商务智能数据分析的管理视角.pptx

《商务智能数据分析的管理视角.pptx》由会员分享，可在线阅读，更多相关《商务智能数据分析的管理视角.pptx（50页珍藏版）》请在三一文库上搜索。

1、Chapter 2:Data WarehousingBusiness Intelligence:A Managerial Perspective on Analytics(3rd Edition)Copyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-2 2Learning ObjectivesUnderstand the basic definitions and concepts of data warehousesLearn different types of d

2、ata warehousing architectures;their comparative advantages and disadvantagesDescribe the processes used in developing and managing data warehousesExplain data warehousing operations(Continued)(Continued)Copyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-3 3Lear

3、ning ObjectivesExplain the role of data warehouses in decision supportExplain data integration and the extraction,transformation,and load(ETL)processesDescribe real-time(a.k.a.right-time and/or active)data warehousingUnderstand data warehouse administration and security issuesCopyright 2014 Pearson

4、Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-4 4Opening VignetteIsle of Capri Casinos Is Winning with Enterprise Data WarehouseCompany backgroundProblem descriptionProposed solutionResultsAnswer&discuss the case questions.Copyright 2014 Pearson Education,Inc.Copyright 2014 Pear

5、son Education,Inc.Slide 2-Slide 2-5 5Questions for the Opening Vignette1.Why is it important for Isle to have an EDW?2.What were the business challenges or opportunities that Isle was facing?3.What was the process Isle followed to realize EDW?Comment on the potential challenges Isle might have had g

6、oing through the process of EDW development.4.What were the benefits of implementing an EDW at Isle?Can you think of other potential benefits that were not listed in the case?5.Why do you think large enterprises like Isle in the gaming industry can succeed without having a capable data warehouse/bus

7、iness intelligence infrastructure?Copyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-6 6Main Data Warehousing TopicsDW definitionCharacteristics of DWData Marts ODS,EDW,MetadataDW FrameworkDW Architecture&ETL ProcessDW DevelopmentDW IssuesCopyright 2014 Pearson

8、 Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-7 7What is a Data Warehouse?A physical repository where relational data are specially organized to provide enterprise-wide,cleansed data in a standardized format“The data warehouse is a collection of integrated,subject-oriented data

9、bases designed to support DSS functions,where each unit of data is non-volatile and relevant to some moment in time”Copyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-8 8A Historical Perspective to Data WarehousingCopyright 2014 Pearson Education,Inc.Copyright

10、2014 Pearson Education,Inc.Slide 2-Slide 2-9 9Characteristics of DWsSubject orientedIntegratedTime-variant(time series)NonvolatileSummarizedNot normalizedMetadataWeb based,relational/multi-dimensional Client/server,real-time/right-time/active Copyright 2014 Pearson Education,Inc.Copyright 2014 Pears

11、on Education,Inc.Slide 2-Slide 2-1010Data MartA departmental small-scale“DW”that stores only limited/relevant data Dependent data mart A subset that is created directly from a data warehouse Independent data martA small data warehouse designed for a strategic business unit or a department Copyright

12、2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-1111Other DW ComponentsOperational data stores(ODS)A type of database often used as an interim area for a data warehouseOper marts-an operational data mart.Enterprise data warehouse(EDW)A data warehouse for the enterpris

13、e.Metadata Data about data.In a data warehouse,metadata describe the contents of a data warehouse and the manner of its acquisition and use Copyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-1212Application Case 2.1A Better Data Plan:Well-Established TELCOs Lev

14、erage Data Warehousing and Analytics to Stay on Top in a Competitive IndustryQuestions for Discussion1.What are the main challenges for TELCOs?2.How can data warehousing and data analytics help TELCOs in overcoming their challenges?3.Why do you think TELCOs are well suited to take full advantage of

15、data analytics?Copyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-1313A Generic DW FrameworkCopyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-1414Application Case 2.2Data Warehousing Helps MultiCare Save More LivesQuestio

16、ns for Discussion1.What do you think is the role of data warehousing in healthcare systems?2.How did MultiCare use data warehousing to improve health outcomes?Copyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-1515DW ArchitectureThree-tier architecture1.Data ac

17、quisition software(back-end)2.The data warehouse that contains the data&software3.Client(front-end)software that allows users to access and analyze data from the warehouseTwo-tier architectureFirst two tiers in three-tier architecture is combined into one sometimes there is only one tier?Copyright 2

18、014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-1616DW Architectures3-tier architecture2-tier architecture1-tier Architecture?Copyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-1717Data Warehousing Architectures Issues to consider

19、 when deciding which architecture to use:Which database management system(DBMS)should be used?Will parallel processing and/or partitioning be used?Will data migration tools be used to load the data warehouse?What tools will be used to support data retrieval and analysis?Copyright 2014 Pearson Educat

20、ion,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-1818A Web-Based DW ArchitectureAlternative DW ArchitecturesAlternative DW ArchitecturesEach architecture has advantages and disadvantages!Which architecture is the best?Copyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,

21、Inc.Slide 2-Slide 2-2121Ten factors that potentially affect the architecture selection decision1.Information interdependence between organizational units2.Upper managements information needs3.Urgency of need for a data warehouse4.Nature of end-user tasks5.Constraints on resources 6.Strategic view of

22、 the data warehouse prior to implementation7.Compatibility with existing systems8.Perceived ability of the in-house IT staff9.Technical issues10.Social/political factorsCopyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-2222Teradata Corp.DW ArchitectureCopyrigh

23、t 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-2323Data Integration and the Extraction,Transformation,and Load(ETL)ProcessETL=Extract Transform LoadData integration Integration that comprises three major processes:data access,data federation,and change capture.Ente

24、rprise application integration(EAI)A technology that provides a vehicle for pushing data from source systems into a data warehouse Enterprise information integration(EII)An evolving tool space that promises real-time data integration from a variety of sources,such as relational or multidimensional d

25、atabases,Web services,etc.Copyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-2424Data Integration and the Extraction,Transformation,and Load(ETL)ProcessCopyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-2525ETL(Extract,Tra

26、nsform,Load)Issues affecting the purchase of an ETL toolData transformation tools are expensiveData transformation tools may have a long learning curveImportant criteria in selecting an ETL toolAbility to read from and write to an unlimited number of data sources/architecturesAutomatic capturing and

27、 delivery of metadataA history of conforming to open standardsAn easy-to-use interface for the developer and the functional user Copyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-2626Data Warehouse DevelopmentData warehouse development approachesInmon Model:ED

28、W approach(top-down)Kimball Model:Data mart approach (bottom-up)Which model is best?Table 2.3 provides a comparative analysis between EDW and Data Mart approachOne alternative is the hosted warehouseCopyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-2727Applica

29、tion Case 2.5Starwood Hotels&Resorts Manages Hotel Profitability with Data WarehousingQuestions for Discussion1.How big and complex are the business operations of Starwood Hotels&Resorts?2.How did Starwood Hotels&Resorts use data warehousing for better profitability?3.What were the challenges,the pr

30、oposed solution,and the obtained results?Copyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-2828Additional Data Warehouse Considerations Hosted Data WarehousesBenefits:Requires minimal investment in infrastructureFrees up capacity on in-house systemsFrees up ca

31、sh flowMakes powerful solutions affordableEnables solutions that provide for growthOffers better quality equipment and softwareProvides faster connections more in the bookCopyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-2929Representation of Data in DWDimensi

32、onal Modeling A retrieval-based system that supports high-volume query accessStar schema The most commonly used and the simplest style of dimensional modelingContain a fact table surrounded by and connected to several dimension tablesSnowflakes schema An extension of star schema where the diagram re

33、sembles a snowflake in shapeCopyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-3030The ability to organize,present,and analyze data by several dimensions,such as sales by region,by product,by salesperson,and by time(four dimensions)Multidimensional presentation

34、 Dimensions:products,salespeople,market segments,business units,geographical locations,distribution channels,country,or industryMeasures:money,sales volume,head count,inventory profit,actual versus forecastTime:daily,weekly,monthly,quarterly,or yearlyMultidimensionalityCopyright 2014 Pearson Educati

35、on,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-3131Star versus Snowflake SchemaCopyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-3232Analysis of Data in DWOLTP vs.OLAPOLTP(online transaction processing)Capturing and storing data from ERP,CRM,POS,T

36、he main focus is on efficiency of routine tasksOLAP(Online analytical processing)Converting data into information for decision supportData cubes,drill-down/rollup,slice&dice,Requesting ad hoc reportsConducting statistical and other analyses Developing multimedia-based applicationsmore in the bookCop

37、yright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-3333OLAP vs.OLTPCopyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-3434OLAP OperationsSlice-a subset of a multidimensional arrayDice-a slice on more than two dimensionsDrill

38、Down/Up-navigating among levels of data ranging from the most summarized(up)to the most detailed(down)Roll Up-computing all of the data relationships for one or more dimensions Pivot-used to change the dimensional orientation of a report or an ad hoc query-page displayCopyright 2014 Pearson Educatio

39、n,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-3535OLAPSlicing Slicing Operations on a Operations on a Simple Tree-Simple Tree-DimensionalDimensionalData CubeData CubeCopyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-3636Variations of OLAP Multidim

40、ensional OLAP(MOLAP)OLAP implemented via a specialized multidimensional database(or data store)that summarizes transactions into multidimensional views ahead of time Relational OLAP(ROLAP)The implementation of an OLAP database on top of an existing relational database Database OLAP and Web OLAP(DOLA

41、P and WOLAP);Desktop OLAP,Copyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-3737Technology Insights 2.2Hands-On DW with MicroStrategyA wealth of teaching and learning resources can be found at TUN The available resources include scripted demonstrations,assignm

42、ents,white papers,etcCopyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-3838DW Implementation IssuesIdentification of data sources and governanceData quality planning,data model designETL tool selectionEstablishment of service-level agreementsData transport,dat

43、a conversionReconciliation processEnd-user supportPolitical issues more in the book Copyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-3939Successful DW ImplementationThings to AvoidStarting with the wrong sponsorship chainSetting expectations that you cannot m

44、eetEngaging in politically naive behaviorLoading the data warehouse with information just because it is availableBelieving that data warehousing database design is the same as transactional database designChoosing a data warehouse manager who is technology oriented rather than user oriented more in

45、the bookCopyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-4040Failure Factors in DW ProjectsLack of executive sponsorshipUnclear business objectivesCultural issues being ignoredChange managementUnrealistic expectationsInappropriate architectureLow data quality

46、/missing informationLoading data just because it is available Copyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-4141Massive DW and ScalabilityScalabilityThe main issues pertaining to scalability:The amount of data in the warehouseHow quickly the warehouse is e

47、xpected to growThe number of concurrent usersThe complexity of user queries Good scalability means that queries and other data-access functions will grow linearly with the size of the warehouseCopyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-4242Real-Time/Act

48、ive DW/BIEnabling real-time data updates for real-time analysis and real-time decision making is growing rapidlyPush vs.Pull(of data)Concerns about real-time BINot all data should be updated continuouslyMismatch of reports generated minutes apartMay be cost prohibitiveMay also be infeasible Copyrigh

49、t 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-4343Enterprise Decision Evolution and Data WarehousingCopyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-4444Real-Time/Active DW at TeradataCopyright 2014 Pearson Education,Inc.Co

50、pyright 2014 Pearson Education,Inc.Slide 2-Slide 2-4545Traditional versus Active DWCopyright 2014 Pearson Education,Inc.Copyright 2014 Pearson Education,Inc.Slide 2-Slide 2-4646DW Administration and SecurityData warehouse administrator(DWA)DWA shouldhave the knowledge of high-performance software,ha