ORACLE PRODUCT DATA QUALITY

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1 ORACLE PRODUCT DATA QUALITY Even the best Product Information Management (PIM), Master Data Management (MDM) or data integration initiative can be derailed by poor product data quality. Oracle Product Data Quality is built on the latest semantic technology to handle the extreme variability of product data and overcome the limitations of traditional customer data quality solutions. Oracle Product Data Quality can be integrated with any application or process, and is pre-integrated at a semantic level with the SPECIALIZED SEMANTIC TECHNOLOGY FOR PRODUCT DATA QUALITY FEATURES Semantic recognition of structured or unstructured product information Auto-learning improves recognition as a natural part of ongoing operation Integrated governance capabilities for data stewards and product specialists Designed for rules maintenance by business users Advanced transformation capabilities including classification and translation Advanced matching capabilities including semantic matching Best-in-class patented DataLens technology Semantic-level integration to Oracle Product Hub BENEFITS Improve revenue greater agility, searchability, customer satisfaction Increase PIM ROI, reduce implementations cost and risk Reduce manual data cleansing effort Platform for governance, compliance and continuous improvement Oracle Product Hub to reduce the time and cost to deploy and operate your MDM system for product data, or any other system of record while also extending its capability and benefits. Product Data Quality Specific Problems, Specialized Solution Data quality has been recognized for some time as a critical component of any Master Data Management or data integration initiative, but it is all too often under-planned and under-funded and ends up as the greatest single risk factor in the project. When dealing with product data this problem is compounded as traditional data quality tools that were designed for customer (name & address) data struggle with the extreme variability of typical product data. Consequently enterprises are forced to use manual effort or custom code to clean-up their data neither of which is likely to be a good solution. Oracle Product Data Quality was built from the ground up to tackle these problems and to integrate easily with any existing application or process from PLM to procurement and from online retail to asset management and many more. It can be called in real-time or batch mode and can provide PDQ services from simple standardization to match, merge, transformation, translation and more. Oracle Product Data Quality is pre-integrated with Oracle Product Hub making it the first and only MDM for product data solution to offer a dedicated product data quality capability that is both highly capable of dealing with the specific nuances of product data and is fully integrated to the core PIM system. Oracle Product Data Quality the Right Tool for the Job Product data covers tens of thousands of product categories, each of which has a different semantic definition or context (vocabulary, inferences, validations etc.). To make matters worse, within a single category the format of the data can be infinitely variable (word order, punctuation, synonyms, national language, abbreviation etc.). Such variation is beyond the scope of traditional data quality solutions and a different technology approach is required. Oracle Product Data Quality incorporates patented DataLens technology specifically to deal with these issues: Semantic-based Semantic recognition technology handles pattern and category 1

2 THE Translation ADVANTAGE costs reduced OF to a DATALENS fraction of manual TECHNOLOGY effort variations without additional programming. Auto-learning Semantic inference is applied to understand unrecognized data. If confirmed, inferences create new rules and the system continues to learn as a natural consequence of ongoing operation. Scalability Category-based semantic models and in-memory processing allow easy scaling to handle of many millions of items across thousands of categories. WHAT IS A DATA LENS? Like an optical lens, a data lens does not store the data is sees, but simply provides a real-time mechanism to view your data in a different way transformed or re-focused in whatever way you specify. A data lens contains a lot of contextual knowledge about a product category the vocabulary, abbreviations, rules, attributes, valid values and so on. This allows the data lens to recognize important information in context which is important to avoid the ambiguities and false positives that plague non-semantic approaches. In addition, data lenses contain category-specific information on matching, standards for transformation and publication, translation and more. Integrated governance Dedicated interface for data stewards and product specialists to review process effectiveness (quality metrics and KPIs) as well as interact directly with exception data for rapid remediation. Built for business users Allowing the business users who really understand the data to create and maintain rules allows for faster and more precise operation than if IT is forced to be the middle-man in rules creation. Oracle Product Data Quality Capabilities The purpose of Oracle Product Data Quality is to provide an integrated capability to recognize, cleanse, match, govern, validate, correct and repurpose product data from any source to any target requirement. Anyone who has used a traditional DQ toolset knows that dealing with ambiguous data and false positives is a never-ending problem as their pattern-based systems do not take into account context forcing the programmer to compensate with neverending exceptions usually created in procedural if-thenelse code. Not only is this expensive to create and maintain, but it does not even produce a high quality result. Anyone who has tried to do this with other approaches can easily appreciate the value of semantic-based DataLens technology. Figure - Product Data Quality Server footprint The broad functional capabilities of the system are as follows: Semantic Recognition Each product record is compared to a semantic model that can recognize and extract required information through reference to the semantic context. This is done irrespective of variations in input format, punctuation, terminology etc. and can be applied to both structured and unstructured data. This is the core requirement for handling product data and without it the variability of typical product data will cause subsequent processes to fail. In the case where the Oracle Product Hub is used, semantic metadata from the PIM system will be used ensuring that all data loaded to the PIM already conforms to its defined standards. Standardization Enforce any required PIM standards on product data 2

3 PRODUCT DATA VS. CUSTOMER DATA It is sometimes claimed that traditional data quality tools designed for customer (name & address) data can also handle product data, but this is true only in the most simplistic of cases. In reality, product data has a number of characteristics that put it beyond the scope of traditional toolsets. Product data characteristics: Variability Infinite variability even within a category due to word order, punctuation, abbreviations, units of measure, language etc. Many categories there are tens of thousands of product categories, each with its own terminology, standards, inferences, brand names, rules and validations Ambiguity the meaning of some data can only be determined in context either the context of the category or of the individual record Lack of reference information product data reference sources do exist, but they are highly fragmented, change frequently and rarely represent the same standards that a particular company needs to enforce For these reasons, effective automation of product data quality requires a specialized, semantic-based approach that is quite different than that available in traditional customer data quality tools. o Classification Classify an item to the PIM master item class and any number of alternate catalogs which can be based on industry or custom schemas. Supports multiple classifications per item. o Attributes Enforce PIM standards on attribute data including unit of measure conversion, range standardization, including color ranges, terminology and abbreviations. o Descriptions Enforce PIM standards on descriptions including word order, punctuation, terminology etc. For descriptive information that has to fit a target character length without truncation an AutoAbbreviate function can progressively abbreviate the line until it meets the length requirement while maintaining maximum readability. o Translation Attributes and descriptions can be translated into multiple languages with full support for double-byte character sets and parsing left-toright and right-to-left. Matching Support for many individual and hybrid matching methods from simple key matching (with or without cleanup) to semantic matching with full control over mandatory, optional and weighted attributes. A semantic key is created to ensure high performance when de-duplicating an input file, or when matching against PIM production tables. Matched records can optionally be merged and cross references and associations maintained. Enrichment Extract or append information to enrich the item record o Internal extract information embedded in descriptive fields o External append information from legacy systems, subscription sources etc. Repurposing Transform product data into any form for storage in the PIM or for external publication. Capabilities include all those listed under standardization above and can be called as required to avoid redundant storage in the PIM production tables. Governance Data Stewards can access a graphical dashboard of quality metrics and KPIs to give insight on where and how to drive process improvement. The dashboard is fully configurable to monitor transactional data within the system as well as any other source that can be accessed through a SQL query. 3

4 RELATED PRODUCTS AND SERVICES The Oracle Product Data Quality Server uses nextgeneration semantic technology to manage product data quality from recognition of data in any format to cleansing, standardization, match, validation, governance and repurposing. It is available as an optional module to the Oracle Product Hub. ORACLE PRODUCT HUB The Oracle Product Hub is a best in class MDM solution for product data with out of the box support for verticalspecific business processes across a variety of industries Figure - Product Data Quality Dashboard shows KPIs to drive process improvement Exception management Single console for exception management by data stewards or product specialists. Exceptions can be routed to different work queues for manual or automated remediation and validation. Figure - Product Data Quality Console allows product specialists to verify exception data Integrated to Oracle Product Hub Oracle Product Data Quality is designed to integrate easily with any other system or process in real-time or batch mode and is not dependent on any specific system of record, however, since many companies are adopting PIM, or MDM for product data systems as a core component of their ability to manage product data, Oracle Product Data Quality is pre-integrated into the Oracle Product Hub at a semantic level, that is, semantic metadata from the Product Hub is used to seamlessly create the required semantic recognition models. For operational processes such as loading and matching, the Product Data Quality Server has access to both interface and production data tables for maximum operational simplicity and ease of use. A semantic key is created to enable high performance semantic matching against PIM production tables. This level of integration represents unprecedented co-ordination between a PIM system and a dedicated product data quality system. 4

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