There are a number of sources of valuable feedback. A rapid publishing model for articles (90% of what we know published in 90 min or less) must have an effective feedback mechanism like allowing requestors to rate and comment on articles. The KDEs look after organizing, analyzing, and in some cases, acting on the article feedback. There are also great insights that can be gained by analyzing the content and sentiment in communities, forums, and social media.
The KDEs' initial focus is usually on article feedback. The KDEs must understand and influence the processes by which article feedback is prompted, collected, and made available to the appropriate parties. While a KDE might not be the one to act on specific feedback, they do need to be paying attention to trends and patterns that emerge from the collection of feedback.
In more mature Knowledge Domain Analysis programs, the KDEs also look for patterns and trends that are emerging from communities, forums, and social media content and activity, and may even contribute to those channels by drawing attention to high-value articles.
The volume of feedback is often too large to manually analyze effectively. The analysis of article feedback as well as the content and sentiment in communities and social media is greatly enhanced through the use of text analytic tools and machine learning models.
The patterns and trends that emerge can be a valuable complement to the learnings from the other Knowledge Domain Analysis activities. Equally important is communicating the feedback to the knowledge workers and leaders in the organization. This is especially true for the positive comments. Helping knowledge workers see the value they are creating by contributing to the knowledge base (and how that knowledge is leveraged outside of assisted support) is an important part of what keeps them engaged and contributing.