Optimize Oracle Applications Performance while Lowering Costs An

Optimize Oracle Applications Performance while Lowering Costs An Agilent Case Study Presenters. Kevin Barry Kevin O’Malley Outer. Bay Technologies, Inc.

Agenda • Introduction to Agilent Corporation • The Data Growth Problem • Outer. Bay ADM Suite • The Agilent Solution • Results & Benefits • Q &A Outer. Bay Confidential

About Agilent • Global technology leader in communications, electronics, life sciences and chemical analysis • Revenue - $7 Billion in Revenues • Employees - 29, 000 World Wide • Locations - Headquartered in Palo Alto, CA - 30 Facilities worldwide • Primary businesses - Test and Measurement - Automated Test - Semiconductor Products - Life Sciences and Chemical Analysis Outer. Bay Confidential

Agilent’s Oracle E-Business Environment • Oracle E-Business Suite 11 i • • Modules: Entire ERP Suite Single global instance Consistent, real-time view across all business units Common business processes HP Superdome (64 CPUs) Go-live in July 2002 Application OLTP production Data Growth • 92 GB/month • OLTP instance copied ~ 26 times for test/development • Growth expected to increase with additional plants coming online Outer. Bay Confidential

Agenda • Introduction to Agilent • The Data Growth Problem • Outer. Bay ADM Suite • The Agilent Solution • Results & Benefits • Q &A Outer. Bay Confidential

The Problem Performance issues in production Ø Strong relationship between DB size and OLTP / Batch performance Storage capacity required was explosive ØData growth was more than Oracle predicted ØTotal disk capacity requirements to exceed 26 TB in one year Business continuity challenges ØIncrease in backup and recovery times ØHigher DR/HA costs Outer. Bay Confidential

Data Growth vs. Performance 1200 Application Data 1100 1000 900 800 700 600 Batch Run Time 500 400 Outer. Bay Confidential Application Data (GB) Data Growth Impact Application Data (GB) Total Batch Run Time (Hrs/Month) 1300

Data Growth Alternatives • Ask Oracle for assistance ØServices engagement • Custom/in-house solution ØMajor project – year+ to deploy • Archive vendor search ØSelected approach Outer. Bay Confidential

Agilent - Key Requirements §No disruption to the business § Uncompromised end-user reporting § No change to business processes § Guaranteed data and transaction integrity § Aggressive Data Retention Policies § True 24 x 7 Operations – Zero downtime allowed § Automation § Oracle Certification Outer. Bay Confidential

Agilent Selects Outer. Bay • Suite of proven solutions • Highly automated • Oracle certification • Online/transaction integrity • Experience • Ready to Implement Outer. Bay Confidential

Agenda • Introduction to Agilent • The Data Growth Problem • Outer. Bay ADM Suite • The Agilent Solution • Results & Benefits • Q &A Outer. Bay Confidential

Application Data Management Support & Projects Production Integration Test Disaster Recovery Unit Test Local Standby Development Disk Backup Archive Reporting X(n) Production Parallel Initiatives Application Resource Monitor Policy & Configuration Console Live. Archive Encapsulated Archive Developers Edition Outer. Bay Confidential Instance Generator

Data Life Cycle Example Data Retention Policies 6 months-2 Years 2 -7 Years Application Transparency 7 – 25+ Years 3 rd Party Reporting Tools Encapsulated Archive. XSD Production . XML History • Active OLTP • Online • Sustained growth & perf. • Historical transactions • Online • Application dependent • Archive flat file • XML Query access • Open / Independent Storage Class High Cost High Availability Secondary Outer. Bay Confidential Least Expensive

Applications Resource Monitor • Discover areas of high and low data growth • Decision Support tool for data retention policies • Use across all instances and database applications Database. Xtender Outer. Bay Confidential

Data Growth Analysis TTE 125 100 Relocation Space 75 (GB) 50 25 0 Jan May Sep 2002 2003 Jan May Sep 2004 • Identify active vs. inactive data • Build data relocation into the business process • Implement based on predictable storage requirements Outer. Bay Confidential Jan May Sep 2005 Eligible Forecast Ineligible Forecast

Key Features • Combined View Reporting – native reports/queries • Data Growth and Process Monitoring • Data Parity Support • Database Reorganization Support • Online Operations (users stay on the system) • Concurrent Manager integration (or 3 rd party scheduling tools) • Fully Recoverable/Restartable • Full audit trail – repository-based • Reload Support Outer. Bay Confidential

Outer. Bay Platform Live. Archive Encapsulated Archive Active Repository Application Resource Monitor Instance Generator Developer Edition Outer. Bay Confidential

Agenda • Introduction to Agilent • The Data Growth Problem • Outer. Bay ADM Suite • The Agilent Solution • Results & Benefits • Q &A Outer. Bay Confidential

Agilent Approach Two-tiered Solution • Test and Development Environments - Outer. Bay Instance Generator: Relationally intact subsetting - 11 Modules implemented » INV, COST, WIP, AP/PO, GL, AR, Quotes, Workflow, Cash Mgmt, Order Mgmt, Cycle Count • Production Environment - Outer. Bay Live. Archive: Online Archiving - 9 Modules implemented » INV, COST, WIP, AP/PO, GL, Quotes, Workflow, Cash Mgmt, Supplier Schedules Outer. Bay Confidential

Agilent Approach – Instance Generator Test and Development Environments Patch Policy Sets • Create fully functional subset databases for development, test, UAT, training, and demo • Policies selected by time Production OLTP Copy Training Dev Outer. Bay Confidential

Agilent Approach - Live. Archive Best Practices in Data Retention Policies Archive Module Data Retention in Production Archiving Frequency Inventory 2 Months + Current Monthly Cost History 2 Months + Current Monthly WIP 2 Months + Current Monthly Supplier Schedules 2 Months + Current Monthly AP/Procurement 2 -3 Months after PO gets finally closed Monthly Cash Management 6 Months + Current Monthly GL 12+2 Months of adjustment period, after a period is permanently closed Monthly Quote Expired Quotes, older than 9 months will get deleted Monthly Workflow Delete workflows older than 15 days as part of regular Oracle workflow purge Monthly Outer. Bay Confidential

Agenda • Introduction to Agilent • The Data Growth Problem • Outer. Bay ADM Suite • The Agilent Solution • Results & Benefits • Q &A Outer. Bay Confidential

The Results Delivered by Instance Generator • $ Savings for Storage - Subsets for Test, Dev, and Training = 30% of Production » 500 GB from 1. 8 TB - Current usage » Patch, Training - Planned usage » 11. 5. 10 Upgrade testing: 10+ copies • IT Process Streamlined - 17% reduction in subset creation times Outer. Bay Confidential

The Results Expected Benefits • Defer investments in hardware upgrades • Decrease TCO for E-Business Suite • Minimal user impact/end user training Outer. Bay Confidential

The Results Delivered by Outer. Bay Live. Archive • $ Savings for Storage: 2. 5% of total 2005 IT budget - Number of rows archived = ~ 500 Million 500 GB in storage savings in Production Total storage savings = 13 TB Agilent can now defer disk spending for 1 year • Related Savings - HA, Backup & Recovery Outer. Bay Confidential

The Results Unexpected Benefits • Data growth management stable, predictable performance management - Take guesswork out of capacity planning - Free up resources for business initiatives - Accelerate critical business processes Outer. Bay Confidential

Data Growth Impact Phase II Tuning Sustained Predictable Performance Live. Archive Phase I Tuning Outer. Bay Confidential Application Data (GB) Total Batch Run Time (Hrs/Month) Performance Benefits

Business Benefits Example: Shipping Sustained Predictable Performance Outer. Bay Confidential Average Run time (Min) Total Batch Run Time (Hrs/Month) Pick Selection List Generation

Business Benefits Example: OM Sustained Predictable Performance Outer. Bay Confidential Average Run time (Min) Total Batch Run Time (Hrs/Month) Order Import

Business Benefits Example: AP Sustained Predictable Performance Outer. Bay Confidential Average Run time (Min) Total Batch Run Time (Hrs/Month) Payables Approval

Agilent Approach Lessons Learned – Implementation Project • You can NEVER START THIS TOO EARLY!!! • Set the plan by largest “wins” first and get buy-off immediately • Get message out that data is NOT being purged; just relocated - Users retain transparent access and combined reporting • Engage key business groups early, establish as a critical business project: Finance, Audit, etc… • Strong team work between IT and Business teams required • Incremental rollout enabled the project team to - Refine process - Enforce accountability Outer. Bay Confidential

• “Outer. Bay Software has provided Agilent with a long-term positive business impact with an extraordinary, immediate ROI !” - Naresh Shanker, Agilent Outer. Bay Confidential