1. ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES ANALYSIS SERVICES SQL SERVER SQL SERVER SQL SERVER SQL SERVER DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES INTEGRATION SERVICES Business Intelligence Architecture and Conceptual Framework INTEGRATION 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 SERVICES SQL SERVER SQL SERVER SQL SERVER SQL SERVER DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING DATA MINING

2. About Me Slava Kokaev Group Leader at Boston Business Intelligence User Group Principal BI Developer/ Architect at Industrial Defender vkokaev@bostonbi.org www.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 Norton Authors of “The Balanced Scorecard”

5. Enterprise BI Strategy and Vision To 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 Platform Business Intelligence System Management System Enterprise Data Warehouse System Business Analysis System Operational System

7. Microsoft’s BI platform COLLABORATION CONTENT MANAGEMENT SharePoint Server SEARCH Reports Dashboards Excel Workbooks Analytic Views Scorecards Plans END USER TOOLS & PERFORMANCE MANAGEMENT APPS Excel Power Pivot BI PLATFORM SQL Server Reporting Services SQL Server Analysis Services SQL Server DBMS SQL Server Integration Services

9. Data transformation and synthesis

10. Data enrichment, with business logic, hierarchical views

11. Data discovery via data mining

12. Data presentation and distribution

14. Sales Business Process Balance Scorecards Sales corrections and Improvement Plan Sales Sales Quota Stock Data Sale Orders (Facts /Measures) Resellers Sales Reseller (Dimension) Sales Result Monitor Sales Sales Summary Sales Transaction Analyze Sales SQL Server DB Sales Representative Sales Manager

15. Bike Factory TiresFactory Still Factory AdventureWorks Headquarter Plastic Factory Color Factory Accessory Factory Warehouse Resellers

16. ETL System OLTP SSIS DW OLAP

17. Enterprise Data Source Structure Call Center Web Apps CRM Inventory Finance Data Warehouse ERP HR

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

19. ETL Framework and Logical Architecture Check System State ETL Packages Extract Data File System Load Staging Extract from Staging OLTP STAGING Schema Send Notification Log ETL Process Transform Data Load Dimensions ETL Schema Load Facts DWH Schema Database Process Cube Cube

20. ETL Benefits Productivity Coding 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. Goals Implement many routines quickly, with limited developer resources Reliability and Accuracy Ability to introduce modify remove transformation rules Ability to maintain and apply logical business rules on data Support for scheduled and user-initiated package execution

22. Relational Data Warehouse Architecture and dimensional model ?

23. Star Schema vs. OLTP ETL

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

25. Dimensions Dimensions 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 Tables Each 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 model Delivers high-performance queries Delivers higher performing queries using Star Join Query Optimization Uses mature modeling techniques that are widely supported by many BI tools Requires low maintenance as the data warehouse design evolves

28. Vendors, Suppliers,Channel partners Customers Business partners Monitoring Systems Analysis Systems Business Processes and Operations Controlling Systems Strategy and Planning Systems IT providers Financial service providers Enterprise Business Analysis System

29. Business Process System Cycle Kaizen BPM

30. Abstract Functional Business Model IDEF0 Modeling Notation Feedback (Improvement) Plan Plans, Business Rule and KPI Input Data Process Output (Facts /Measures) Do Resources Check Result Data Act Data Mining Reporting Services SQL Server Analysis Services

31. Business Frameworks and Logical Business Levels

32. ON-LINE Analytical Processing Multidimensional databases are also called online analytical processing (OLAP) databases and… Contain structures optimized for rapid ad hoc information retrieval Pre-calculate and store aggregated values Include calculation engines for fast, flexible transformation of base data Are designed to reveal business trends and statistics not directly visible in the data retrieved from a data warehouse Data mining models discover patterns in data, typically for prediction analysis ProductAssociation Sales Finance Production

33. Understanding Cube Structure 1164 995 1893 1455 1945 1376 945 1553 1874 1245 1576 445 1479 1874 1245 2954 1575 1479 1576 3007 1575 2322 2954 1383 3007 2455 3007 Accessories 1654 Australia 645 1365 2145 645 988 2012 Product Line 2012 Country United States 845 Bikes 745 700 275 1082 234 905 Canada 905 345 Clothing Quarter 1 761 875 Quarter 2 745 745 France Components Semester 1 Quarter 3 Quarter 4 TX Semester 2 MA Calendar Year - 2009 CO VT State

34. Data Visualization System Client access and distribution mechanisms can include: Static report viewers and browsers Ad hoc query tools Report writers Modeling applications Scorecard applications Portals and dashboards Delivering data is a process of continuous business improvement: Monitor Analyze Plan

35. Data Visualization Basic Business Intelligence Custom Business Intelligence Web Portal PowerPivot for SharePoint and Self Services BI

39. Static SSRS Reports (IE 8)Interactive Analysis (Simple Pivot)

41. SQL Server 2008 R2

44. Self Services Reports

45. Interactive Dashboards (IE 8)

46. Customizable Dashboards (IE 8)

47. Pesonalizable DashboardsCUBE Export Report to Excel Dynamic Analysis (Pivot)

49. SQL Server 2008 R2

52. Interactive Reports (IE 8)

53. Interactive Dashboards (IE 8)

54. Customizable Dashboards (IE 8)

55. Pesonalizable DashboardsCUBE Export Report to Excel Dynamic Analysis (Pivot)

56. Introducing PowerPivot PowerPivot for Excel PowerPivot for SharePoint Analyzing Massive Data Volumes in Excel With a few mouse clicks, a user can create and publish intuitive and interactive self-service analysis solutions.

57. Interactive Slicing and Dicing Excel 2010 and Excel Services Interactive slicers enable users to look at the data from various directions in Excel 2010 and in the browser through PowerPivot for SharePoint and Excel Services.

59. Resources SQL Server 2008 Books Online,msdn2.microsoft.com/en-us/library/bb543165(sql.100).aspx The Microsoft Data Warehouse Toolkit by Joy Mundy, Warren Thornthwaite, and Ralph Kimball The Data Warehouse Lifecycle Toolkit by Ralph Kimball, et al.