San Francisco August 10, 2001 Quiver Inc., a manufacturer of categorization software for enterprise and online content, today announced the first product in the Quiver Knowledge Suite. The product, known as QKS Classifier, is designed to efficiently organize content into an intuitive directory of corporate knowledge. Instead of a purely automated categorization solution, Quiver's hybrid taxonomy platform integrates classification algorithms with a workflow and directory management toolset to capture human input and expertise. Using this technology, the company hopes to offer a solution that automates the process of finding, screening and categorizing content while providing control over and visibility into those categorization results.
"Getting the right document to the right person is critical to increasing informed decision making. Accurately organizing the most current research data into categories that match our R&D [research and development] areas of practice is imperative; our employees cannot take advantage of the content we continually aggregate if they can't find it or worse don't even know it exists," stated David Vielmetter, network architect at Xencor, a biotechnology company.
The Quiver solution acknowledges that the accuracy of technology is limited without the influence of human judgment and contextual analysis. With QKS Classifier, departmental information managers and IT managers have the control and visibility to manage enterprise content by leveraging human judgment as well as technological efficiency.
"In the drive to create portals with better information context for business users, classification technology is increasingly in the center of the discussion," said Hadley Reynolds, research director at portal market expert Delphi Group, Boston, Mass. "The large gorilla in the corner of taxonomy planning is the continuing shortfall in accuracy of the fully automated methods, with its related collapse of credibility in users' experience of the portal. The new Quiver classification software focuses on teaming human smarts and machine processing, which is a refreshing alternative to the more extravagant claims for statistically-driven knowledge operations."