Content curation involves identifying and tagging enterprise content so that search can elevate the best materials in response to a user inquiry. Good curation strategies also distinguish between content that is best pushed to users to optimize their work vs. what they pull via search. Currently, curation is a primarily manual task and therefore expensive and difficult to keep up with. Machine learning can facilitate curation by using algorithms to analyze and identify similarities among documents, cluster them in groups that make sense, and then tag and display them both proactively and on demand in response to specific search queries.
This use case was co-developed by APQC’s 2015-2016 KM Advanced Working Group, which studied emerging cognitive computing capabilities and their potential impact on knowledge management. APQC would like to thank the Advanced Working Group members for their participation and contributions: Deloitte, EY, NASA, Pfizer Inc., and U.S. Army Training and Doctrine Command (TRADOC).