This document discusses business intelligence architecture and concepts. It covers topics like analysis services, SQL Server, data mining, integration services, and enterprise BI strategy and vision. It provides overviews of Microsoft's BI platform, conceptual frameworks, dimensional modeling, ETL processes, and data visualization systems. The goal is to improve organizational processes by providing critical business information to employees.

1. ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICESSQL SERVER SQL SERVER SQL SERVER SQL SERVER DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MININGINTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES Business Intelligence Architecture and Conceptual FrameworkINTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICESSQL SERVER SQL SERVER SQL SERVER SQL SERVERDATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING

2. About MeSlava KokaevGroup Leader at Boston Business Intelligence User GroupPrincipal BI Developer/ Architect at Industrial Defendervkokaev@bostonbi.orgwww.bostonbi.org/blog.aspx

3. Agenda

4. Drive Corporate PerformanceGiving a purpose to business intelligence“You can’t manage what you can’t measure. You can’t measure what you can’t describe”Robert Kaplan and David NortonAuthors of “The Balanced Scorecard”

5. Enterprise BI Strategy and VisionTo improve organizational KeyBusiness Processes and Operations by providing critical to business Information at RightTime and RightFormat to all levels of employees.Goals

6. Understanding The Business System Microsoft BI PlatformBusiness Intelligence SystemManagement SystemEnterpriseData WarehouseSystemBusinessAnalysisSystemOperational System

7. Microsoft’s BI platformCOLLABORATIONCONTENT MANAGEMENTSharePoint ServerSEARCHReportsDashboardsExcel WorkbooksAnalyticViewsScorecardsPlansEND USER TOOLS & PERFORMANCE MANAGEMENT APPSExcelPower PivotBI PLATFORMSQL Server Reporting ServicesSQL Server Analysis ServicesSQL Server DBMSSQL Server Integration Services

8. IntegrateReportAnalyzeCovered in Module 03Covered in Modules 06, 07, and 08Covered in Modules 04, 05, and 07SQL Server 2008 BI Platform ComponentsData acquisition from source systems and integration

9. Data transformation and synthesis

10. Data enrichment, with business logic, hierarchical views

11. Data discovery via data mining

12. Data presentation and distribution

13. Data access for the massesBusiness Intelligence Conceptual FrameworkSource SystemETL SystemDW SystemDA System

14. Sales Business ProcessBalance ScorecardsSales corrections and ImprovementPlan SalesSales QuotaStock DataSale Orders (Facts /Measures)Resellers SalesReseller (Dimension)Sales ResultMonitor SalesSales SummarySales TransactionAnalyze SalesSQL Server DBSales RepresentativeSales Manager

15. Bike FactoryTiresFactoryStill FactoryAdventureWorksHeadquarterPlastic FactoryColor FactoryAccessory FactoryWarehouseResellers

16. ETL SystemOLTPSSISDWOLAP

17. Enterprise Data Source StructureCall CenterWeb AppsCRMInventoryFinanceData WarehouseERPHR

18. ETL SystemExtract, Transform, Load (ETL)ETL is a process in Business Intelligence that:Extract data from the source systemsTransform the data to convert it to a desired stateLoad the data into the data warehouse

19. ETL Framework and Logical ArchitectureCheck System StateETL PackagesExtract DataFile SystemLoad StagingExtract from StagingOLTPSTAGING SchemaSend NotificationLog ETL Process Transform DataLoad DimensionsETL SchemaLoad FactsDWH SchemaDatabaseProcess CubeCube

20. ETL Benefits ProductivityCoding ETL scripts using a metadata-driven graphical tool with built-in data cleansing and transformation functions is generally faster than hand coding. Mappings, extract rules, cleansing rules, transformation rules, aggregation logic and loading rules are generally handled as separate objects in an ETL tool. This means that you can change one object in an ETL "string" without affecting the other objects. For example, you can change the loading logic for a particular target table (say, from direct insert to generating a flat loader file) without affecting the cleansing and/or transformation logic for that table. This compartmentalization eases maintenance, and reduces the need for retesting. Objects in an ETL tool (e.g., transformation rules) can be reused. ETL tools facilitate impact analysis when modifying or enhancing a data warehouse. Methodology•ETL tools impose a certain level of structure, rigor, and consistency in your development approach. Documentation•The meta data trapped by an ETL tool graphically documents source and target database structures, mappings (a.k.a. "data genealogy"), cleansing rules (a.k.a. "business rules") and transformation rules.

21. GoalsImplement many routines quickly, with limited developer resourcesReliability and AccuracyAbility to introduce \ modify \ remove transformation rulesAbility to maintain and apply logical business rules on dataSupport for scheduled and user-initiated package execution

22. Relational Data Warehouse Architecture and dimensional model ?

23. Star Schema vs. OLTPETL

24. Fact and Dimensions together or “Star Schema” Database

25. DimensionsDimensions are the foundation of the dimensional model, describing the objects of the business, such as employee, product, customer, service.They describe the surrounding measurement events. The business processes (facts) or actions of the business in which the dimensions participate. Each dimension table links to all the business processes in which it participates. A single dimension that is shared across all these processes is called a conformed dimension.

26. Fact TablesEach fact table contains the measurements associated with a specific business process. A record in a fact table is a measurement, and a measurement event can always produce a fact table record. These events usually have numeric measurements that quantify the magnitude of the event, such as quantity ordered, sale amount, or call duration. These numbers are called facts(or measuresin Analysis Services).The key to the fact table is a multi-part key made up of a subset of the foreign keys from each dimension table involved in the business event.

27. Reviewing Star Schema Benefits Transforms normalized data into a simpler modelDelivers high-performance queriesDelivers higher performing queries using Star Join Query OptimizationUses mature modeling techniques that are widely supported by many BI toolsRequires low maintenance as the data warehouse design evolves

28. Vendors, Suppliers,Channel partnersCustomersBusiness partnersMonitoring Systems Analysis SystemsBusiness Processes and Operations Controlling SystemsStrategy and Planning SystemsIT providersFinancial service providersEnterprise Business Analysis System

29. Business Process System CycleKaizen BPM

30. Abstract Functional Business ModelIDEF0 Modeling NotationFeedback (Improvement)PlanPlans, Business Rule and KPIInput DataProcess Output (Facts /Measures)DoResourcesCheckResult DataActData MiningReporting ServicesSQL ServerAnalysis Services

31. Business Frameworks and Logical Business Levels

32. ON-LINE Analytical ProcessingMultidimensional databases are also called online analytical processing (OLAP) databases and…Contain structures optimized for rapid ad hoc information retrievalPre-calculate and store aggregated valuesInclude calculation engines for fast, flexible transformation of base dataAre designed to reveal business trends and statistics not directly visible in the data retrieved from a data warehouseData mining models discover patterns in data, typically for prediction analysisProductAssociationSalesFinanceProduction

33. Understanding Cube Structure116499518931455194513769451553187412451576445147918741245295415751479157630071575232229541383300724553007Accessories1654Australia645136521456459882012Product Line2012CountryUnited States845Bikes7457002751082234905Canada905345ClothingQuarter 1761875Quarter 2745745FranceComponentsSemester 1Quarter 3Quarter 4TXSemester 2MACalendar Year - 2009COVTState

34. Data Visualization SystemClient access and distribution mechanisms can include:Static report viewers and browsersAd hoc query toolsReport writersModeling applicationsScorecard applicationsPortals and dashboardsDelivering data is a process of continuous business improvement:MonitorAnalyzePlan

35. Data VisualizationBasic Business IntelligenceCustom Business Intelligence Web PortalPowerPivot for SharePoint and Self Services BI

36. Basic Intelligence Static ReportsReport ServerCUBEExport Report to ExcelTechnology:ASP.NET

37. SQL Server 2008 R2Business User Tools:Excel 2007