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An Oracle White Paper February Accelerating Your Business with Data Integration

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An Oracle White Paper February 2011

Accelerating Your Business with

Data Integration

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Executive Summary ... 4  

Data Integration: A Corporate Imperative ... 4  

Main Use Cases for Data Integration ... 4  

Business Drivers for Data Integration ... 7  

Important IT Trends for Data Integration ... 7  

Data Integration: Key Functional Capabilities ... 10  

Data Movement and Transformation, ELT/ETL ... 10  

Real-Time Data Replication and Change Data Movement ... 11  

Data Quality ... 12  

Data Services ... 12  

2UDFOH¶V'DWD,QWHJUDWLRQ6ROXWLRQV ... 13  

Customer Case Studies ... 15  

Overstock.com ... 15  

Sabre Holdings ... 16  

NYK Line ... 16  

City of Anthem ... 17  

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Executive Summary

7RGD\·VPRVWLQQRYDWLYHFRPSDQLHVrecognize that leveraging their data ² and integrating it effectively to turn into an asset² is the key to accelerating their business. More specifically, data integration is now seen as a critical enabler to improve the accessibility, timeliness, and quality of mission-critical data.

This paper will review some of the main business imperatives around data integration, along with new trends in the technology space. In addition, this paper will discuss key data integration capabilities that addresses new IT and business requirements. Finally, the paper will outline 4 important case studies for implementing a comprehensive data integration strategy.

Data Integration: A Corporate Imperative

,QWRGD\·VFKDOOHQJLQJHFRQRPLFFOLPDWHFRPSDQLHVDUHVWUXJJOLQJWRGRPRUHZLWKOHVVWKH\DUHUHWUHDWLQJWR core IT fundamentals and expecting immediate return on their investments. Data integration is no exception. To meet these demanding thresholds, data integration must move beyond traditional batch and ETL to provide enterprise architects with more tangible and compelling benefits for critical business and IT initiatives.

Main Use Cases for Data Integration

Businesses need to consolidate their disparate data and move it from place to place so that it can be used by the appropriate applications. As a result, the need to transform and move data has been the leading reason for adopting data integration tools.

Data integration now serves as a foundation for many IT initiatives including business intelligence (BI) and enterprise performance management, modernization and consolidation, service oriented architecture (SOA) DQGPDVWHUGDWDPDQDJHPHQW 0'0 7KHUHIRUHLWLVQRORQJHUD´QLFH-to-KDYHµEXWD´PXVW-KDYHµ WHFKQRORJ\DQGDUFKLWHFWXUDODSSURDFKIRUWRGD\·VLQQRYDWLYH,7HQYLURQPHQWV

Data Integration for Actionable BI

Increasingly, companies rely on Enterprise Performance Management and Business Intelligence systems for mission-critical decisions and planning. While companies process increasing amount of data, turning that into actionable information is critical to improve operationVDQGFXVWRPHUVHUYLFHLQWRGD\·VFRPSHWLWLYHEXVLQHVV world. In this journey to actionable intelligence, bringing data from heterogeneous systems for a complete view is obviously a must-have. However, visibility is no longer sufficient;; users must be empowered to directly act on this data based on the available information. For example, a BI solution might report that a partner is no longer meeting a service-level agreement, but how does the company act on that data? The partner must be demoted from a platinum-level profile to a lower-profile category. This in turn means changing the data in the GDWDZDUHKRXVHRU´FORVLQJWKHORRSµDPRQJGDWDLQWHJUDWLRQEXVLQHVVSURFHVVLQWHJUDWLRQDQGEXVLQHVV intelligence. It is an important trend as more solutions take advantage of interoperability points in SOA, BI, and data warehousing.

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To achieve timely, actionable BI, the business user must be able to easily drill into the data behind a

dashboard to see details about the data lineage³that is, where the data came from and what transformations were applied. When a BI process cuts across data silos, the project team needs metadata to understand the context of information, including terminology, calculations, and methodologies³all prerequisites for a single version of the truth. Achieving actionable BI relies on accurate data throughout the data lifecycle³from data origin to data retirement. Data integration is a critical enabler of this data integrity.

The strongest BI offerings embed versatile data integration solutions that increase the value of the information delivered to the business user. Optimizing data integration within a BI solution delivers

consolidation across complex applications, clean and consistent data, real-time data access, and actionable BI.

Integration for Modernization and Consolidation

As IT organizations strive for improving performance while decreasing costs, modernizing IT systems by migrating or consolidating into more cost efficient ones is a key component of IT system planning and life cycle management. During these projects ensuring data is migrated to new systems without interrupting business operations is a daunting task. Therefore, many organizations perceive data migration as a major risk during system upgrades and migrations and delay such modernization projects to avoid interruption to business. These delays cause IT organizations to use less effective and more costly IT systems, and decrease their ability to compete with the companies that do migrate to modern systems.

Data integration solutions can address data migration challenges by enabling uninterrupted operations and improving data quality while moving the data to the new environment. For eliminating downtime during migrations, data integration solutions should have real-time change data movement capabilities, which enable reliable synchronization between the old and new systems before switchover, even when handling large data volumes. Data quality and profiling capabilities are crucial for environments where additional data

improvement may be needed to achieve trusted information.

Real-time data integration and data quality solutions help organizations to modernize and consolidate systems with minimized risks and allow them to reduce costs and increase IT system performance sooner than later.

Data Integration for SOA and Application Integration

Companies initiating projects that require data must make choices. Should they continue to connect data in the same customized, rigid, point-to-point way that they use for applications? How can they ensure data is consistent, accurate, and current? How can the data be managed, tracked, and profiled?

Experienced IT professionals know that all of these factors are important and need to be considered together. They recognize that custom code or single-use data integration projects are neither scalable nor reproducible, and that they offer negligible ROI. The better strategy is to apply reusable principles to data integration³ turning data into a service that is available as logical modules, each with a standards-based interface. This allows users to access and use the service more easily, improves data visibility, and promotes greater reuse.

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Data reuse and flexibility is often one of the key architectural requirements for large enterprises. Data integration can also contribute to more successful SOA initiatives ² helping provide the heavy lifting of data movement, replication and data transformation without burdening the processing times of business process execution engines.

By leveraging both service-oriented data integration and combining those with real-time data extraction technologies, companies can lower costs through consolidating legacy systems, reduce risk of bad data polluting their applications, and shorten the time to deliver new service offerings.

Finally embedding these real-time data integration components directly within your existing CRM, ERP, or industry-applications, can rapidly increase implementation and delivery times as opposed to custom developing these integrations independently for each and every application.

Data Integration as a Foundation for MDM

One of the most significant areas where data integration can be applied for improving ROI involves master data management (MDM). MDM consolidates master data such as customer, product, supplier, asset, site, partner, part, service, account, etc. into an OLTP data model where it can be cleansed, governed and shared with applications, business processes, and analytical systems across the enterprise.

The data integration platform is critically important across multiple MDM processes including: x Data profiling within all sourcing systems

x Data acquisition, standardization, cleansing, matching and de-duplication from attached systems into the MDM data hubs

x Clean and timely MDM data propagation from the MDM data hubs to destination systems

x Entity mappings for business intelligence tools such as operational application dashboards and enterprise performance management.

x Bi-directional data flow of key dimension information from the MDM data hubs into the data warehouse and analytical results from the data warehouse into the MDM data hubs.

x Metadata management enabling joins across data warehouses, data marts, operational data stores and MDM data hubs

The MDM components for data governance, data mastering, and real-time data quality should not be considered separately from the act of data integration. As a result, enterprise architects and data stewards should exercise caution before undertaking an MDM strategy without also implementing core data integration and data quality technologies that integrate their data-centric applications. In combination, MDM and data integration can be seen as the cornerstones for successful data-centric architectures that provide authoritative master data for a single view of business.

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Business Drivers for Data Integration

6LPSO\´ORDGLQJP\GDWDZDUHKRXVHµ GRHVQ·WVXIILFHDVDEXVLQHVVGULYHUIRUFRPSDQLHVWRGD\HQWHUSULVH architects, together with line-of-business managers, are looking for top-line reasons to justify the IT expenses and organizational changes associated with integrating data. By supporting key business and IT initiatives in organizations data integration delivers many business benefits to organizations including decreasing costs, increased agility, reducing risk, improving business insight, and enhancing customer intimacy.

x Decreased costs can be achieved by consolidating and modernizing into more efficient systems. Data integration products also help reduce complexity in accessing data across disparate systems, and reduce development and management costs compared to homegrown solutions. And if designed for non-intrusive operations, they can reduce the overhead on the whole infrastructure ² from source systems to network. x Increased Agility comes with access to accurate, manageable, and transparent data that allows

organizations to more-quickly identify and respond to internal and external events. Data integration solutions foster this type of agility by uniting heterogeneous data sources across the enterprise. x Reduced Risk is a major benefit due to having access to reliable, consistent and accurate data.

Organizations can suffer significant tangible and intangible losses in day to day operations and customer relationship management when using poor quality data.

x Improved Business Insight is possible when companies leverage timely and accurate data in decision making, which turn data into competitive advantage. Better and faster decision making paves the way for improved operations and new efficiencies for higher profitability and faster growth.

x Enhanced Customer Intimacy is achieved via gaining access to timely, accurate and comprehensive information about customers. Achieving 360 degree view of customers allows companies to tailor their offerings accordingly and create a superior customer experience.

Important IT Trends for Data Integration

While data integration serves as the foundation for many important business and IT initiatives, there are specific trends that shape the data integration capabilities IT organizations demand to address their current needs.

Growing Data Volumes and Shortening Batch Windows

Batch processing oriented systems often scan source database tables and create a batch file to load onto the target database. This type of extract process creates high overhead on the source system and impacts transaction processing. Because of the high overhead and to ensure read consistency during the extract process extraction must typically be performed during batch windows, most often at night.

For many years, batch processing was sufficient to meet BI requirements. However, with the growth of the internet, consumers transact business and expect access to services 24/7, which has effectively eliminated the

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FRQFHSWRIWKH´EXVLQHVVGD\µ$s a result, the window available for batch data movement is shrinking, while simultaneously, the amount of data that must be processed is growing very rapidly.

Faced with this dilemma, IT departments must employ highly efficient operations to process the data within batch windows, but many are finding it impossible to perform the required processes in the allotted time.

Figure 1: Risk of batch window dependency

The Need for Timely and Trusted Information

Business time is increasingly moving toward real time. As organizations look to grow their competitive advantage, they are trying to uncover opportunities to capture and respond to business events faster and more rigorously than ever. Today, effective and innovative use of IT, especially in enterprise business intelligence

initiatives, delivers a significant portion of that competitive advantage. A robust enterprise data warehouse combined with an enterprise analytics framework allows companies to achieve faster and more actionable BI.

Across the enterprise, each facet of the business gathers data through an assortment of activities, and many organizations now deliver this data to a central data warehouse³where the data is captured, aggregated, analyzed, and leveraged to improve decision making. The quality of these decisions depends not only on the sophistication level of the analytics applications that run on the data warehouse, but also on the underlying data. Data has to be complete, accurate, and trusted. For that reason, it has to be timely: timely data enables better-informed decisions.

The lifecycle of a data record through enterprise analytics starts with the capture of a business event in a data repository such as a database. Data acquisition technologies deliver the event record to the data warehouse.

Business users want quick access to reliable real-time information to help them make better business decisions. As a result, enterprise architecture and information architecture professionals find themselves on a never-ending TXHVWWRLPSURYHGDWD¶VTXDOLW\DQG timeliness.1

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Analytical processing helps turn the data into information, and a business decision leads to a corresponding action.

To approach real time, the duration between the event and its consequent action needs to be minimized. As outlined in Figure 2, the initial data acquisition and delivery to the warehouse introduces the majority of the latency. 1

Figure 2: The longer it takes to capture and process data, the lower the value of the information.

Leading industry analysts have recently reported on this trend for BI and data warehousing based on the clear value achieved by companies that have deployed real-time capabilities. For example, in its 2009 Update:

Evaluating Integration Alternatives report, Forrester stated that ´tRGD\·VFRPSOH[GDWDLQWHJUDWLRQ requirements

demand higher-quality data and more-robust metadata and auditability, with service level agreements (SLAs) requiring data delivery ranging from nightly batching to real-time services across heterogeneous IT

ecosystems. To accommodate these changing needs, ETL software vendors have expanded their portfolios to include stronger metadata management, integrated data quality and profiling, as well as real-time data

integration techniques including CDC and data federation.µ

High System Availability

As businesses expand their use of online applications and operate more and more globally, business-critical applications and underlying data must be accessible at or near 24/7/365 without service interruption or performance degradation

.

System availability is obviously important for running uninterrupted operations around the clock. It is also critical for data integration purposes. As employees and enterprise systems access

1 7HFK5DGDUŒ)RU(QWHUSULVH$UFKLWHFWXUH3URIHVVLRQDOV(QWHUSULVH'DWD,QWHJUDWLRQ4)HE1RHO

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and act on data from other systems more frequently and increasingly with very loZODWHQF\DVRXUFHV\VWHP·V availability has impact beyond its direct users. This paradigm is even more prominent in service oriented architectures (SOA) where application logic and data are shared by increasing number of end users. Because of these interdependencies, many mission-critical systems have strict service level agreements (SLAs) for availability and performance levels.

IT organizations that are tasked with delivering robust data infrastructures need to design their data

architectures and implement solutions for robust data availability. High availability solutions should not only address unplanned outages such as disasters, they should also eliminate downtime from planned outages such as maintenance and upgrades. Even though planned outages can be arranged to off business hours, for many PLVVLRQFULWLFDODSSOLFDWLRQVWKDWVXSSRUWRQOLQHDQGRUJOREDORSHUDWLRQVDQ´RIIEXVLQHVVKRXUVµFRQFHSW does no longer apply. Therefore a comprehensive data availability solution is a critical component of a robust data infrastructure.

Data Integration: Key Functional Capabilities

Although data integration functionality can differ somewhat depending on the business and IT problems addressed, there is general agreement that a comprehensive data integration solution includes the following core functional capabilities:

x Data movement and transformation. Core ETL capabilities, bulk data transfer and transformation.

x Data replication. Change data capture and synchronization.

x Data quality. Data cleansing, data quality business rules.

x Data services. Data access services. Virtual or physical data store to support bulk or trickle-feed data

services.

Each customer can prioritize the functional capabilities above in a different manner, depending on the environment, the use case, intended scale of their data integration deployment, available skills, and types of technologies already in use.

Data Movement and Transformation, ELT/ETL

ETL technology supports the extraction, transformation, and integration of data from multiple data sources, including databases and data warehouses. ETL allows businesses to consolidate their disparate data from any source while moving it from place to place. ETL can transform not only data from different departments but also data from different sources altogether. For example, order details from an enterprise resource planning system and service history from a customer relation-ship management application can be consolidated into a central data hub for a single view of the customer.

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transform³tools because they can optimize where transformations are deployed either on the target destination or even on the source. This also allows for greater flexibility, improved scalability, and greater performance. E-LT approaches can also reduce costly IT infrastructure costs. The following should also be considered when evaluating an E-LT solution:

x Performance optimizations for set-based transformations. x Optimizations for database appliances.

x No hardware requirements;; run-time agents should be deployed on the databases themselves. x Data that always goes from source to target through optimized database pathways;; data should never move through the intermediary.

x Extensible support for standard Java and SOA environments

x Design tools that support out-of-the-box optimizations. Users should not have to write special scripts or custom code to enable optimized performance.

Not all ELT approaches are equal. When selecting an ELT solution, it is important to discern between brittle proprietary technologies that can easily break and open ELT platforms that can dramatically improve performance while simultaneously lowering cost of ownership.

Real-Time Data Replication and Change Data Movement

Another key requirement in any data integration solution is the need for moving only the changed data in real time. As data latency increases, the information becomes less consistent with reality and its organizational value diminishes. Some business operations require business insights that use the most current data to enable employees to take action with completely reliable and accurate information. To improve operational efficiency and effectiveness, companies need to rely on business intelligence (BI) that uses timely operational

information as well as historical context.

For data integration needs, companies have generally relied upon moving bulk data periodically to their analytical systems which requires batch data processing windows. As mentioned earlier, with data volumes increasing rapidly and business hours approaching 24/7, IT teams might not be able to complete the extraction of data they need to move within the allotted time window. The same problem occurs if they need to restart the batch process for any reason;; the allotted time window might not be adequate.

To overcome this dilemma, companies should avoid focusing on custom scripts or piecemeal bulk data handling solutions. Instead, they should adopt more-comprehensive data integration approaches that combine both bulk data movement and transformation with real-time data integration to enable highly available mission-critical systems and real-time BI.

Augmenting ETL or E-LT systems with a real-time, log-based change data capture (CDC) solution enables IT teams to meet the requirements of mission-critical systems. Through a log-based CDC approach,

organizations can source data from OLTP systems without impacting performance and feed the ETL/E-LT system with a continuous stream of real-time data. This method not only decreases data latency for BI

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systems, but it also eliminates the reliance on batch-processing windows, while allowing continuous operations for business-critical systems.

Real-time data integration and replication capabilities are also important for system modernization and consolidation uses cases, for which they enable data migration without interrupting business operations. Additionally, using real-time heterogeneous data replication companies can synchronize systems across data centers and support global operations with real-time, accurate data. Furthermore, real-time data replication allows high datDDYDLODELOLW\UHTXLUHPHQWVRIWRGD\·VJOREDOEXVLQHVVZRUOG5HDO-time data replication eliminates not only planned outages during system modernization, migration and consolidation project, but also eliminates downtime during unplanned outages.

Data Quality

All enterprise software projects are at risk from bad data;; even worse, inaccurate and inconsistent data exists everywhere. However, the demand for trusted data continues to increase, driven by investments in packaged applications and BI software. Strategic IT initiatives such as MDM also add additional pressure. Further complicating the matter, regulatory compliance initiatives³such as the Sarbanes-Oxley Act, the U.S. Patriot Act, and Basel II³require tracing the source of the data used in financial reports, as well as examination, tracking (through snapshots), and certification of the state and quality of the business data.

The act of data profiling is often omitted when trying to achieve data quality. Data profiling is a data investigation and quality-monitoring mechanism that allows business users to assess data quality through metrics, discover or infer rules based on this data, and monitor the evolution of data quality over time. Data profiling works with data quality to better understand and manage the holistic issues associated with data quality.

In addition, the component elements of data quality for matching, parsing, standardization, and enrichment help meet any data cleansing scenario for providing a single view of trusted information.

Careful attention should be paid to the discrete differences of product data (item data) vs. customer data (party data), and even financial data. For example cleansing a name/address sequence will require separate standardized rules than parsing product codes.

Together these data quality and data profiling solutions can eliminate some of the risk associated with bad data corrupting complex data-centric architecture projects.

Data Services

Data services have a transformational influence on enterprise data-centric architectures. They are the foundation of many SOA deployments and

are needed to bridge the gaps between processes and the core application infrastructure. While there are many categories for data services, data access services are the most commonly used. Our analysis indicates that there are three important architectures where data can be exposed as reusable access services.

x Single data access (an adapter)

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x Multi-source data access via data consolidation on an operational data store (ODS)

Many off-the-shelf products in SOA, BPM, databases, and even development tools include basic functionality IRUDFFHVVLQJGDWDVRXUFHVYLDD¶VLQJOHGDWDDFFHVV·SRLQW6LQJOHGDWDDFFHVVKDVLWVOLPLWDWLRQVLQWKDWLWGRHVQ·W consider performance impact on the source systems. In addition, it reinforces point-to-point architectures, something that Service-Oriented Architectures try to avoid. The best practice for data services, is to consolidate data into a hub using ETL/ELT and log-based CDC, and then build real-time access points as services. This approach minimizes the overhead on the source system that can cause performance

degradation, while at the same time allows the IT team to design and optimize the operational data store (ODS) environment to meet availability and performance SLAs for a variety of data services. However, when the option of moving data to an ODS is not available, either due to ownership rights or regulations, virtually aggregating the data through data virtualization² also known as data federation can provide a simple

alternative. Data federation leaves data at the source and consolidates information virtually³in a manner very similar to how an enterprise service bus virtualizes messages. Data federation allows companies to aggregate data across multiple sources into a single view that can be reused as a service.

2UDFOH¶V'DWD,QWHJUDWLRQ6ROXWLRQV

Oracle Data Integration provides a fully unified solution for building, deploying, and managing data-centric

architectures for operational and analytical environments. In addition, it combines all the elements of data integration³real-time data movement, transformation, synchronization, data quality, data management, and data services³to ensure that information is timely, accurate, and consistent across complex systems. 2UDFOH'DWD,QWHJUDWLRQSURYLGHVNH\FRPSRQHQWVWRPHHWWRGD\·VVRSKLVWLFDWHGHQWHUSULVHGDWDLQWHJUDWLRQ requirements, addressing both core ETL requirements as well as emerging trends in real-time, data quality, data management, and data services federation.

Oracle Data Integrator Enterprise Edition delivers unique next-generation, Extract Load and Transform

(E-LT) technology that improves performance, reduces data integration costs, even across heterogeneous V\VWHPV8QOLNHFRQYHQWLRQDO(7/WRROV2UDFOH·VVROXWLRQGRHVQ·WUHTXLUHVHSDUDWHKDUGZDUHIRUWKH(7/ engines. These ETL engines can be deployed directly on the target or even the source database.

Oracle Data Integrator Enterprise Edition can be optimized for improved query performance together with Oracle Exadata, improving performance by as much as 10 times traditional ETL approaches.

In addition, Oracle Data Integrator provides capabilities for data services between Oracle SOA Suite, and rich metadata management to enable improved data lineage across Oracle BI / EPM solutions.

In addition, Oracle Data Integrator provides capabilities for data virtualization together with Oracle Data Service Integrator. Data .services can be integrated across Oracle SOA Suite and Oracle Business Process Management with rich metadata management to enable improved data lineage across Oracle BI / EPM solutions.

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Oracle GoldenGate provides real-time, log-based change data capture, routing and delivery between

heterogeneous systems. Using this technology, it enables cost-effective and low-impact real-time data integration and continuous availability solutions. In addition, Oracle GoldenGate moves committed

transactions with transaction integrity and minimal overhead on your existing infrastructure. The architecture supports multiple data replication topologies such as one-to-many, many-to-many, cascading and bidirectional. Its wide variety of use cases includes real-time business intelligence;; query offloading;; zero-downtime

migrations and consolidation;; disaster recovery;; and active-active databases for data distribution, data synchronization and high availability. Oracle GoldenGate complements Active Data Guard with its

heterogeneous, transactional real-time data replication capabilities. The combined solution delivers a complete high availability solution that eliminates planned and unplanned downtime across the enterprise.

Oracle Data Quality for Data Integrator and Oracle Data Profiling extends Oracle Data Integration to

provide advanced data quality and governance capabilities. Both products are fully integrated with Oracle Data Integration to place quality at the center of all your information initiatives and projects.

Oracle Data Profiling is a data investigation and quality monitoring tool. It allows business users to assess the quality of their data through metrics, to discover or infer rules based on this data, and to monitor historical metrics about data quality.

Oracle Data Quality for Data Integrator is the leading data quality platform that covers even the most complex data quality needs. Its powerful rule-based engine and its robust and scalable architecture places data cleansing at the heart of an enterprise data integration strategy.

In addition, Oracle provides a rich offering of both customer, product and MDM focused data quality options for application-centered data quality. These are part of Oracle Master Data Management solutions.

Oracle Data Integration: Complete, Open, Integrated

With a unique data integration platform that is architected for performance and reliability, Oracle Data Integration products provide a high degree of flexibility and modularity. With a comprehensive list of features for broad set of use cases it delivers a complete solution for data integration. By providing broad support for GLYHUVH,7HQYLURQPHQWVLWH[HFXWHVRQ2UDFOH·VFRPPLWPHQWWRKHWHURJHQHLW\5HJDUGOHVVRIWKHGDWDEDVHVRU applications within your IT ecosystem, Oracle Data Integration solution can be optimized to drive the highest-performance bulk or real-time transformations.

By using Oracle Data Integration solutions, organizations have achieved real, tangible results: x Reduced development costs by 30%,

x Improved speed of handling data by five times, x Achieved 6-fold gains in productivity

x Decreased business process execution times by at least 70 % x Lowered TCO by 80%

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These cost savings and efficiency gains are critical to leverage in today's challenging global economic climate. Below are a few real-life examples.

Customer Case Studies

Overstock.com

Overstock.com is an online retailer offering discount brand name merchandise. Overstock.com offers customers the opportunity to shop smarter and more conveniently online for top-quality bargains. In 2004, 2YHUVWRFNFRP·VUHYHQXHZDVXSILYHIROGIURPDQGJURVVSURILWVQHDUO\TXDGUXSOHG:LWKEXVLQHVV growing at a rate of 100 percent a year, the company braced for continued growth and increasing customer demand.

Experiencing 14 to 18 million hits a month to its Website, Overstock.com recognized the need to both scale and streamline operations to better support its 24/7 customer transaction and reporting loads. Being an online retailer, there is zero tolerance for downtime. Overstock.com is always open, so its IT infrastructure must be continuously available. In addition, the company wanted to enable a real-time, single view of the customer to better understand purchasing habits, refine marketing efforts, and more-effectively drive business to its Website.

To achieve these objectives, Overstock.com decided to implement an enterprise data warehouse and customer analytics applications. The company selected Oracle GoldenGate to extract customer data from the Oracle Databases supporting its shopping and auction sites, and transform it locally on a data warehouse appliance using Oracle Data Integrator.

Oracle GoldenGate allows access to data in real time throughout the day as transactions commit in source systems, which enables Overstock.com to run reports around the clock, without putting additional strain on the online transaction processing systems. In the past, the system could be tied up for long periods for a single data report, causing significant reduction in productivity. Moreover, Overstock.com was forced to treat all customers the same, whereas now the retailer can analyze customer behavior and purchase history to execute highly-personalized marketing campaigns and service.

Overstock.com has already started to see the benefits of leveraging customer data in real time. ´Day-old data KDV]HUREHQHILWWRXVµVDLG3DXO/RQJKXUVWGLUHFWRURIGDWDZDUHKRXVLQJ2YHUVWRFNFRP´,IZHH[HFXWHD marketing campaign, we need to knRZLILW·VZRUNLQJ:HQHHGWRNQRZLIFRQVXPHUVDUHFOLFNLQJLQWKHULJKW place, if the email is driving consumers to the site, and if those customers are making purchases. We have deployed Oracle GoldenGate with Oracle Data Integrator Enterprise Edition and we can access this kind of GDWDLQUHDOWLPHUDWKHUWKDQZDLWLQJRQHWZRRUWKUHHGD\Vµ

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Sabre Holdings

6DEUH+ROGLQJVFRQQHFWVSHRSOHZLWKWKHZRUOG·VJUHDWHVWWUDYHOSRVVLELOLWLHVE\UHWDLOLQJWUDYHO products and providing distribution and technology solutions for the travel industry. Sabre is renowned for its airline UHVHUYDWLRQQHWZRUNZKLFKZDVWKHZRUOG·VILUVWFRPSXWHUL]HGWUDQVDFWLRQ-processing system, and for Travelocity, the pioneering website that set influential e-commerce standards for usability and performance. 6DEUH·VFRUHEXVLQHVVWRXFKHVPRUHWKDQRIWKHZRUOG·VWUDYHOUHVHUYDWLRQV

With the emergence of online travel shopping, Sabre was challenged with servicing high volumes of air travel requests from millions of non-travel DJHQW´EURZVHUVµ%HFDXVHWKHVHXVHUVDUHOHVVNQRZOHGJHDEOHDERXWDLU routes, fare rules, and airline schedules, the responses required a greater number of options, increasing the FRPSOH[LW\RIWUDQVDFWLRQV6LQFH´ORRNLQJµGULYHVFRVW, DQG´ERRNLQJµGULves revenue, Sabre needed to develop a low cost, highly scalable, and highly available solution to balance the needs of both audiences. The team at Sabre designed a hybrid computing environment based on a horizontally scalable server farm of 120 Linux/MySQL database servers and Linux application servers. An HP NonStop multimode system provides the master database. Sabre decided to use Oracle GoldenGate to replicate data to the MySQL GDWDEDVHFRSLHVLQUHDOWLPHWRPDLQWDLQGDWDDFFXUDF\IRU´ORRNHUVµ

As low fare search traffic increases exponentially (and peaks seasonally), perhaps the greatest attribute of the hybrid environment has been its scalability. With Oracle *ROGHQ*DWH·VDELOLW\WRVLPSO\DGGGHOLYHU\PRGXOHV to more database servers as needed, Sabre was able to grow the environment as needed. In addition to improvements in customer service and performance on the system, the new distributed, tiered infrastructure has delivered more than 80% savings by off-loading query transactions, an overall savings amounting to millions of dollars.

The company later decided to migrate the database farm from MySQL to Oracle Databases to further decrease their total cost of ownership. The move from 160 MySQL Databases instances down to 12 Oracle helped the company achieve 69 percent reduction in software maintenance costs, 81 percent reduction in hardware costs, and 79 percent reduction in hosting costs.

Sabre relied on Oracle GoldenGate also for migrating several HP NonStop systems to Oracle Database. With its real-time, heterogeneous and transactional replication capabilities, Oracle GoldenGate delivered zero downtime migration from legacy systems to Oracle Database.

NYK Line

In an economy that trades on information, companies must be able to integrate vast amounts of data from disparate systems³and manage data movement, synchronization, and quality, as well as data management and services³while reducing costs and improving efficiencies.

One company that has succeeded in this effort is NYK Line, a 120-year-old shipping company. For this organization, which coordinates 700 ships and generates US$24 billion in revenue annually, handling tens of thousands of cargo containers from around the world and delivering them safely and on time is an everyday event.

But, like many companies, Tokyo, Japan-based NYK Line faced a business challenge: as part of implementing a new global shipping solution, it needed tighter integration between its key business software, its e-commerce

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easily address a U.S. Customs Service requirement for shipping companies to notify Customs 24 hours before a container was loaded on a ship in a foreign port. This meant a quantum shift in how the cRPSDQ\·VGDWDZDV managed and reported³and that meant NYK Line needed a new data integration strategy.

´1<.QHHGHGWRPDNHFRQQHFWLRQVWRGLIIHUHQWGDWDEDVHVDQGGDWDVRXUFHVLQFOXGLQJ,%004IODWILOHV ;0/GULYHUVDQGPRUHµVD\V'XUJDSUDVDG3XODNNDW architect in the IT governance group within NYK Business Systems 1%6 ´7KDW YHUVDWLOLW\ZDVLPSRUWDQWWRXVµ

2UDFOH'DWD,QWHJUDWRU·V-'%&;0/GULYHUVLPSOLILHVGHYHORSPHQWDQGFRQILJXUDWLRQDFFRUGLQJWR0RKDQ Loganathan, architect and manager, data VHUYLFHVJURXSDW1%6´<RXFDQFRQQHFWWRDQ\WDUJHWZLWKD-'%& GULYHUµKHVD\V´2UDFOH'DWD,QWHJUDWRULVHDV\WROHDUQDQGWKDW·VRQHRILWVPDMRUDGYDQWDJHV

In the short term, NYK Line was able to meet the U.S. Customs Service requirements for noWLILFDWLRQ´,Q less than a month, we were able to implant new business processes using Oracle Data Integrator that report RQFRQWDLQHUPRYHPHQWVDQGSURYLGHWKDWLQIRUPDWLRQWR86&XVWRPVµVD\V/RJDQDWKDQ´7KDW·VZKHQRXU business managers realized how good the time to market is with Oracle Data Integrator-based solutions. Now ZKHQWKH\QHHGDVROXWLRQWKH\DVNWKDWZHGRLWLQ2UDFOH'DWD,QWHJUDWRUµ

The company began rolling out Oracle Data Integrator-based solutions in February 2006 and has rolled out new solutions using the technology every one or two months since.

´5LJKWQRZZHKDYHDERXWWR2UDFOH'DWD,QWHJUDWRU-based solutions running in production, doing about 10,000 jobs on a daily basis and moving about 25 million transactions per dD\µVD\V3XODNNDW NBS also uses Oracle Data Integrator for a wide variety of solutions, including data integration between e-commerce systems, partner integration with customers, legacy integration with regional systems, integration with enterprise resource planning (ERP) solutions such as SAP, and data integration with a corporate management information system. For example, NBS uses Oracle Data Integrator to provide the data

integration services for its container-tracking application used by customers around the world, which needs to be accessible 24 hours a day.

´:HQHHGDYDLODELOLW\SULPDULO\IRURXUH-FRPPHUFHGDWDLQWHJUDWLRQµVD\V/RJDQDWKDQ´-XVWDV)HG([ DHL, or UPS provides tracking numbers for shipments, NYK Line has a site, www.nykline.com, where FXVWRPHUVFDQHQWHUWKHLUFRQWDLQHURUERRNLQJRUELOORIODGLQJQXPEHUDQGVHHZKHUHWKHFRQWDLQHULVµ Overall, the company supports about 5,000 users around the world for the global shipping solution, while the management information systemVSURYLGHVXSSRUWIRUXVHUV´:HGRDORWRIGDWDLQWHJUDWLRQµVD\V Pulakkat.

City of Anthem

For the City of Arnhem in the Netherlands, data integration was only one step toward delivering better services to its citizens. But it was an important step. The goal was to put online more than 4 million pages of GRFXPHQWDWLRQVXFKDVGUDZLQJVEXLOGLQJDSSOLFDWLRQVDQGSHUPLWVIURPWKHFLW\·VSODQQLQJDUFKLYHWR integrate and consolidate all this information into a single, accessible database for everyone from real estate agents to firefighters to homeowners;; and to make the information easily accessible.

(17)

The job was huge. The planning archive contains documents dating back to 1917. Additional information related to some documents, such as permits, was recorded on paper as well as by computer systems. Previously, the documents were spread across three sites, making the task of locating and accessing

documents time consuming and costly. The city of Arnhem implemented a flexible SOA-based infrastructure that would enable all its central office applications to access the consolidated information. This included using Oracle Data Integrator to import metadata and existing data records related to the documents from a wide range of diverse sources (from old DataFlex and Microsoft Access applications to existing Oracle

applications) into the Redora Business and Data Warehouse. Next, the City of Arnhem digitized all the documents using Oracle Document Capture. Scanned files were stored in compressed PDF/A format to save space and speed downloads.

By the time the project is completed, the solution will encompass about 25 applications from different suppliers sharing centralized information on properties, people, and buildings.

´7RJHWDOORIWKHDSSOLFDWLRQVLQ$Unhem to talk to each other is complex and requires a lot of interfaces DQGDGDSWHUV,W·VDOVRYHU\H[SHQVLYHDQGWLPHFRQVXPLQJWRWU\DQGFRQQHFWWKHPPDQXDOO\µVD\V+HPPR GH*URRWGLUHFWRULQIRUPDWLRQPDQDJHPHQW&LW\RI$UQKHP´2UDFOH'DWD,QWHJUDWor, combined with Oracle Enterprise Service Bus and Oracle BPEL Process Manager, gives us a universal connection interface that makes it possible to do it in an easy and standardized way. Now, using Oracle Data Integrator, we have one point to manage our data integrations. We can use one tool to do all those different kinds of data WUDQVSRUWDWLRQDFURVVWKHRUJDQL]DWLRQµ

´7KHJRDOVRIDFLW\DUHQRWFRPPHUFLDO³WKH\·UHWRSURYLGHJRRGTXDOLW\RIVHUYLFHVWR\RXUFLWL]HQVWREHD good city to live in, to EHVDIHDQGWREHDQLFHSODFHµVD\VGH*URRW´2UDFOH'DWD,QWHJUDWRULVKHOSLQJXV achieve those goals by enabling us to work more efficiently and standardizing our technology. It helps us provide better quality of data to our citizens, and we can manage it with fewer people because we have one WHFKQRORJ\SODWIRUPµ

Conclusion

While data integration is deeply rooted in traditional data warehousing and ETL technologies, it is evolving now into a critical component for many technology imperatives. Reliable, timely and trusted data is essential for the agile enterprise. By supporting variety of key business and IT initiatives such as service oriented architecture, BI, and MDM data integration offers businesses improved agility, reduced risk, improved business insight, better customer intimacy, and lower cost structures.

$VDFRPSUHKHQVLYHRIIHULQJ2UDFOH·V'DWD,QWHJUDWLRQSURYLGHVGDWDPRYHPHQW (/7(7/ GDWD

synchronization, data quality, and data services in a single, unified solution and delivers timely, accurate, and consistent information from multiple systems. Many industry leaders have partnered with Oracle for building the foundation for information management and accelerated their business with tangible results.

For more information about OraclH·VGDWDLQWHJUDWLRQVROXWLRQVSOHDVHYLVLW

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Accelerating Your Business with Data Integration

February 2011

Author: Dain Hansen, Irem Radzik Oracle Corporation World Headquarters 500 Oracle Parkway Redwood Shores, CA 94065 U.S.A. Worldwide Inquiries: Phone: +1.650.506.7000 Fax: +1.650.506.7200 oracle.com

Copyright © 2011, Oracle and/or its affiliates. All rights reserved. This document is provided for information purposes only and the contents hereof are subject to change without notice. This document is not warranted to be error-free, nor subject to any other warranties or conditions, whether expressed orally or implied in law, including implied warranties and conditions of merchantability or fitness for a particular purpose. We specifically disclaim any liability with respect to this document and no contractual obligations are formed either directly or indirectly by this document. This document may not be reproduced or transmitted in any form or by any means, electronic or mechanical, for any purpose, without our prior written permission.

Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners.

AMD, Opteron, the AMD logo, and the AMD Opteron logo are trademarks or registered trademarks of Advanced Micro Devices. Intel and Intel Xeon are trademarks or registered trademarks of Intel Corporation. All SPARC trademarks are used under license and are trademarks or registered trademarks of SPARC International, Inc. UNIX is a registered trademark licensed through X/Open Company, Ltd. 1010

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