name: gap-analysis description: Identify differences between current state and desired future state. Analyze gaps in capabilities, processes, skills, and technology to plan improvements and investments.

Gap Analysis

Overview

Gap analysis systematically compares current capabilities with desired future state, revealing what needs to change and what investments are required.

When to Use

• Strategic planning and goal setting
• Technology modernization assessment
• Process improvement initiatives
• Skills and training planning
• System evaluation and selection
• Organizational change planning
• Capability building programs

Instructions

1. Gap Identification Framework

# Systematic gap identification

class GapAnalysis:
    GAP_CATEGORIES = {
        'Business Capability': 'Functions organization can perform',
        'Process': 'How work gets done',
        'Technology': 'Tools and systems available',
        'Skills': 'Knowledge and expertise',
        'Data': 'Information available',
        'People/Culture': 'Team composition and mindset',
        'Organization': 'Structure and roles',
        'Metrics': 'Ability to measure performance'
    }

    def identify_gaps(self, current_state, future_state):
        """Compare current vs desired and find gaps"""
        gaps = []

        for capability in future_state['capabilities']:
            current_capability = self.find_capability(
                capability['name'],
                current_state['capabilities']
            )

            if current_capability is None:
                gaps.append({
                    'capability': capability['name'],
                    'gap_type': 'Missing',
                    'description': f"Organization lacks {capability['name']}",
                    'importance': capability['importance'],
                    'impact': 'High' if capability['importance'] == 'Critical' else 'Medium'
                })
            elif current_capability['maturity'] < capability['target_maturity']:
                gaps.append({
                    'capability': capability['name'],
                    'gap_type': 'Maturity',
                    'current_maturity': current_capability['maturity'],
                    'target_maturity': capability['target_maturity'],
                    'gap_size': capability['target_maturity'] - current_capability['maturity'],
                    'importance': capability['importance'],
                    'impact': 'Medium'
                })

        return gaps

    def prioritize_gaps(self, gaps):
        """Rank gaps by importance and effort"""
        scored_gaps = []

        for gap in gaps:
            importance = self.score_importance(gap)
            effort = self.estimate_effort(gap)
            value = importance / effort if effort > 0 else 0

            scored_gaps.append({
                **gap,
                'importance_score': importance,
                'effort_score': effort,
                'value_score': value,
                'priority': self.assign_priority(value)
            })

        return sorted(scored_gaps, key=lambda x: x['value_score'], reverse=True)

    def score_importance(self, gap):
        """Score how important gap is"""
        if gap['importance'] == 'Critical':
            return 10
        elif gap['importance'] == 'High':
            return 7
        else:
            return 4

    def estimate_effort(self, gap):
        """Estimate effort to close gap"""
        # Returns 1-10 scale
        return gap.get('effort_estimate', 5)

    def assign_priority(self, value_score):
        """Assign priority based on value"""
        if value_score > 2:
            return 'High'
        elif value_score > 1:
            return 'Medium'
        else:
            return 'Low'

2. Gap Analysis Template

Gap Analysis Report:

Organization: Customer Analytics Platform
Analysis Date: January 2025
Prepared For: Executive Team

---

Executive Summary:

Current State: Legacy on-premise system with manual processes
Future State: Cloud-native platform with real-time analytics
Gap Magnitude: Significant

Key Findings:
  - 7 critical capability gaps
  - Estimated investment: $500K - $750K
  - Timeline: 12-18 months
  - Primary gaps: Technology, Process, Skills

---

Detailed Gap Analysis:

## Category: Technology

Gap 1: Cloud Infrastructure
  Current: On-premise data center
  Desired: Multi-cloud (AWS primary, Azure backup)
  Gap Size: Large
  Effort: 16 weeks
  Cost: $200K
  Dependencies: None (can start immediately)
  Priority: Critical

Gap 2: Real-Time Data Processing
  Current: Batch processing (nightly)
  Desired: Streaming (sub-second latency)
  Gap Size: Large
  Effort: 20 weeks
  Cost: $150K
  Dependencies: Cloud infrastructure (Gap 1)
  Priority: High

Gap 3: Analytics Tools
  Current: Custom-built dashboard
  Desired: Enterprise BI platform (Tableau/Power BI)
  Gap Size: Medium
  Effort: 8 weeks
  Cost: $80K (software + training)
  Dependencies: Data warehouse modernization
  Priority: High

---

## Category: Skills

Gap 4: Cloud Engineering Expertise
  Current: 0 cloud engineers
  Desired: 3 dedicated cloud engineers
  Gap Size: Large
  Solution: Hire 2, train 1 existing
  Effort: 8 weeks hiring + 4 weeks training
  Cost: $300K annual
  Priority: Critical

Gap 5: Data Science Capability
  Current: 1 analyst (spreadsheet based)
  Desired: 3 data scientists (ML/Python)
  Gap Size: Large
  Solution: Hire 2 data scientists
  Effort: 12 weeks recruiting
  Cost: $400K annual
  Priority: High

---

## Category: Process

Gap 6: Continuous Integration/Deployment
  Current: Manual deployment (quarterly)
  Desired: Automated CI/CD (daily)
  Gap Size: Medium
  Effort: 12 weeks
  Cost: $60K (tools + training)
  Dependencies: Cloud infrastructure
  Priority: High

Gap 7: Data Governance
  Current: Informal, ad-hoc
  Desired: Formal governance framework
  Gap Size: Small
  Effort: 4 weeks
  Cost: $20K (training + tools)
  Dependencies: None
  Priority: Medium

---

## Gap Closure Plan

High Priority Gaps (Start Now):
  1. Cloud Infrastructure - 16 weeks
  2. Cloud Engineering Skills - 8 weeks + training
  3. Data Governance Framework - 4 weeks

Medium Priority Gaps (Start after Cloud ready):
  1. Real-Time Data Processing - 20 weeks (depends on Gap 1)
  2. Analytics Tools - 8 weeks
  3. CI/CD Implementation - 12 weeks

---

Investment Summary:

Capital Expenditure:
  - Cloud infrastructure setup: $200K
  - Technology/tools: $250K
  - Hiring/recruitment: $50K
  - Total CapEx: $500K

Operational Expenditure (Annual):
  - Cloud services: $150K
  - Tool licenses: $80K
  - Salary (3 engineers): $700K
  - Total OpEx: $930K

---

Timeline: 12-18 Months

Q1 2025: Planning & Infrastructure
  - Finalize architecture
  - Begin cloud migration
  - Recruit cloud engineers

Q2 2025: Development & Hiring
  - Cloud infrastructure operational
  - Data engineering foundation
  - Hire data scientists

Q3 2025: Analytics Platform
  - Deploy real-time pipeline
  - Implement BI tools
  - User training

Q4 2025: Production Launch
  - Full platform operational
  - Legacy system decommission
  - Performance optimization

---

Success Metrics:

Before:
  - Query time: 24 hours (batch)
  - Data freshness: 1 day old
  - Cost: $100K/month
  - User satisfaction: 2.5/5

After:
  - Query time: <1 second (real-time)
  - Data freshness: Real-time
  - Cost: $60K/month (40% reduction)
  - User satisfaction: 4.5/5

ROI: Break-even in 18 months

3. Gap Closure Planning

// Create action plans to close gaps

class GapClosurePlanning {
  createClosurePlan(gap) {
    return {
      gap_id: gap.id,
      gap_description: gap.description,
      target_state: gap.target_state,

      approach: gap.gap_type === 'Maturity'
        ? this.createMaturityPlan(gap)
        : this.createCapabilityPlan(gap),

      timeline: {
        start_date: gap.start_date,
        target_completion: gap.target_date,
        duration_weeks: Math.ceil(gap.effort_estimate),
        milestones: this.defineMilestones(gap)
      },

      resources: {
        people: gap.required_staff,
        budget: gap.estimated_cost,
        tools: gap.required_tools
      },

      success_criteria: gap.success_metrics,

      risks: this.identifyClosureRisks(gap),

      dependencies: gap.dependencies
    };
  }

  createMaturityPlan(gap) {
    // Plan for improving existing capability
    return {
      strategy: 'Improve capability maturity',
      phases: [
        {
          phase: 'Assess Current',
          activities: ['Document current state', 'Identify improvement areas'],
          duration: '2 weeks'
        },
        {
          phase: 'Plan Improvements',
          activities: ['Define target maturity', 'Create roadmap', 'Allocate resources'],
          duration: '2 weeks'
        },
        {
          phase: 'Implement',
          activities: ['Execute improvement', 'Training', 'Process changes'],
          duration: gap.effort_estimate + ' weeks'
        },
        {
          phase: 'Validate',
          activities: ['Measure against targets', 'Validate maturity', 'Document learnings'],
          duration: '2 weeks'
        }
      ]
    };
  }

  createCapabilityPlan(gap) {
    // Plan for building new capability
    return {
      strategy: 'Build new capability',
      phases: [
        {
          phase: 'Design',
          activities: ['Define requirements', 'Design solution', 'Get approvals'],
          duration: '4 weeks'
        },
        {
          phase: 'Build',
          activities: ['Develop', 'Test', 'Integrate'],
          duration: gap.effort_estimate + ' weeks'
        },
        {
          phase: 'Deploy',
          activities: ['Pilot', 'Roll out', 'Support transition'],
          duration: '4 weeks'
        }
      ]
    };
  }

  defineMilestones(gap) {
    return [
      { name: 'Gap closure initiated', date_offset: 'Week 0' },
      { name: 'First deliverable', date_offset: `Week ${Math.ceil(gap.effort_estimate / 3)}` },
      { name: 'Mid-point review', date_offset: `Week ${Math.ceil(gap.effort_estimate / 2)}` },
      { name: 'Final validation', date_offset: `Week ${gap.effort_estimate}` }
    ];
  }
}

4. Communication & Tracking

Gap Analysis Communication:

Stakeholder Updates:

Executive Summary (1 page):
  - What gaps exist?
  - Why do they matter?
  - What's the investment?
  - When will we close them?

Detailed Report (10 pages):
  - Gap identification methodology
  - Gap descriptions and impacts
  - Priority and sequencing
  - Detailed closure plans
  - Risk assessment

Team Briefing (30 min):
  - Overview of gaps
  - Impact on team
  - Their role in closure
  - Timeline and changes

---

Tracking Dashboard:

Gap 1: Cloud Infrastructure
  Status: In Progress (40%)
  Timeline: On track
  Budget: On budget ($200K allocated, $80K spent)
  Next Milestone: Infrastructure provisioning (due Feb 15)

Gap 2: Cloud Engineering Skills
  Status: Not started
  Timeline: At risk (delayed by hiring)
  Budget: On budget
  Next Milestone: 2nd engineer hire (due Feb 28)

Gap 3: Data Governance
  Status: Completed
  Timeline: Complete
  Budget: Under budget ($18K vs $20K)
  Business Impact: 30% improvement in data quality

Best Practices

✅ DO

• Compare current to clearly defined future state
• Include all relevant capability areas
• Involve stakeholders in gap identification
• Prioritize by value and effort
• Create detailed closure plans
• Track progress to closure
• Document gap analysis findings
• Review and update analysis quarterly
• Link gaps to business strategy
• Communicate findings transparently

❌ DON'T

• Skip current state assessment
• Create vague future state
• Identify gaps without solutions
• Ignore implementation effort
• Plan all gaps in parallel
• Forget about dependencies
• Ignore resource constraints
• Hide difficult findings
• Plan for 100% effort allocation
• Forget about change management

Gap Analysis Tips

• Involve people doing the work
• Be realistic about effort estimates
• Start with highest-value gaps
• Build dependencies and sequencing
• Monitor progress weekly