Similarity Systems Launches Data Quality Solution
For Consumer Packaged Goods Manufacturers

ATHANOR CPG for Monitoring, Validation and Cleansing of
Trading Partner and Item Data

Dublin, Ireland -- April 20, 2005 -- Similarity Systems™, the leading provider of business-focused Data Quality solutions has launched ATHANOR CPG™, a new Data Quality offering combining technology, services, and reference data for the Consumer Packaged Goods (CPG) manufacturing sector.

ATHANOR CPG, which provides a complete Data Quality solution for CPG manufacturers, is based on Similarity Systems’ leading Data Quality management product, ATHANOR™. The solution also comprises a package of Data Quality rules and reference data for the sector coupled with Similarity Systems’ methodologies for continuous Data Quality improvement. The methodologies were developed through hands-on experience in CPG organizations.

According to a recent survey carried out by consulting firm AT Kearney, CPG manufacturers consider internal data cleansing as the most significant barrier to Global Data Synchronization (GDS). “Improving the quality of item and trading partner data has become an urgent priority for CPG manufacturers. Poor Data Quality is a pervasive problem that is proving to be a significant roadblock in the way of eBusiness initiatives such as Global Data Synchronization and RFID,” said Garry Moroney, chief executive officer, Similarity Systems.

Low-quality data already generates significant problems throughout the supply chain, new product introductions, promotions, order taking, fulfillment, logistics, pricing, and invoicing. These problems directly affect the bottom line by introducing the need for re-work, costly manual processes, extra stock being carried, or resulting in missed sales through out of stock situations and poor customer service. Further automation will only accelerate the rate at which data-related problems can occur, while implementation of applications such as product information management (PIM) to provide centralized management of product data also increases the need for Data Quality measurement, validation and cleansing processes.

Similarity Systems’ ATHANOR CPG was developed in conjunction with a number of global supply-side organizations to enable CPG companies to achieve rapid and cost effective Data Quality improvement. Similarity Systems is also working closely with systems integrator partners such as Capgemini to deliver Data Quality solutions to CPG companies. “Accurate and consistent item data is the cornerstone for an efficient, collaborative supply chain. It affects every aspect of the business -- including sales, buying/merchandizing, supply chain and finance,” said Kees Jacobs, principal consultant, Capgemini. “If the Data Quality is poor initiatives aimed at automating the supply chain will not deliver the promised benefits.”

With ATHANOR CPG manufacturing companies have access to a complete Data Quality solution that enables them to identify and resolve Data Quality and consistency issues across the entire organization and all critical data types. The solution empowers business owners to audit and monitor Data Quality across multiple applications to ensure conformance with internal and external standards.

ATHANOR CPG is a one-stop-shop for Data Quality monitoring, standardization, de-duplication, and consolidation within the firewall of CPG manufacturing organizations. It includes industry-specific reference content, such as GS-1 (EAN/UCC) product requirements, validation and consistency checking rules, and standard Data Quality plans and scorecards to enable CPG manufacturers to deploy a consistent Data Quality solution across the entire organization. The solution encompasses all of a company’s critical data including master data stored in enterprise resource planning, product information management (PIM), CRM, warehouse management, and business intelligence systems.

The solution enables information owners and data analysts to take control of enterprise-wide Data Quality. ATHANOR’s centrally controlled, locally managed and distributed architecture means that data inconsistencies can be monitored centrally and resolved in source systems, close to the originating processes, by people who understand the data.

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