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Monday, October 26, 2009

Oracle Siebel Data Quality


By on 2:05 PM

Oracle Siebel Data Quality (SDQ) is built to accomplish two main objectives—to maintain high-quality data and to prevent bad data from entering the application. The two product modules available for performing data quality functions within the Siebel enterprise are the Oracle Siebel Data Quality Universal Connector and the Oracle Siebel Data Quality Matching Server.


Oracle Siebel Data Quality Universal Connector


The SDQ Universal Connector allows companies to leverage their existing data quality investments. It enables third-party integration with Oracle Siebel applications including Siebel Call Center, Siebel Marketing, Siebel Sales, Siebel Service, and Siebel Universal Customer Master. In addition to supporting updates in both real-time and batch mode, SDQ Universal Connector is Unicodeenabled,providing support for several languages that contain multibyte characters.With SDQ Universal Connector, organizations can leverage the capabilities of data quality vendors that best meet their needs and integrate into Siebel through a highly scalable, flexible interface that reduces the total cost of ownership of the solution.


The Siebel Data Quality Matching Server


The SDQ Matching Server identifies duplicate accounts, contacts, or prospects within the Siebel application. It employs complex algorithms and fuzzy logic that take into account common errors that occur with long names and addresses, taking into consideration variations found in certain geographic regions. By supporting multiple languages and double-byte character sets, the SDQ Matching Server allows Oracle Siebel Data Quality to be used with Oracle Siebel CRM solutions almost anywhere in the world. The SDQ Matching Server comes embedded with rebuilt matching rules that make for easy deployment, while simultaneously ensuring high accuracy. It provides prebuilt, specialized matching rules for data, and monitoring the data. Siebel Data Quality offers capabilities to address each of these functions.


Prevent Bad Data


Oracle Siebel Data Quality prevents bad data from entering the system by using fuzzy query and interactive data matching, and with list import.Oracle Siebel Data Quality enhances Siebel queries by retrieving not only accounts/contacts that are exact matches, but also potentially misspelled, phonetically similar names as well. The interactive matching is performed when a contact is being added to the database, through the user interface and/or list import. Oracle Siebel Data Quality, through the SDQ Matching Server, checks to see if the contact already exists in the database, and if it does, then it warns the user of potential duplicates through a pop up, thereby preventing the creation of a new record. For example, if a new contact called “John Jackson” was being added, Oracle Siebel Data Quality applies the matching rules and finds Jon Jackson or John A. Jackson as possible duplicates. The user can then make a decision as to whether John Jackson is an existing contact or a new contact by picking an existing account from a list or proceeding to create a new contact.


Cleanse Customer Data


Cleansing customer data ensures standardization of name and address postal formats and ensures that customer information is stored and displayed in a consistent format. This functionality is provided through bestof- breed partners who interface to Oracle Siebel Data Quality through the SDQ Universal Connector. Cleansing can be performed in real-time or in batch mode.Incoming addresses are matched against postal directories to accomplish standardization. For example, if an incoming address looks like “145 mission street south, Corex Incorporated,” it is cleansed to “145 S. Mission St., Fl. 7, Corex, Inc.”


Match/Merge Records


Siebel Data Quality provides data matching in batch mode and enables the data steward to merge information from duplicate records into one complete record. Oracle Siebel Data Quality merge offers configurable conflict resolution and updates of sparse fields by allowing the data steward to define the sequence of the merge operation. Merging also preserves relationships by re-parenting the child objects from the records being lost to the surviving record. The DUNS number and date fields are included as standard matching elements to improve match rates and to resolve questionable matches.


Key Features


• Prevents duplicate creation in the source systems through fuzzy query and interactive matching
• Provides country-specific “Quick Start Rules” for data matching
• Provides out-of-the-box “Quick Start Rules” for account, contact, and prospect data
• Standardizes names/addresses and validates addresses using postal directories through third-party tools
• Enhances customer information with data from external sources such as D&B(Dun &
Bradstreet) to ensure completeness and accuracy
• Provides central enforcement of data management rules using leading data quality tools, resulting in lower data management costs
• Allows data stewards to configure data quality rules quickly and easily through a flexible user interface
• Provides ability to track corporate data quality effectiveness such as data accuracy, data completeness, and data
uniqueness
• Supports batch mode data quality tasks for maintaining high data quality
• Provides prebuilt integration to leading data quality vendors through the Siebel Universal Connector
• Ensures suitability for mission-criticaldeployments by providing enterpriseclass performance and scalability

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