The Problem

Have you ever found yourself searching for information with a typical search tool by typing a few key words and then watching as hundreds or thousands of web sites, documents or messages are returned for you to peruse? In today's market, workers are expected to be "subject matter experts" or "knowledge workers," yet the tools provided for content retrieval are not up to the challenges inherent in the language we speak.

Most enterprise data (knowledge) is no longer created, shared, and stored as a managed resource. Instead, it principally resides across multiple, seemingly disjointed systems, such as e-mail, chat (real-time), database, multimedia (e.g., videoconferencing), voicemail, and document management systems. Current knowledge management applications fall short of bridging this digital divide by keeping track of the data but not telling the knowledge worker much of anything about the content of the data; these applications can tell you where your information is, but they can not tell you whether the information is relevant to your needs.

Market research firm IDC estimates that knowledge workers may lose as many as three hours per day, or about 30% of their available effort, searching for the information they need to do their jobs. This loss of worker productivity is underscored by the fact that 90% of all corporate intellectual property and assets are today created in the digital domain.

The Result: The average knowledge worker is floating in an ocean of lost data that isn't effectively used; poor management of data resources results in a loss of profit, productivity and opportunity.
If knowledge management applications could be automated to understand the written language, knowledge workers would save valuable time and companies would realize double digit improvement to productivity, and corresponding balance sheet benefits.

Next: The Solution