Proactive Analytics Automate the Knowledge Maintenance Process
Date: March 27, 2015
Author: John Ragsdale
According to TSIA’s most recent knowledge management survey, nearly half of technology companies have had three or more employee-facing knowledge management (KM) systems in recent memory, and with 69% of support organizations planning a KM purchase in the next one to two years, clearly this cycle of “rip and replace” is not slowing down. But why do so many KM programs fail? The root cause, based on years of research and member conversations, is often lack of ongoing maintenance. Although the program may get off to a great start, by year two interest and resources begin to wane, content grows stale and unreliable, and both employees and customers click elsewhere for information.
Analytics can turn this trend around. This report will look at the role of analytics in improving and automating knowledge maintenance, with a focus on three areas: content gap analysis, knowledge usage analysis, and relevancy analysis. Companies launching a new KM product and/or process, or those trying to keep a mature program going, should understand how analytics can improve knowledge management success and create a road map to introduce best-of-breed analytics into the program in 2015.
Education Services;Field Services;Professional Services;Service Revenue Generation-Recurring;Managed Services;Support Services;Customer Success
analytics, knowledge management, dashboards, intelligent search, unified search, federated search, gap analysis
Consumer Technologies;Enterprise IT and Telecommunications;Healthcare and Healthcare IT;Industrial Equipment