Evolve Loop content processes are critical for continuous learning, innovation, and improvement. They leverage the Solve Loop content, create incremental value for the organization, and help to elevate awareness and sensitivity to the requestor or customer experience in the organization. As the organization matures in its use of KCS, Evolve Loop content is managed by an important function: Knowledge Domain Analysis. This critical function assures that issues are resolved effectively and efficiently, while also gathering and communicating data about the impact KCS in having in the organization.
To help maximize the benefits of KCS, Knowledge Domain Analysis focuses on the knowledge base and pays attention to the quality of the articles, the effectiveness of the workflow that produces and improves the articles and, perhaps most importantly, the use of the articles.
The knowledge workers doing this function, Knowledge Domain Experts (KDEs) must have both deep subject matter expertise as well as a profound understanding of KCS. KDEs look after the health of a collection or domain of knowledge, usually a subset of the knowledge base that aligns with their expertise.
The KDE seeks to optimize the creation, improvement, and use of articles as well as identify patterns and trends of reuse to identify potential product, process, or policy changes that could eliminate the root cause of the most frequent issues. Based on the analysis, the KDEs work with Coaches and the KCS Council to improve the content standard and the KCS workflow. Success of the Knowledge Domain Analysis function is measured through improvements in findability, self-service use, and success rates and incident volume reduction that is a result of corrective actions taken to eliminate the cause of pervasive issues.
For a detailed look at what's involved in Knowledge Domain Analysis, including creating Evolve Loop articles and conducting a New vs. Known analysis, please visit the KCS v6 Knowledge Domain Analysis Reference Guide.
Knowledge Domain Analysis outputs include the identification of :
- Improvements to the content standard and process integration (workflow)
- Findability issues: knowledge exists but is not being found - search performance and optimization
- Content gaps: knowledge people are looking for that does not exist
- Content overlaps: consolidating duplicate articles, identifying the best or preferred resolution among many proposed resolutions
- Improvements in how we leverage known issues, eliminating re-work, improving access and findability
- Improvements in how we solve new issues, suggestions for problem solving and collaboration to solve new issues quickly
- Pervasive issues: facilitating root cause analysis and working with business owners on high impact improvements
- Value of the knowledge base, such as article reuse rates, self-service success, and contribution in improving time to resolve
- Archiving strategy for the knowledge base