Integrate Product Data Quality & Governance processes into the overall enterprise workflow
Most workflow tools are of limited use for complex processes because they can not identifying the ‘meaning’ of the product data.
Using Silver Creek’s DataLens™ System, complex workflow processes can be seamlessly combined to integrate and match information from multiple systems as well as perform class-based routing and exception handling. The DataLens Systems is ideal for the following functions.
- Matching & De-duplication
- Problem – One of the toughest and most persistent problems with product data is being able to match and group items that are inconsistently described. This occurs in system consolidations, sourcing and procurement, inventory management, and in everyday response to customer requests. These significant problems have traditionally had no automated solutions.
- DataLens Solution – Disparate product data can be understood and standardized so products can be grouped and matched by their attributes — either fuzzy or precise matching based on business rules.
- Content-Based Workflow
- Problem – Many processes, including pricing, parts creation and inventory management, require different workflow routing based on a fundamental understanding of the item in question. If an item can't be identified, it can't be correctly routed and will require manual intervention.
- DataLens Solution – An item can be accurately grouped for routing and an integrated workflow used to ensure that the right person sees the right information at the right time.
- Exception Handling
- Problem – Some data will not be recognized because data changes continually as items come and go and descriptions are updated. These exceptions must be trapped and resolved to ensure that the same exception is not repeated, resulting in lost time and money.
- DataLens Solution – Each record is given a quality metric and any item can be routed based on its content, quality, or both. Valid items that are not recognized by the system can be used as training samples to extend the coverage of the data lenses and ensure that the item in question — and all others in the same class — will be recognized in the future.
