1. Introduction to Building a BI Solution 권오주 OLAPForum www.olapforum.com

2. Agenda Introduction to Building a BI Solution Defining Data Warehouse Structures Populating the Data Warehouse Creating Simple and Parallel Data Loads Copying, Managing, and Transforming Data Storing, Managing, and Executing Packages

3. Agenda Building Cubes Understanding Analysis Services Architecture Designing OLAP Dimensions Using Measures Implementing Calculations Using MDX Applying Virtual Cubes Case Study

4. Agenda Designing Aggregations Processing Cubes and Dimensions Optimization and Performance Tuning Advanced Analytical Features and Security Using Office for Reporting and Analysis Jumpstarting BI Solutions with SSABI

5. Understanding Business Intelligence (BI) The Primary Goal of BI Is to Impact the Bottom Line Strategic decisions protect and enhance competitive advantage Tactical decisions manage and measure specific operations or employee behavior BI Requires the Integration of Several Components Data that companies collect in its daily operations Technology which collects and organizes data People who can analyze data and make effective decisions

6. Components of Multidimensional Analysis Measures Numeric values that are of interest to business analysis Base measures, Key Performance Indicators (KPIs), and benchmark metrics Dimensions Categorical view of measures All members of a dimension belong together as a group

7. Single-Dimension View Each view appears homogenous

8. Multi-Dimension View Interaction reveals variations UnitsRegion ProductQuarterAtlantaChicagoDenverDetroitGrand Total ApplesQtr 14000 Qtr 2250015004000 Apples Total4000250015008000 CherriesQtr 1100030004000 Qtr 22000 4000 Cherries Total100030002000 8000 GrapesQtr 1150025004000 Qtr 2100030004000 Grapes Total10003000150025008000 MelonsQtr 12000 4000 Qtr 22000 4000 Melons Total2000 8000 Grand Total8000 32000

9. Benefits of Multidimensional Analysis Useful approach for viewing information Allows for flexible business analysis Ability to analyze measures simultaneously categorized across many dimensions Especially powerful when measures and dimensions include combined data from multiple data sources

10. OLTP Data Sources OLTP System Characteristics Processing real-time transactions of a business Containing data structures optimized for entries and edits Providing limited decision support capabilities OLTP System Examples Order tracking Customer service Service-based sales Banking functions

11. Silos of Data Call Center Call Center Marketing Campaign Mgmt Marketing Campaign Mgmt CRM and eCRM Internet Financial/ Accounting Procurem ent HR Inventory Data Warehouse

12. Data Warehouse System Characteristics Presents Data for Business Analysis Processes Provides a Consistent Historical Data Store Stores Data in Structures that Are Optimized for Extraction and Querying Integrates Data from Heterogeneous Source Systems Combines Validated Source Data Organizes Data into Non-Volatile, Subject-Specific Groups

13. Data Warehouse System Components Data Warehouse Data Access End User Data Access Data Sources Staging Area Data Marts Data Extract, Transform, and Load

14. The Microsoft Data Warehousing Framework

15. Microsoft SQL Server 2000 Relational Database Management Systems Contain and manage large quantities of data Foundation of a data warehouse SQL Server 2000 Roles Online Transaction Processing System Data Staging Data Warehouse Data Mart

16. Data Transformation Services Extract, Transform, Load, and Management (ETLM) Tools Extract data from heterogeneous source systems Transform source data to load into a destination Data Transformation Services Copies and transforms data from a variety of sources Creates reusable transformations and functions Automates data loads

17. Analysis Services 2000 Online Analytical Processing (OLAP) Databases Provide an intuitive, multidimensional view of data Provide fast data retrieval Robust calculation engines Analysis Services 2000 Creates multidimensional cubes Optimizes aggregations to provide rapid response Supports multidimensional expressions (MDX) to retrieve and manipulate multidimensional data Includes PivotTable service, an OLE db-compliant provider, for reporting applications

18. Extensible Markup Language for Analysis XML/A is a Data Access Protocol Extending BI to Microsoft.NET Platform Supports Exchange of Analytical Data Between Clients and Servers Available on any device or platform Using any programming language

19. End User Data Access End User Applications Data Access and Distribution Mechanisms Ad-hoc Query Tools Report Writers Modeling Applications Portals and Dashboards

20. Excel for Business Intelligence Excel Provides a Familiar Interface for Data Analysis OLAP PivotTables and PivotCharts Allow Access to Large Data Sets Offline Cube Files Allow Analysis When Disconnected from the Network

21. Office Web Components for Business Intelligence OWC Delivers PivotTable and PivotChart Functionality to Web OWC Facilitates Flexible, Customizable Solutions

22. Data Analyzer for Business Intelligence Data Analyzer Adds Rich Visualization and Analysis Capabilities Integration with Excel Facilitates Exploration Before In-Depth Analysis Full Support for OLAP features in Analysis Services

23. SharePoint Portal Server Organizing Documents Finding Documents Implementing Approval Processes Ensuring Document Security Searching for Documents Collaboration and Update Notification Providing Scalability at the Enterprise Level

24. Microsoft SQL Server Accelerator for BI Extension of Microsoft BI Platform Used to Build Customizable BI Solution SQL Server Accelerator for BI Components Analytics Builder Workbook to configure data model Analytics Builder utility to create an analytical application based on the configured data model Templates for business analysis with front-end tools

25. Imagine the Possibilities