierIncrease Customer Sophistication Resulting from CompetitionIssues ChallengesSituationCounteract Differentiate Improve Customer SegmentationEnhance Target MarketingData Warehouse At WorkTELCO IT ArchitectureBilling InformationPayment InformationCustomer InformationCustomer Care InformationFinancial InformationCall InformationContract Information Service Package InformationService Item InformationContract HistoryIn Bound CallOut Bound CallRate Plan InformationBalance Sheet Ine StatementTransactionalInformationWarehouse Conceptual Model The ArchitectureCustomerContractBilling PaymentCustomerDemographicContractHistoryServicePackagesReasonStatus Call RecordsCallDemographicPeriodCustomerCarePromotionDecision Support Application Customer ProfilingCustomerServicePlanningNetworkUtilizationCustomerCareCustomerAcquisitionCustomerRetentionCustomerBilling/PaymentFraudCustomerLoyaltyCustomerCare? Customer Complain and Response。? Customer Targeting。Data Mining Technology? Cluster Similar Behavior。? Forecasting。 No shared reference data– Once built, difficult to integratew Vs. Dependent and/or Architected Data MartsExternalDataSystemsData Warehousing Market Themesw Avoiding Failure– 1 in 2 are failing– High Riskw Return on Investment– Measuring Business Value– Data Warehouses vs. Data Martsw Data Warehouse Management– Evolve with, not after, the business does.– Support growing number of users, in more places, with more data! The Critical SuccessFactor in Implementinga Data Warehousein your OrganizationSession Agendaw Data Warehousing todayw 8 Reasons of Failures to Avoid !!!w 10 Critical Success Factorsw How to measure successw SummaryData Warehousing Todayw Many flavors of DW has been implemented (in Asia as well)– Enterprise endtoend DW– Metadata Management– Subject Area Data Marts– Reporting– OLAP– EISw ERP users implementing DWw More emphasis of Webenablementw However …. 1 of 2 DW projects fails !!!!8 Reasons of Failurew Success is hard to measure…Failure is easy !!!w Reasons why DW projects fail– No more funding– Bad data quality– Users unhappy with query tools– Only a small percentage of users use the DW– Poor performance– Inability to expand– Data is not integrated– ETL process does not fit batch windowCritical Success Factorsw Common Data Definitions– Consolidate different sets of departmental definitions (Extremely difficult ….)– These definitions are rarely documented !!!– Each project should have a glossary of business terms to support the projectLegacy ApplicationsVSAMIDMSIMS CICSCOBOLMultimediaDocumentsPackagedApplicationsGroupware DatabasesCritical Success Factorsw Welldefined transformation rules– Data from source systems will be transformed in one way or another.– Data will always be specifically selected, recorded, summarized and integrated with other data– Transformation rules are critical to do this correctlyCritical Success Factorsw Properly trained users– Regardless of how easy to use a tool is, users must be trained– Training should be geared to the level of user and the way they use the data warehouse– Types of training? how to use the tools? how to use any custom developed applications? availability of predefined queries reports? the data itself, and data structures for more powerful usersCritical Success Factorsw Expectations municated to users– Performance.– Availability of the data warehouse.– Functions and what data is accessible, what predefined queries and reports are available, the level of detail data and how data is integrated and aggregated.– The expectations of simplicity and easeofuse.– The expectations of accuracy in both data cleanliness and what the data means.– Timeliness of when data will be available, and frequency of refreshing the data.– Schedule expectations (system delivery).– Wh
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