Gap Grouping

Knowledge Gap Grouping and Categorization

Overview

This skill builds on gap analysis by grouping, clustering, and categorizing identified knowledge gaps into actionable themes. Individual gaps in isolation are hard to prioritize, but when clustered by topic, service area, or user impact, patterns emerge that reveal systemic documentation deficiencies requiring coordinated action.

This skill helps you:

• Collect and normalize knowledge gap data from multiple sources (incidents, search logs, feedback, submissions)
• Cluster related gaps by service area, category, configuration item, and assignment group
• Identify systemic documentation deficiencies across service domains
• Prioritize gap clusters by aggregate impact, frequency, and business criticality
• Create structured action plans assigning gap clusters to knowledge authors with deadlines
• Track progress on gap remediation efforts

When to use: After completing a gap analysis, during quarterly knowledge planning, when building a knowledge creation backlog, or when staffing knowledge authoring sprints.

Prerequisites

• Roles: knowledge_manager or knowledge_admin for full workflow; knowledge for read-only analysis
• Access: Read access to incident, kb_knowledge, kb_submission, search_log, kb_feedback tables; write access to kb_submission and kb_knowledge
• Plugin: com.glideapp.knowledge (Knowledge Management) activated
• Recommended: Completed gap analysis (see knowledge/gap-analysis skill) providing raw gap data
• Knowledge: Understanding of your organization's service catalog taxonomy and knowledge ownership model

Procedure

Step 1: Collect Raw Gap Data from Incidents

Retrieve a bounded, field-limited sample of resolved incidents without linked KB articles and aggregate it locally to identify volume-based gaps.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: opened_at>=javascript:gs.daysAgoStart(90)^state=6^kb_knowledgeISEMPTY^close_notesISNOTEMPTY
  fields: sys_id,category,subcategory,assignment_group
  limit: 1000
  instance: dev

Count rows locally by (category, subcategory, assignment_group), sort descending, and retain the top 50. Paginate deliberately if the sample limit is reached and complete coverage is required.

Using REST API:

GET /api/now/stats/incident?sysparm_query=opened_at>=javascript:gs.daysAgoStart(90)^state=6^kb_knowledgeISEMPTY^close_notesISNOTEMPTY&sysparm_group_by=category,subcategory,assignment_group&sysparm_count=true&sysparm_orderby=COUNT&sysparm_limit=50

Step 2: Collect Gap Data from Failed Searches

Retrieve bounded failed-search rows and count normalized search_term values locally to find clusters of unmet user needs.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: search_log
  query: sys_created_on>=javascript:gs.daysAgoStart(90)^results_count=0^search_application=knowledge
  fields: sys_id,search_term
  limit: 1000
  instance: dev

Using REST API:

GET /api/now/stats/search_log?sysparm_query=sys_created_on>=javascript:gs.daysAgoStart(90)^results_count=0^search_application=knowledge&sysparm_group_by=search_term&sysparm_count=true&sysparm_orderby=COUNT&sysparm_limit=40

Step 3: Collect Gap Data from Knowledge Submissions

Review pending knowledge submissions (requests for new articles) to capture user-reported gaps.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: kb_submission
  query: state=pending^ORstate=open^sys_created_on>=javascript:gs.daysAgoStart(180)
  fields: sys_id,number,short_description,description,kb_knowledge_base,category,assignment_group,priority,sys_created_on
  limit: 40

Using REST API:

GET /api/now/table/kb_submission?sysparm_query=state=pending^ORstate=open^sys_created_on>=javascript:gs.daysAgoStart(180)&sysparm_fields=sys_id,number,short_description,description,kb_knowledge_base,category,assignment_group,priority,sys_created_on&sysparm_limit=40

Step 4: Collect Gap Data from Negative Article Feedback

Find articles receiving negative feedback, which may indicate incomplete coverage of a topic area.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: kb_feedback
  query: sys_created_on>=javascript:gs.daysAgoStart(90)^rating=not_helpful^ORrating=1
  fields: sys_id,article,article.number,article.short_description,article.kb_category,comments,sys_created_on
  limit: 40

Using REST API:

GET /api/now/table/kb_feedback?sysparm_query=sys_created_on>=javascript:gs.daysAgoStart(90)^rating=not_helpful^ORrating=1&sysparm_fields=sys_id,article,article.number,article.short_description,article.kb_category,comments,sys_created_on&sysparm_limit=40

Step 5: Define Grouping Dimensions

Cluster the collected gaps using these dimensions:

Grouping Taxonomy:

Step 6: Cluster Gaps by Service Area

Map each gap to a service area cluster. Query existing KB categories to align clusters with the knowledge base taxonomy.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: kb_category
  query: kb_knowledge_base=[primary_kb_sys_id]^active=true^parent_id=NULL
  fields: sys_id,label,full_category,kb_knowledge_base
  limit: 30

Using REST API:

GET /api/now/table/kb_category?sysparm_query=kb_knowledge_base=[primary_kb_sys_id]^active=true^parent_idISEMPTY&sysparm_fields=sys_id,label,full_category,kb_knowledge_base&sysparm_limit=30

Map incident categories to KB categories:

Step 7: Score and Prioritize Gap Clusters

Apply a composite scoring model to rank gap clusters by urgency and impact.

Cluster Scoring Formula:

Priority Tiers:

• Critical (Score 4.0-5.0): Immediate action required; assign to next sprint
• High (Score 3.0-3.9): Plan for current quarter
• Medium (Score 2.0-2.9): Schedule for next quarter
• Low (Score 1.0-1.9): Add to backlog, address opportunistically

Step 8: Identify Knowledge Authoring Ownership

Determine which team or individual should own each gap cluster based on assignment group patterns.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: incident
  query: opened_at>=javascript:gs.daysAgoStart(90)^state=6^category=network^kb_knowledgeISEMPTY
  fields: sys_id,assignment_group
  limit: 1000
  instance: dev

Count the bounded rows locally by assignment_group, then keep the top 10.

Using REST API:

GET /api/now/stats/incident?sysparm_query=opened_at>=javascript:gs.daysAgoStart(90)^state=6^category=network^kb_knowledgeISEMPTY&sysparm_group_by=assignment_group&sysparm_count=true&sysparm_limit=10

The assignment group resolving the most incidents for a gap cluster is the best candidate to author the knowledge articles.

Step 9: Create Action Plan Records

Create knowledge submission records for each gap cluster with clear scope and deadlines.

Using MCP:

Tool: SN-Create-Record
Parameters:
  table_name: kb_submission
  data:
    short_description: "Gap Cluster: Network Connectivity Troubleshooting (5 articles needed)"
    description: |
      === KNOWLEDGE GAP CLUSTER ACTION PLAN ===
      Cluster: Infrastructure - Network & Connectivity
      Priority: Critical (Score: 4.3/5)

      Gap Items:
      1. DNS configuration troubleshooting (47 incidents, 23 failed searches)
      2. VPN split tunnel configuration (31 incidents, 15 failed searches)
      3. Wireless network authentication errors (28 incidents, 12 failed searches)
      4. Network printer connectivity (22 incidents, 8 failed searches)
      5. Proxy configuration for remote users (18 incidents, 10 failed searches)

      Total Incident Impact: 146 incidents in 90 days
      Estimated Articles: 5 new, 2 updates to existing
      Target Completion: 2026-04-30
      Assigned Team: Network Operations

      Success Criteria:
      - All 5 articles published and indexed
      - Failed search rate for cluster topics reduced by 50%
      - Incident-to-KB attachment rate >60% for network category
    kb_knowledge_base: [target_kb_sys_id]
    category: Network
    assignment_group: Network Operations
    priority: 1

Using REST API:

POST /api/now/table/kb_submission
Content-Type: application/json

{
  "short_description": "Gap Cluster: Network Connectivity Troubleshooting (5 articles needed)",
  "description": "=== KNOWLEDGE GAP CLUSTER ACTION PLAN ===\nCluster: Infrastructure...",
  "kb_knowledge_base": "[target_kb_sys_id]",
  "category": "Network",
  "assignment_group": "[group_sys_id]",
  "priority": "1"
}

Step 10: Track Gap Remediation Progress

Periodically check how many gaps in each cluster have been addressed by querying newly created articles.

Using MCP:

Tool: SN-Query-Table
Parameters:
  table_name: kb_knowledge
  query: sys_created_on>=javascript:gs.daysAgoStart(30)^kb_category.label=Network^workflow_stateINdraft,review,published
  fields: sys_id,number,short_description,workflow_state,kb_category,author,sys_created_on
  limit: 20

Using REST API:

GET /api/now/table/kb_knowledge?sysparm_query=sys_created_on>=javascript:gs.daysAgoStart(30)^kb_category.label=Network^workflow_stateINdraft,review,published&sysparm_fields=sys_id,number,short_description,workflow_state,kb_category,author,sys_created_on&sysparm_limit=20

Tool Usage

MCP Tools Reference

REST API Reference

Best Practices

• Group Before You Prioritize: Individual gaps are noisy; clustering reveals the true priorities and prevents fragmented effort
• Align to KB Taxonomy: Map gap clusters to existing knowledge base categories so articles land in the right place
• Assign Ownership by Expertise: The team that resolves the incidents is best positioned to write the articles; use assignment group data to identify authors
• Set Realistic Timelines: A typical knowledge author can produce 2-3 quality articles per week; plan cluster timelines accordingly
• Define Success Metrics: Each action plan should include measurable criteria (search hit rate improvement, incident deflection, feedback scores)
• Review Quarterly: Gap clusters shift as the organization evolves; re-run the analysis each quarter and adjust priorities
• Start with Quick Wins: Within each cluster, identify gaps that can be filled by repurposing existing close notes or problem workarounds

Troubleshooting

"Gap clusters are too broad -- every gap falls into the same category"

Cause: Incident categorization is too coarse (e.g., everything is "Software") Solution: Use subcategory, CI, or assignment group as additional dimensions. If subcategories are sparse, cluster by keyword patterns in short descriptions

"Cannot determine knowledge authoring ownership"

Cause: No clear assignment group pattern; incidents spread across many teams Solution: Assign to the Knowledge Management team as a centralized authoring function, or identify a subject matter expert through the most frequent resolver

"Pending knowledge submissions are stale and outdated"

Cause: Submissions were created but never triaged or assigned Solution: Filter submissions by age and close those older than 12 months that are no longer relevant. Focus on submissions from the past 6 months

"Gap cluster priorities change frequently"

Cause: New incident spikes or organizational changes shift priorities Solution: Use a rolling 90-day window for scoring to capture current trends. Re-score monthly and communicate changes to stakeholders

"Action plan records are not being tracked"

Cause: No workflow or reporting on knowledge submission records Solution: Create a simple dashboard or scheduled report tracking kb_submission records by state, assignment group, and age

Examples

Example 1: Infrastructure Gap Cluster

Cluster: Infrastructure - Network & Connectivity Score: 4.3/5 (Critical)

Gaps Identified:

Action Plan: 5 new articles, assigned to Network Operations, target completion 4 weeks. Start with DNS and VPN as highest volume.

Example 2: End-User Computing Gap Cluster

Cluster: End-User Computing - Applications Score: 3.5/5 (High)

Gaps Identified:

Action Plan: 4 new articles + 2 article updates, assigned to Desktop Support knowledge authors, target completion 3 weeks. Prioritize Office 365 migration content due to ongoing rollout.

Example 3: Customer-Facing Gap Cluster

Cluster: Customer Service - Product Support Score: 4.1/5 (Critical)

Gaps Identified:

Action Plan: 3 new customer-facing articles for external KB, assigned to Customer Service knowledge team. Critical priority due to direct customer impact and self-service deflection opportunity. Target completion 2 weeks.

Related Skills

• knowledge/gap-analysis - Identify individual knowledge gaps (prerequisite for this skill)
• knowledge/article-generation - Generate articles to fill gaps in each cluster
• knowledge/duplicate-detection - Prevent duplicates when creating articles for gap clusters
• knowledge/content-recommendation - Validate that new articles are surfaced for relevant incidents