1 Copyright © 2009, Oracle. All rights reserved. Oracle Business Intelligence Enterprise Edition: Overview

Copyright © 2009, Oracle. All rights reserved Objectives After completing this lesson, you should be able to: Define and describe business analytics and business intelligence Identify the analytical business challenge and the solution provided by Oracle BI products Define and describe data warehousing and data modeling Identify and describe the Oracle BI Enterprise Edition products used to support business intelligence requirements Why you need to know: Provides a big-picture overview to set the context for the remainder of the course

Copyright © 2009, Oracle. All rights reserved What Is Business Intelligence? Provides users the data and tools to answer questions that are important to running the part of the business for which they are responsible: Determine whether the business is on track. Identify where things are going wrong. Take and monitor corrective actions. Spot trends. Examples include: Show me sales for each district by month. Show me the average sales amount for this quarter. Compare sales this quarter with sales a year ago. Show me the lowest-ranked salesperson by region. Show me the lowest-ranked product this year.

Copyright © 2009, Oracle. All rights reserved Business Intelligence Challenges Differing requirements Ineffective tools Large and changing data volumes

Copyright © 2009, Oracle. All rights reserved Large and Changing Data Volumes Large amounts of data need to be accessed to provide meaningful results. –Data may reside in many systems. –Results may require accessing millions of records. –Data volumes are ever-increasing. Data requires changes based on business requirements. Data organization may make access difficult, time-consuming, and resource intensive.

Copyright © 2009, Oracle. All rights reserved Differing Requirements Based on the role of the user, different questions need to be answered. Level of data detail required varies. –Summarized data is appropriate for executives, but details are required at lower levels.

Copyright © 2009, Oracle. All rights reserved Ineffective Tools Analysis tools are often difficult to master, hard to use, and specialized. They: May require detailed knowledge of the data layout and special syntax May require manual consolidation of results from multiple sources Are often complex and single-purpose: query versus analysis Reporting tools are often static or fixed and do not allow for interactivity: Questions may be asked, but cannot be answered. Drill down is often impossible, making causes difficult to determine.

Copyright © 2009, Oracle. All rights reserved Solution: Oracle BI Enterprise Edition Provides insight, processing, and prebuilt solutions that allow users to seamlessly access critical business information and acquire the business intelligence to achieve optimal results Assess Identify Act Diagnose Optimal Results

Copyright © 2009, Oracle. All rights reserved Oracle BI Enterprise Edition Is a next-generation business intelligence platform: Provides optimized intelligence to take advantage of relational database technologies –Accesses data regardless of its organization or layout Leverages and extends common industry techniques –Data warehousing –Dimensional modeling

Copyright © 2009, Oracle. All rights reserved Data Warehousing Brings together data from many sources Organizes data for analytical processing –Denormalize data: Duplicate and flatten data structures –Reduce joins: Reduce the number of tables and relationships –Simplify keys: Use surrogate keys such as a sequence number –Employ star schemas: Simplify relationships between tables

Copyright © 2009, Oracle. All rights reserved Transactional Versus Analytical Systems Transactional SystemAnalytical System Database  Manages individual transactions  Write-intensive  Constant updates, inserts, and deletes  Queries return small datasets  Little data aggregation  Reports require calculation  Data optimized for storage and read/write performance  Data relatively normalized  Multiple table joins  Responds to analysis queries  Read-intensive  Static data loads  Queries return large datasets  Data highly aggregated  Data precalculated for reporting  Data optimized for query performance  Data denormalized, flattened  Minimal table joins Use  Data entry  Data retrieval  Reports  Charts and pivot tables

Copyright © 2009, Oracle. All rights reserved Transactional Versus Analytical Systems Database Schema Transactional schema optimized for read/write; multiple joins Analytics schema optimized for querying large datasets; few joins

Copyright © 2009, Oracle. All rights reserved Star Schema A star schema organizes data into a central fact table with surrounding dimension tables. Each dimension row has many associated fact rows. The dimension tables do not relate to each other. Dimension Fact

Copyright © 2009, Oracle. All rights reserved Fact Contains business measures or metrics –Data is often numerical. Is the central table in the star schema Dimension Fact Dollars Units Shipments

Copyright © 2009, Oracle. All rights reserved Dimension Contains attributes or characteristics about the business –Data is often descriptive (alphanumeric). Qualifies the fact data Customer ProgramProduct Time Sales Name Address Name Product Line Month Year Name Format

Copyright © 2009, Oracle. All rights reserved User-Friendly Models the way users think about data Enables data to be understood and analyzed Customer ProgramProduct Time Sales Dollars, Units, Shipments by Customer Dollars, Units, Shipments by Product Dollars, Units, Shipments by Program Dollars, Units, Shipments by Time

Copyright © 2009, Oracle. All rights reserved Star Schema: Example Sales fact table with dimension tables and relationships: SALES FACT ROW_WIDCUST_IDPER_IDPROD_IDQTY_ORDEREDQTY_SHIPPEDAMT CUSTOMER DIMENSION ROW_WIDCUST_NAMEOTHER 17023A. K. Parker Betta Builders CostCutter Stores... PRODUCT DIMENSION ROW_WIDPROD_NAMEOTHER 12091Widget Super Widget Lite Widget... PERIOD DIMENSION ROW_WIDDATEOTHER /1/ /1/ /1/ Fact table contains measures to be analyzed. Dimension tables contain characteristics that qualify the facts.

Copyright © 2009, Oracle. All rights reserved Dimensional Modeling It is a technique for logically organizing business data in a way that helps end users understand it. Data is separated into facts and dimensions. Users view facts in any combination of the dimensions. It allows users to answer “Show me X by Y by Z” type questions. Example: Show me sales by product by month. Fact Dimension

Copyright © 2009, Oracle. All rights reserved Oracle BI Enterprise Edition Platform Is an engine that provides core business intelligence and analytics capabilities Is a platform to model data so that users can understand it Is a server to generate SQL and seamlessly access and manipulate data from multiple sources Is simple to use, highly interactive, Web-based analysis tool and has the ability to preconstruct dynamic reports and alerts

Copyright © 2009, Oracle. All rights reserved Oracle BI Applications Provides all that the Oracle BI Platform does, plus: Applications for common industry analytical processing, such as Service Analytics, Sales Analytics, Pharma Analytics, and so on Prebuilt role-based dashboards and requests to support the needs of everyone from line managers to chief executive officers A prebuilt database designed for analytical processing with prebuilt routines to extract, load, and transform data from transactional systems such as Oracle’s Siebel CRM application.

Copyright © 2009, Oracle. All rights reserved Summary In this lesson, you should have learned how to: Define and describe business analytics and business intelligence Identify the analytical business challenge and the solution provided by Oracle BI products Define and describe data warehousing and data modeling Identify and describe the Oracle BI products used to support business intelligence requirements

Copyright © 2009, Oracle. All rights reserved