Poor information quality wastes monetary resources, squanders data's underlying value to the enterprise, and significantly increases risk in the form of regulatory compliance or litigation.
As one of the most mature of the technologies that supports knowledge management, search solutions have changed so much from the days of keyword searching that they are now often referred to as "insight engines."
Cloud-based KM requires a careful balance of innovation and restraint. The best results ensue when companies combine aggressive adoption of advanced technology with strong central oversight, traditional taxonomy, and flexible implementation to accommodate different parts of the business.
The ability to personalize is an indicator that an organization is managing its content well enough to deliver what each individual needs.
The answer to the problem of digital fragility lies in knowledge centralization
Four strategies to consider for keeping a steady flow of content that consistently meets each customer's unique interests
Executives at leading knowledge management software and services organizations are reflecting on the lasting impact we can expect
Understanding natural language processing, common obstacles faced, and methodologies to overcome them
As with all tools, data has uses because of complex contexts that include other objects, physics, social norms, social institutions, and human intentions.
Moving to a push rather than a pull mentality simply means that we now have the technology to tag, manage, and interpret information automatically and near instantly—automatically pushing the right information to the right person (or application) at the right time.
Given the increased negative media exposure that comes from project failure, organizations need more tightly integrated, intelligent project management systems, in addition to people who have the requisite skills. This need will grow as systems continue to become more complex and timelines more tightly compressed.
No matter how much "intelligence" is programmed into a computer, it will very likely never understand the results it produces. Doing so takes human cognition, intuition, judgment, and other ways we humans make sense out of data.