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Upgrade to Oracle E-Business Suite R12 While Controlling the Impact of Data Growth

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Table of Contents Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 The Causes and Effects of Accelerated Data Growth in R12 Upgrades . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 New R12 Functionality Adds New Volumes of Data . . . . . . . . . . . . . . . . . . . . . . . 3 More Production Copies Needed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Legacy 11i Environments Retained Longer Than Expected . . . . . . . . . . . . . . . . 4 Data Growth Gets Replicated to the Data Warehouse . . . . . . . . . . . . . . . . . . . . 4 Disaster Recovery and Test Copies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Common Ways to Handle Data Growth . . . . . . . . . . . . . . . . . . . . . 5 Upgrade Infrastructure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Purge Excess Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Quantify Inactive Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Better Way to Handle Data Growth: Lean Data Management . . 6 Purge Interface and Intermediate Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Partition Active and Inactive Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Compress and Archive Inactive Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Tier Infrastructure to Maximize Resources and Minimize TCO . . . . . . . . . . . . . 7 Only Clone Partitions or Production Data Necessary for Testing . . . . . . . . . . . 7 Refresh Backups and Disaster Recovery Copies . . . . . . . . . . . . . . . . . . . . . . . . . 7 Integrate Compliance with Retention Regulations . . . . . . . . . . . . . . . . . . . . . . . . 8 Create Lean Data Warehouses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Lean Data Management Automation with Informatica Application ILM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Upgrade to Oracle E-Business Suite R12 While Controlling the Impact of Data Growth

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Executive Summary IT is constantly challenged to keep up with ever changing business requirements and the need to modernize enterprise applications. There are many obstacles that can prevent Oracle E-Business Suite application owners from helping the business realize the full potential of these investments. The Enterprise Strategy Group (ESG) research firm conducted a survey asking more than 300 respondents about their top data management challenges of managing database and the underlying infrastructure. The top two responses were 1. Managing data growth and database size 2. Keeping up with database performance Many Oracle customers are running a previous version of the Oracle E-Business Suite application, such as 11.5.10, and are in early planning phases of moving to R12. With extended support for 11i version on the 10g database ending, IT organization needs to solidify plans for the R12 upgrade. Those already considering an upgrade to R12 will face an increase in data volume footprint and growth rates. With the added functionality in modules such as financials, more data gets generated and stored in the underlying schemas potentially having a significant impact on overall performance and total cost of ownership. Written expressly for application owners responsible for the overall Oracle E-Business Suite and the R12 upgrade project, this white paper discusses: • The causes and effects of accelerated data growth in an R12 upgrade • Common ways to handle data growth • Best practices, such as lean data management, to assist in controlling data growth • How to automate lean data management with Informatica® Application Information Lifecycle Management™ (ILM) software

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The Causes and Effects of Accelerated Data Growth in R12 Upgrades Oracle recommends that 11i customers plan for at least a 20-percent increase in hardware requirements for storage, compute power, and memory when upgrading to R12. The reasons more hardware resources are required include: • More data gets generated as a result of new application functionality

An R12 upgrade can cause production and all production support copies to grow by 20 percent or more.

• During the upgrade, additional copies of production are needed to conduct testing • Experience illustrates that many keep the legacy version of 11i running longer than necessary • Increased demand on transaction processing applications and operational analytics • Multiplier effect of nonproduction and disaster recovery copies IT organizations reach a point during the upgrade where they scramble for disc space and extra horsepower on the servers, which just creates a bigger problem. IT organizations are reacting to infrastructure needs when they actually need to be concentrating on the R12 functional upgrade itself. With the R12 upgrade, the number of tables and columns increase. There are additional processes that generate more data. And as with any database application, if the infrastructure is not upgraded to keep up with the extra volumes of data, performance deteriorates.

New R12 Functionality Adds New Volumes of Data The latest version of R12 offers significant new functionality, such as the financial services accounting hub (formerly referred to as FSAH, now FAH). Users can efficiently create detailed, auditable, reconcilable accounting records from a variety of source systems. Oracle delivers a more integrated workflow into its general accounting application, and this particular module serves as one enterprise-wide accounting repository to meet a variety of corporate, management, and reporting requirements. The new XLA tables in R12 serve multiple functions within the financials application. They are the source of new workflows and new processes—and thus new data growth. In fact, many IT organizations are surprised by the amount of data growing in these particular tables. If the R12 upgrade is serving as an opportunity to consolidate instances, data grows exponentially.

More Production Copies Needed Development teams need to create additional copies of production specifically for the R12 upgrade project. This raises infrastructure requirements. When the system is upgraded, the production and all production support copies are bigger by 20 percent or more. The larger the database, the longer it takes for test and development cycles to complete. If every production and nonproduction system is backed up on a regular basis, the backup infrastructure requirements for those also increase by 20 percent. The total impact on the increase in storage requirements is not just the increase in 20 percent for your production environment; you also need to account for all nonproduction copies including backups and remote standby copies used for disaster recovery.

Upgrade to Oracle E-Business Suite R12 While Controlling the Impact of Data Growth

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Legacy 11i Environments Retained Longer Than Expected A production R12 application growing at 100 gigabytes per month can equate to several terabytes of consumed disk in less than a year.

When R12 upgrade projects are complete, IT organizations should be able to retire extra copies of the previous version and the upgrade test environments. The reality is all too often projects get delayed and the costs increase for one unexpected reason or another. If the upgrade results in a partially implemented R12 environment, some IT organization may opt to keep some legacy 11i instances just to maintain business as usual for some of their older processes. These legacy systems continue to consume Oracle licenses, hardware, and resource time to maintain.

Data Growth Gets Replicated to the Data Warehouse In 2011 the Independent Oracle Users Group (IOUG) conducted a survey on sources of Oracle database growth and what organizations are doing to deal with it. The study found that the number one reason for the most significant sources of data growth was growing demand of the Oracle application (e.g., added business units, geographies, capabilities). The second highest survey response was the data warehouse and business intelligence (BI) applications. As the leading data integration company, Informatica has witnessed the increasing need for making transactional data available for reporting and for operational analytics and business intelligence. With IT vendors promoting the benefits of operational analytics and more affordable options, more business analysts want access to that transactional data in more real time and in more detail. The Oracle Financial Accounting Hub (FAH) and Oracle Business Intelligence Enterprise Edition (OBIEE) with Oracle Business Intelligence Applications (OBIA) bring analytics and reporting closer to the source, which accelerates analysis without sacrificing details. However, replicating detailed transaction data in the data warehouse results in excess data volumes in not only production transaction system, but also in the data warehouse.

Disaster Recovery and Test Copies Business protection, backup recovery, replication, and redundant mirroring require more IT infrastructure. IT organizations need to consider the holistic storage consumption model when accounting for total cost of R12 ownership. A 1-terabyte production database can have a 25 time multiplier effect on raw storage consumption. Data gets replicated to a data warehouse, a copy of production and the data warehouse is created for standby, and yet another copy is created remotely for disaster recovery. Each test copy is refreshed from production. Backups are taken incrementally with full backups weekly. Storage is configured with RAID protection. A production R12 application growing at 100 gigabytes per month can equate to several terabytes of consumed disk in less than a year.

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Common Ways to Handle Data Growth This section discusses common ways IT organizations handle growing data volumes in the context of an R12 upgrade.

Upgrade Infrastructure If your IT organization is experiencing significant increase in storage consumption after an R12 upgrade, a simple option is to plan and purchase more storage capacity. Oracle Exadata, with its advanced compression capabilities, is another option that combines storage, server and software in a pre-engineered appliance. Neither of these options proactively addresses the data growth issue requiring more hardware and compute capacity on an ongoing basis.

Purge Excess Data Purging excess data in the production database is another option. Oracle E-Business Suite ships with prepackaged archive and purge routines for key application modules that experience significant data growth. The challenge here is to gain agreement from the business on when data is eligible for archiving and how archiving impacts end user access to data. The business may prefer to maintain access for longer, while IT would prefer to purge data more often to accommodate data growth. Organizations facing performance issues in newly upgraded R12 environments may not have enough data eligible for archiving to have a positive impact on performance. However, a large volume of data could be considered “inactive” or data related to closed business transactions. This inactive data volume cannot be purged; users may still need to access the data from the native application forms and reports.

Quantify Inactive Data Active data are the data elements in the database that are actively being updated or transacted against. Active data includes general ledger and sub-leger data in open booking periods or inventory items for products that are about to ship. Eventually the business process ends, and the transaction closes. When it does, a lot of that data becomes read-only. It doesn’t get updated again. Or it is just accessed for reporting purposes. It’s accessed far less frequently, if at all. At this point in the business process, this data can be labeled inactive. Because all that data is sitting in the production database without an infrastructure upgrade in the near future, performance is certain to deteriorate. Maintenance windows grow. Full back ups not only take longer to complete; restores take longer as well. All of the effort and resources assigned to manage the R12 instance, when a good portion of the data doesn’t change on a regular basis, indicates a significant inequality of effort versus value. This situation calls for implementing lean data management best practices.

Upgrade to Oracle E-Business Suite R12 While Controlling the Impact of Data Growth

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Better Way to Handle Data Growth: Lean Data Management Lean data management, adopted from lean manufacturing principals, focuses on eliminating wasted resources across transactional and analytic systems and across production and nonproduction environments. By removing dormant or inactive data from applications and data warehouses, shrinking and securing nonproduction copies, and retiring redundant or obsolete applications, IT organizations can dramatically lower cost, improve service level agreements (SLAs), and ensure proper compliance by proactively managing retention of structured data. Lean data management can deliver maximum benefits when implemented in conjunction with information lifecycle management (ILM) strategies. ILM comprises the policies, processes, practices, and tools used to align the business value of information with the most appropriate and cost effective IT infrastructure from the time information is conceived through its final disposition. Information is aligned with business processes through management policies and service levels associated with applications, metadata, information, and data. A key aspect of ILM is classifying data and how it changes over time. As data ages through the lifecycle, it’s assumed that it has less immediate value to the business, and therefore the underlying infrastructure should be tuned and aligned to match this value. High-value data should reside on high-performance, highly available infrastructure. Less valuable data should reside on more appropriate infrastructure. Combining ILM with lean data management, resources are optimized and waste and excess spend are minimized. Applying these best practices in an Oracle Application lifecycle can have a ripple effect on the landscape of entire R12 infrastructure. Once production data gets classified as either active or inactive, a data management plan, based on lean data management and ILM best practices, can be designed and executed. Examples of these best practices include: • Purge interface and intermediate data • Partition active and inactive data based on application aware segmentation policies • Compress aging inactive data not eligible for archiving • Archive and/or purge data that is eligible for archiving • Tier infrastructure to maximize resources and minimize total cost of ownership (TCO) • Only clone partitions or production data necessary for testing purposes • Refresh backups and disaster recovery copies • Integrate compliance with retention regulations • Extend lean data management principles to the data warehouse to create lean data warehouses Let’s look at each of these lean data management best practices for R12 upgrades in more detail.

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Purge Interface and Intermediate Data Inactive data includes interface data, old concurrent requests, and completed workflow data sitting in the R12 database. This data consumes database resources, yet is not needed by the business once it has served its purpose. This data can and should be purged.

Partition Active and Inactive Data Oracle recommends using database partitioning to align the physical storage of related data so that when the application queries or retrieves data, there are fewer storage requests. As a result, there are fewer performance bottlenecks associated with storage input/ouput (I/O) limitations. Oracle gives database administrators (DBAs) the tools needed to separate data based on business logic. DBAs should take advantage of these tools and apply them to R12 application modules.

Compress and Archive Inactive Data Oracle delivers data compression functionality that can be applied at the data object level. Compression reduces the data footprint, shrinking storage requirements. When data has been partitioned appropriately (that is, the partitions are aligned with the business application modules and the changing value of that data over time), DBAs can then compress partitions accordingly. Because aged data is less frequently accessed, organizations experience minimal performance implications due to standard decompression tasks. Data that is eligible for archiving, yet is still accessed by business users, can be retained in the production database as compressed partitions. However, for those organizations that need to realize the benefits of removing large data volumes from the production database altogether can archive the aged partitions that contain archive eligible data. Data can be still accessed via standard reporting methods because the format is still in a relational data store.

Tier Infrastructure to Maximize Resources and Minimize TCO Archived data can reside in the lowest-tier infrastructure to minimize cost. However, the most active data may require higher-performance infrastructure temporarily. While in-memory databases and flash storage offer incredible performance benefits, they come with a high price tag. Using a data classification model that aligns data value and partitioning strategies, DBAs can move the most active database segments to highest performing resources when needed.

Only Clone Partitions or Production Data Necessary for Testing Another benefit of applying the data classification strategy to R12 instances is the ability to reduce test and development copies to smaller subsets. Using RapidClone, administrators can configure the process to only copy the active data, or active segments to minimize the test data footprint. Smaller test copies can streamline patch, test, and development processes because acceptance tests can be completed faster without unnecessary data slowing the database down.

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Refresh Backups and Disaster Recovery Copies With more efficient use of infrastructure resources for the production R12 database environments, copies used for backup and recovery, standby, and disaster recovery are more streamlined after refresh. Partitions marked read-only can be excluded in RMAN backups.IT organizations can use disk-based deduplication solutions to realize additional savings.

Integrate Compliance with Retention Regulations Records managers continue to struggle to adhere to retention policies across the enterprise. Compliance with these policies is automated and simple if your ILM strategy with R12 database partitioning takes legal retention periods into account. When it’s time to identify, select and dispose data that is no longer necessary, DBAs can enforce the policy at the database partition level.

Create Lean Data Warehouses The above best practices apply not only to transaction processing databases; they can have profound benefits when applied to the data warehouse as well. Businesses implementing OBIA to gain operational insight into their business processes should also be concerned with the impact of too much data on overall performance. A holistic lean data management strategy needs to focus on the data not just in the source, but in reporting environments, in copies used for analytics, and convenient copies for departments.

Lean Data Management Automation with Informatica Application ILM Lean data management is a combination of best practices, policies, and procedures. It is most impactful when automated by technology. Informatica Application ILM solutions are designed to enable administrators to implement and automate lean data management strategies quickly and without risk. Informatica offers prebuilt data growth analytics and reports giving administrators the ability to quantity how much data is eligible for archiving for specific Oracle E-Business Suite modules based on recommended business policies. With Informatica ILM Performance Monitor, administrators can then pinpoint performance bottlenecks related to data volumes and recommend database partitioning strategies to fix and address the issues. Informatica Data Archive with Smart Partitioning creates application-aware data segmentation aligned with each Oracle E-Business Suite module resulting in drastically improved performance. Informatica Data Archive empowers IT organizations to implement enterprise data archives that control data growth and integrate retention management in their R12 production data. End users maintain appropriate levels of access to data throughout its lifecycle maximizing productivity. The Informatica Test Data Management solution is comprised of two products: Informatica Data Subset and Informatica Data Masking. This solution minimizes the impact an R12 upgrade on nonproduction copies and automates lean data management throughout the application development lifecycle. With prebuilt templates and data archive and test data management accelerators for Oracle E-Business Suite, application owners minimize risk with Oracle validated solutions that are proven and tested to work with their R12 investments.  

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Conclusion If your organization is contemplating an upgrade to Oracle E-Business Suite R12, you should aware of a critical factor that affects the success of the entire upgrade: growing data volumes. What causes this data growth? More data gets generated as a result of new application functionality. During the upgrade, additional copies of production are needed to conduct testing. Many IT organizations choose to keep the legacy version of 11i up and running. There’s a greater demand on transaction processing applications and operational analytics during an R12 upgrade. And don’t forget the multiplier effect of nonproduction and disaster recovery copies. This data growth may degrade the overall performance of the Oracle E-Business Suite environment and increase your total cost of ownership. The common ways of handling data growth—upgrading the IT infrastructure, purging excess data, and quantifying inactive data—are effective but limited. A better way to handle data growth during an R12 upgrade is to employ lean data management best practices coupled with an application ILM strategy. Data classification is an important part of an ILM strategy, and it can be indispensable in terms of its ability to optimize resources and minimized excess waste and spend. Informatica Application ILM solutions enable IT organizations to control data growth before and after an R12 upgrade. These solutions automate lean data management best practices to: • Remove dormant or inactive data from applications and data warehouses • Shrink and secure nonproduction copies • Retire redundant or obsolete applications • Manage data retention policies in compliance with regulations As a result, your IT organizations can dramatically reduce the cost and risk associated with an R12 upgrade.

Upgrade to Oracle E-Business Suite R12 While Controlling the Impact of Data Growth

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