What do we mean when we say "knowledge management"?

A reflection on the meanings and nuances of "knowledge management."

The punch line: knowledge management (KM) is a mindset and process of collaboratively describing a desired future state of affairs, the required information needed to make decisions towards those desired outcomes, and the ecosystem of practices to collect, organize, and interrogate that information.

Given Assumptions

  • Accurate, timely, useful information is vital to organizational success
  • Organizations that value collecting and analyzing information increase the likelihood of making wise decisions
  • Collecting bits of data requires harvesting analog, digital, and human sources
  • All members of the organization or network are responsible to embody KM principles

Ecosystem of Practices

  • Listen to all stakeholder information needs and pain points throughout the iterative cycle of collecting-organizing-evaluating.
  • Collect actionable and accurate data; pass on all other data.
  • Regularly evaluate the efficacy of the KM dashboard and all its data points; make slight corrections and modifications sooner rather than later.
  • Recognize that KM is more than collecting only bits of data. Knowledge is available in other forms: observations, stories, intuition.
  • Match the digital tools to fit the purpose as well as the workflow of the users. Intentionally design a system that meets the needs of the users by intently and purposefully listening to users and their needs.
  • Test and experiment with a subset of the user base then evaluate effectiveness before deploying a new feature to the entire set of users. Be willing to take risks, test, experiment, fail, learn, grow, adapt, evolve.

KM is a thoughtful, intentional ethos surrounding practices designed to determine, unearth, and utilize significant data from a variety of sources all in service of desired outcomes. Stemming from that mindset emerges activities and digital tools aiding in the organization and evaluation of that data in order to determine next steps in the decision making cycle. Of critical importance as well is an intentional effort of evaluation across the data collection life cycle so that corrections to the practices and systems are regular occurrences by design.