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

2. 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

3. <Insert Picture Here>
Definition : business Intelligence

4. 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.

5. Quiz
Question:
What is business intelligence?

6. 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

7. 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 ..

8. 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

9. 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

10. 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

11. 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

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

13. 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

14. EXAMPLE OF BI IN ACTION

16. 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

17. <Insert Picture Here>
Technical Overview

18. 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

19. 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

20. 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

21. 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

22. 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.

23. 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

24. 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

25. 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

26. 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

27. ENTERPROSE PERFORMANCE MANAGEMENT TOOLS

28. You can drill down

32. 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

33. QUESTIONS