Oracle Product Data Quality

Oracle Product Data Quality is built from the ground up to tackle the unique challenges of assessing, improving, and managing product data. The Oracle Product Data Quality solution is built on market-leading DataLens semantic-based technology and has been proven in a wide range of customer scenarios involving product, item, asset, SKU, and other forms of product or product-like data across a range of industries.

Typical product data is unstructured, non-standard, and often missing important information. With Oracle Product Data Quality, customers cancreate consistent and reliable product data by quickly identifying and applying standards to product data across systems, repositories, and processes, including the ability to identify and remediate problem data.

Most traditional data quality solutions were built to deal with customer or 'party' data, and struggle with the unique challenges of product data. By contrast, Oracle Product Data Quality is designed to handle data that is:

• Poorly structured—Requires sophisticated semantic parsing capabilities
• Non-standard—Requires standards to be applied and data transformed to meet enterprise standards
• Highly variable—Requires flexible recognition and transformation capabilities to address nearly infinite combinations of acronyms, spelling, and vocabulary variations
• Category-specific—Requires the ability to both categorize an item and apply different rules based on content and context
• Variable quality—Requires integrated exception management and remediation capabilities
• Duplicated—Requires sophisticated semantic matching capabilities

Oracle Product Data Quality delivers:

• Semantic-based recognition—Context-based recognition enables accurate parsing, standardization, and matching along with auto-learning to handle the extreme variability and unpredictability of product data
• Scalability—Manages millions of items across thousands of categories
• Integrated governance—Allows data stewards to monitor overall process effectiveness as well as drive direct data remediation
• Business user interface—Code-free interface streamlines use for business users, who best understands the rules and nuances of the data
• Enterprise-wide applicability—Standard process easily plugs in to existing systems and processes to enforce product data quality standards in any process or system

Data quality is foundational to almost any business process, as the benefits of consistent and reliable product data can be felt in every aspect of the process. Typical benefits of improvedd data quality are in the following areas:

• Improve revenues—Enables online search; drives cross-sell and up-sell opportunities; Accelerates and improves response to quote requests; Improves customer experience
• Cost efficiencies—Reduces direct and indirect spending through improved procurement practices; eliminates need for manual data clean-up, classification, standardization, matching, de-duplication or translation; avoids cost of error correction; reduces data synchronization and integration costs
• IT projects—Reduces time, risk, and cost of system deployment, consolidation or merger; avoids complex rules building and maintenance; enables governance by business users; improves business agility
• Reporting & compliance—Provides consistent platform for data quality governance; enforces data standards across systems, processes, repositories; dashboards & metrics drive continuous process improvement

Oracle Product Data Quality is made up of three core modules that work together to enforce category-specific standards on disparate product information:

Oracle DataLens Knowledge Studio

• Semantic 'data lenses' interpret and standardize unstructured, disparate information
• Data lenses enable:
  
  • Contextual recognition
  • Translation for any language to any language, including double-byte languages
  • Match & merge, including survivorship, enrichment and overrides
  • Transformation and standardization to conform to any format or standard
  • Classification to any taxonomy, whether public or private
• Data lenses are designed to be built and maintained by business users who understand the nuances in meaning of product descriptions
• Includes facilities to 'AutoBuild' data lenses from available metadata (extracted from PIM or legacy systems, rules or standards)

Oracle DataLens Application Studio

• 'Data Service Applications' take incoming items through a business task flow
• Data Service Applications enable:
  
  • Implementation of business rules for imposition of data standards
  • Manage both 'good' items and exceptions through a full workflow process including both automated and manual remediation
  • Real-time or batch integration—take data from any source and return it to any destination
  • Data enrichment using internal and external sources as well as manual effort as required
• Data Service Applications can be called by any system or process in either real-time or batch mode

Oracle DataLens Governance Studio

• The Governance Studio presents a user interface specifically designed for process governance and data remediation
• The Governance Studio enables:
  
  • Dashboard view of process and data quality metrics—so data stewards can monitor and drive continuous process improvements
  • Data transformation review—allowing product specialists to review recognized and transformed data as required
  • Exception management view—for product specialists to review remaining data problems on an exception basis
  • Match review—for product specialists to review system-generated matches (full review or exception-based)
  • Auto-learning—system creates inference rules for unrecognized data
• The DataLens Governance Studio can be used by a broad audience of Data Stewards and product specialists to monitor and drive data quality for their area of responsibility

Oracle has acquired Silver Creek Systems, Inc. (Silver Creek Systems), a leading provider of product data quality solutions. The combination of Oracle and Silver Creek Systems brings together two companies with complementary products and a common vision of improving information management through a foundation of integrated data quality solutions.