Business Intelligence & Analytics E. Tom Owens Director IT Wah Chang

Todays Objectives Know definition of Business Intelligence (BI) Know the difference between BI and Data Warehousing How is BI derived - structure Examples for understanding Enterprise Performance Management Tools Q&A

<Insert Picture Here> Definition : business Intelligence

Definition: Business Intelligence A broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better decisions and reports. The term implies you have a complete understanding of your business. We must have a strong knowledge about all factors of your company including customers, competition, business partners, internal operations, and the economic environment to make effective and good quality business decisions. Business Intelligence allows you to make these kinds of decisions. The term BI was used as early as 1996 When Gartner Group said: By 2000, Information Democracy will emerge in forward-thinking enterprises, with Business Intelligence information and applications available broadly to employees, consultants, customers, suppliers and the public.

Quiz Question: What is business intelligence?

Pervasive Information Access Through a Unified BI Foundation Desktop Gadgets Ad-hoc Analysis Interactive Dashboards Search Reporting & Publishing Proactive Detection and Alerts Disconnected & Mobile Analytics MS Office & Outlook Integration Common Enterprise Information Model Integrated Security, User Management, Personalization Multidimensional Calculation and Integration Engine Intelligent Request Generation and Optimized Data Access Services OLTP & ODS Systems Data Warehouse Data Mart Essbase SAP, Oracle PeopleSoft, Siebel, Custom Apps Files Excel XML Business Process

What do we do with it? Enterprise Performance Management System Portals Data Mining Applications Desktop Tools Any JSR 168 Portal Oracle Data Mining, SPSS, SAS Oracle EBS, Siebel, SAP, PeopleSoft, JD Edwards .. Excel, Outlook, Lotus Notes .. Enterprise Performance Management System Business Intelligence Foundation Security Data Access Data Integration Hot-pluggable Oracle Kerberos iPlanet MSFT AD Novell Custom Others .. Oracle RDBMS Oracle OLAP Option Microsoft SQL Server & Analysis Services IBM DB2 Teradata Essbase SAP BW XML, Excel, Text Oracle Data Integrator (Sunopsis) Oracle Warehouse Builder Informatica Ascential Others ..

Ad-hoc Query Capability Comprehensive subject areas available for Ad-hoc analysis New Calculated Fields Available within Market Leading BI Toolset Easy to use Charting tool Formatting Widgets Highlighting Multi-language

Integrated Security Launch Oracle OBI EE dashboard from Application Navigator Shared responsibilities between Oracle EBS and Oracle OBI EE Sign in only once in EBS

BI Applications Add insight to CRM and ERP applications Order Management & Fulfillment Sales Service & Contact Center Marketing Procurement & Supply Chain Financials Human Resources Add insight to CRM and ERP applications Easy to adapt and extend Works with existing IT environment Low TCO BI Server Common Enterprise Information Model Multi-source, and combine across sources Other Data Sources IVR, ACD, CTI Hyperion MS Excel Syndicated

BI Applications Multi-Source Analytics with Single Architecture Auto Comms & Media Complex Mfg Consumer Sector Energy Financial Services High Tech Insurance & Health Life Sciences Public Sector Travel & Trans Sales Service & Contact Center Churn Propensity Customer Satisfaction Resolution Rates Service Rep Effectiveness Service Cost Analysis Service Trends Marketing Order Management & Fulfillment Supply Chain Financials Human Resources Pipeline Analysis Triangulated Forecasting Sales Team Effectiveness Up-sell / Cross-sell Cycle Time Analysis Lead Conversion Campaign Scorecard Response Rates Product Propensity Loyalty and Attrition Market Basket Analysis Campaign ROI Order Linearity Orders vs. Available Inventory Cycle Time Analysis Backlog Analysis Fulfillment Status Customer Receivables Supplier Performance Spend Analysis Procurement Cycle Times Inventory Availability Employee Expenses BOM Analysis A/R & A/P Analysis GL / Balance Sheet Analysis Customer & Product Profitability P&L Analysis Expense Management Cash Flow Analysis Employee Productivity Compensation Analysis HR Compliance Reporting Workforce Profile Turnover Trends Return on Human Capital Other Operational & Analytic Sources Prebuilt adapters: BI Suite Enterprise Edition

<Insert Picture Here> Value of Pre-built BI Applications

Change the Economics of BI Build from Scratch with Traditional BI Tools Oracle BI Applications Training / Roll-out BI Applications solutions approach: Faster time to value Lower TCO Assured business value Define Metrics & Dashboards DW Design Back-end ETL and Mapping Training / Rollout Easy to use, easy to adapt Define Metrics & Dashboards Role-based dashboards and thousands of pre-defined metrics DW Design Prebuilt DW design, adapts to your EDW Back-end ETL and Mapping Prebuilt Business Adapters for Oracle, PeopleSoft, Siebel, SAP, others Months or Years Weeks or Months Source: Patricia Seybold Research, Gartner, Merrill Lynch, Oracle Analysis

EXAMPLE OF BI IN ACTION

AFLAC GOOSE FARM PROFIT ANALYSIS FOR 2007 GOOSE FOOD POISON WHAT CAN CAUSE A DEVIATION 68% CORRECT ON PROFITS 95% CORRECT? WHAT COULD CAUSE IT TO GO S GEESE DIE, COST MORE AND SLOW YOU DOWN RAW MATERIAL- GOT SUED - MACHINE BROKE - NEW SYSTEM

<Insert Picture Here> Technical Overview

Oracle BI Applications Architecture Administration Oracle BI Presentation Services Dashboards by Role Reports, Analysis / Analytic Workflows Metadata Role Based Dashboards Analytic Workflow Guided Navigation Security / Visibility Alerts & Proactive Delivery Metrics / KPIs Logical to Physical Abstraction Layer Calculations and Metrics Definition Visibility & Personalization Dynamic SQL Generation Logical Model / Subject Areas Oracle BI Server Physical Map Direct Access to Source Data Data Warehouse / Data Model Abstracted Data Model Conformed Dimensions Heterogeneous Database support Database specific indexing ETL Load Process Staging Area Extraction Process DAC Highly Parallel Multistage and Customizable Deployment Modularity Oracle SAP R/3 Siebel PSFT EDW Federated Data Sources Other

Oracle BI Presentation Services Reports, Analysis / Analytic Workflows ETL Overview Administration Oracle BI Presentation Services Dashboards by Role Reports, Analysis / Analytic Workflows Metadata Three approaches to accessing / loading source data Batch ETL Low Latency ETL Direct access to source data from Server ETL Layered architecture for extract, universal staging and load Provides isolation, modularity and extensibility Ability to support source systems version changes quickly Ability to extend with additional adapters Slowly changing dimensions support Architected for performance All mappings architected with incremental extractions Highly optimized and concurrent loads Bulk Loader enabled for all databases Datawarehouse Application Console (DAC) Application Administration, Execution and Monitoring (ETL-Extract, transform, load) Metrics / KPIs Logical Model / Subject Areas Oracle BI Server Physical Map Direct Access to Source Data Data Warehouse / Data Model ETL Load Process Staging Area Extraction Process DAC DAC Key ETL components and features we support Batch ETL (Extract, Transform, Load) Recently Low Latency ETL ie to refresh the warehouse with a very low latency The ETL process is divided into 2 major sub-processes Extraction Load The architecture seeks to keep all source specific logic in the extract layer Thus supporting a new version or a new adapter requires writing only extract layer Over the years we have done significant performance tuning on various areas for all platforms in terms of use of bulk loaders/database specific indexing etc Lastly DAC is a critical tool for us used for application installation, administration, ETL scheduling and monitoring Oracle SAP R/3 Siebel PSFT EDW Federated Data Sources Other

Data Warehouse Application Console (DAC) DAC is a metadata driven administration and deployment tool for ETL and data warehouse objects Used by warehouse developers and ETL Administrator Application Configuration Manages metadata-driven task dependencies and relationships Allows creating custom ETL execution plans Allows for dry-run development and testing Execution Enables parallel loading for high performance ETL Facilitates in index management and database statistics collection Automates change capture for Siebel OLTP Assists in capturing deleted records Fine grain restartability Monitoring Enables remote admin and monitoring Provides runtime metadata validation checks Provides in-context documentation DAC Background – There are a variety of things to be deployed and managed in a data warehouse DW objects (tables/indices) ETL Code Seed data Variety of configurations for ETL etc This is our tool to administer/install/maintain and monitor ETL runs Also used by ETL administrators in production to monitor and restart jobs Significant TCO and performance improvements since DAC was released. Several customers have improved their ETL run timings several times

Physical Data Model Overview Administration Oracle BI Presentation Services Dashboards by Role Reports, Analysis / Analytic Workflows Metadata Modular enterprise-wide data warehouse data model with conformed dimensions Sales, Service, Marketing, Distribution, Finance, Workforce, Operations and Procurement Integrate data from multiple data sources Code Standardization Real-time ready Transaction data stored in most granular fashion Tracks historical changes Supports multi-currency, multi-languages Implemented and optimized for Oracle, SQL Server, IBM UDB/390, Teradata Metrics / KPIs Logical Model / Subject Areas Oracle BI Server Physical Map Direct Access to Source Data Data Warehouse / Data Model ETL Load Process Staging Area Extraction Process DAC The data model is a dimensional model Can load data from multiple sources One of the key methods to conform data from various sources is “code standardization” ie define and convert source specific codes into a normalized warehouse code. Eg Invoice Types etc Supports all 4 major databases and has been tested and optimized for all of them Oracle SAP R/3 Siebel PSFT EDW Federated Data Sources Other

Integrated Enterprise Analytics Data Model Service Customers Sales Marketing Distribution Finance HR / Workforce Operations Procurement Suppliers Features: Conformed dimensions Transaction data stored in most granular fashion Tracks full history of changes Prebuilt and extensible Built for speed Benefits: Enterprise-wide business analysis (across entire value chain) Access summary metrics or drill to lowest level of detail Accurate historical representations In this slide, talk about features and benefits of single comprehensive enterprise data model and how it enables “cross-value-chain” analytics Our solution provides that capability by something called “Conformed Dimensions” as we discussed earlier. They are shared by the fact tables in each of the different areas, and enables what we call “cross-value-chain” analytics. For example if you had only a “Sales” analytics system, how would you derive a metric on say “profitability by product” or “profitability by sales area”. It would take significant additional effort to get those answers. It is automatically provided by our solution. This is a key advantage over other vendor solutions on the market that only address a specific functional silo of information. This is very limiting for many of the metrics that will be required. As another differentiator from other solutions, particularly those based on cube architectures, the detailed transaction data is also stored in the data model. It is stored at the lowest level possible. For example, we take “sales quantity” and even match it to “delivery schedule”. Then we match the real deliveries to that schedule. That way we can track “on-time” deliveries. Having the detail data in the data model also enables quick and easy drill-down capability to get the details you will need in performing data analysis. The model enables complete history tracking, by supporting the Slowly Changing Dimensions.

Selected Key Entities of Business Analytics Warehouse Sales Opportunities Quotes Pipeline Order Management Sales Order Lines Sales Schedule Lines Bookings Pick Lines Billings Backlogs Marketing Campaigns Responses Marketing Costs Supply Chain Purchase Order Lines Purchase Requisition Lines Purchase Order Receipts Inventory Balance Inventory Transactions Finance Receivables Payables General Ledger COGS Call Center ACD Events Rep Activities Contact-Rep Snapshot Targets and Benchmark IVR Navigation History Service Service Requests Activities Agreements Workforce Compensation Employee Profile Employee Events Pharma Prescriptions Syndicated Market Data Financials Financial Assets Insurance Claims Public Sector Benefits Cases Incidents Leads Conformed Dimensions Customer Products Suppliers Internal Organizations Customer Locations Customer Contacts GL Accounts Employee Sales Reps Service Reps Partners Campaign Offers Cost Centers Profit Centers This slide provides an overview of the width of content in the warehouse, going all the way from Marketing/Sales to Backoffice and HR analytics There is a single logical model on the warehouse, enabling cross process metrics and calculations Examples of processes Sales  Lead to order; Order to cash Supply Chain  Requisition to Check; Dock to Stock Service Open to close a trouble ticket Call Center  Origination to termination of a call, whether handled by a person, a machine, or both Financials  used to keep score Let's discuss these processes in more detail. As we go through them, you'll see all the way that Analytics can add value. Modular DW Data Warehouse Data Model includes: ~350 Fact Tables ~550 Dimension Tables ~5,200 prebuilt Metrics (2,500+ are derived metrics) ~15,000 Data Elements

Example: Sales Order Lines Fact Table Sales Order Details Cust. Location Sold to / Ship to / Bill to Example Metrics # of Cancelled Order Lines # of Customers # of First Customers # of Order Lines # of Orders # of Products # of Returned Order Lines % Order Discount Average # of Products per Order Average Order Size Cancelled Amt / Qty Orders to Booking Close Rate Outstanding Booking Amt / Qty Total Ordered Amt / Qty Total Return Amt / Qty Employee Sales Channel Sales Order Lines Customers Products Mfg / Sales / Supplier Locations Plant / Mfg Ship / Storage Sales Orgs This is an example of one of the logical models with its dimensionality. Richness of the stars are important because it gives more analysis insight. There are ~150 of them Payment Terms EAI Date ETL Features Includes 27 logical dimensions and 33 out of the box metrics Provides ability to do detailed analysis of sales order lines Data stored at transaction grain and at line level

Server Repository Overview Administration Oracle BI Presentation Services Dashboards by Role Reports, Analysis / Analytic Workflows Metadata Multi-layered Abstraction Separation of physical, logical and presentation layers Logical modeling builds upon complex physical data structures Logical model independent of physical data sources, i.e. same logical model can be remapped quickly to another data source Metrics / KPIs Multi-pass complex calculated metrics (across multiple fact tables) One Logical Fact can span several table sources including aggregates and real-time partitions Level based metrics Aggregate navigation Federation of queries Security and visibility Prebuilt hierarchy drills and cross dimensional drills Metrics / KPIs Logical Model / Subject Areas Physical Map Oracle BI Server Direct Access to Source Data Data Warehouse / Data Model ETL Load Process Staging Area Extraction Process DAC Move the focus to Oracle BI Applications Repository Metadata To re-emphasize, Analytics server is model centric and report centric. This is the place where the model is built and not reports/dashboards The model consists of Logical Model Has all dimensions/facts/metrics Physical Model Logical to Physical Mappings Aggregates/Federation (Revenue for employee) Security and Visibility Dimensions and drills etc Oracle SAP R/3 Siebel PSFT EDW Federated Data Sources Other

Tools and Web Catalog Overview Administration Oracle BI Presentation Services Dashboards by Role Reports, Analysis / Analytic Workflows Metadata Role based dashboards Covering more than 100 roles Navigation Most reports have at least one level of navigation embedded Drill to details from many interactive elements, e.g. chart segments Guided Navigation Conditional navigational links Analytic Workflows Action Links Direct navigation from record to transactional while maintaining context Alerts Scheduled and Conditional iBots Highlighting Conditional highlighting that provides context on metrics (is it good or bad?) Metrics / KPIs Logical Model / Subject Areas Oracle BI Server Physical Map Direct Access to Source Data Data Warehouse / Data Model ETL Load Process Staging Area Extraction Process DAC The key end user delivery mode is dashboards besides the ad-hoc tool called Answers which is used by more savvy users The dashboards are very interactive and alive (not static html) pages. There are various ways a user interacts to a dashboard Navigation, ie jump to another report or view with the context Guided Navigation – conditional navigation which appears on fulfillment of some condition Action Links : to transactional system Alerts : Delivered via scheduler ie scheduled reports Highlighting etc Oracle SAP R/3 Siebel PSFT EDW Federated Data Sources Other

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Today’s Objectives Know definition of Business Intelligence (BI) Know the difference between BI and Data Warehousing How is BI derived - structure Examples for understanding Enterprise Performance Management Tools 32

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