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Product data is more important now than ever before. Of course it's always been important. It's been the basis of all types of sales, inventory and manufacturing systems since the beginning of IT. But now, with the advent of SOA and a wide range of ever-broader business requirements, having the right product data in the right form at the right time is more important than ever before. In addition, it can't be just any product data - it has to be consistent, reliable and accurate. For most organizations, that's no easy task.

Let's take a closer look at the issue of product data reliability and business needs.

At the same time that organizations have been expanding their use of SOA, the business needs for accurate and reliable product data have expanded. Changes in online commerce, multi-channel commerce, globalization, in-store systems and even business intelligence and more sophisticated merchandising systems have all driven the need for greater accuracy and reliability from the data flowing through, and created in, an organization.

An organization's ability to use data, such as product data, effectively can have a big impact on everything from the cost of operations to an organization's ability to react quickly to market changes. Incomplete or unstructured data can prevent effective communication and reduce the effectiveness of SOA strategies.

Most organizations have product information flowing in from a wide variety of outside sources, including: supplier portals, manufacturer feeds, merchandizing information, legacy systems and much more. Frequently the data sources include hundreds and even thousands of different manufacturer feeds in different formats, describing different types of products, with a huge range of variation. "Standards" for product data depend on what type of product you're talking about. For example, resistors have a different "normalized" schema than motors or handbags-different attributes, different validations, different vocabulary, different abbreviations, etc. In order to manage product data effectively, and organizations needs to be able to address and react to all those different requirements. You also need to be able to integrate and consolidate that data in way that makes sense.

As a result, product data integration requirements are almost everywhere. Integration (and consolidation) of product data is also required for a wide variety of increasingly critical front-end and back-end functions. For example, in retail, integrated and rationalized product data is required for guided search and navigation capabilities, product data enhancement, multi-channel consistency, customer catalogs and other requirements. In distribution systems it's required for inventory consolidation, quote matching and more.


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