Narrative to Metrics: Structural Transformation
The Core Objective
The project involved 91 individual strategic initiatives across Montenegro's development landscape (e.g., TO Žabljak tourism development, Marina Bar infrastructure expansion, Rural digitalization programs).
The primary challenge was converting unstructured textual descriptions—often spanning multiple pages of narrative policy documents—into a comparative scoring matrix that enables quantitative analysis. By extracting key parameters such as social impact, EU alignment, environmental sustainability, and implementation risk, we enabled data-driven decision-making across the entire strategic portfolio.
From: Unstructured Text
"Lokalni razvoj omogućava prilagođavanje specifičnim potrebama zajednice, uz fokus na održivi turizam i infrastrukturne projekte..."
To: Quantifiable Metrics
EU Alignment: 65
Risk Factor: 70
Sustainability: 82
Initial State
✕ Prose-Based Documentation
Strategic data was buried in lengthy paragraphs and narrative descriptions, making it impossible to rank projects systematically or identify systemic risks at scale.
✕ Manual Evaluation Risk
Subjective interpretation by different stakeholders led to inconsistent benchmarks across governmental sectors and departments.
✕ No Cross-Comparison
Lack of standardized metrics prevented comparative analysis between similar projects in different municipalities.
Data Lab 42 Solution
✓ Automated Extraction Engine
A Python-based NLP model that translates narrative reasoning into numerical scores, enabling instant comparison across all 91 initiatives.
✓ Standardized Reporting
Transformation of disparate project documents into a uniform, decision-ready format fully aligned with EU Chapters 23 & 27 requirements.
✓ Visual Analytics Dashboard
Interactive visualization system allowing stakeholders to filter, rank, and compare projects based on customizable criteria.
Technical Methodology
To ensure 100% data integrity and system robustness, we developed an adaptive engine using Python with advanced NLP libraries. The system employs defensive programming patterns to handle varied data entry formats and edge cases.
A key technical feature is the .get() logic pattern, which prevents system crashes when dealing with inconsistent data structures. This ensures that every project receives a unique identifier and complete evaluation, even when source documents vary in format or completeness.
# Using .get() ensures no KeyErrors if a column is missing
project_id = row.get('ID', f'AUTO_{index:03d}')
project_name = row.get('Naziv', 'Unnamed Project')
# Dynamic metric extraction with fallback values
social_impact = extract_score(text, 'social') or DEFAULT_SCORE
# Automated header scaling for complex titles
if len(project_name) > 50:
font_size = max(8, 12 - len(project_name) // 10)
# EU alignment scoring with weighted criteria
eu_score = calculate_alignment(text, EU_CHAPTERS[23, 27])
Key Outcomes & Impact
The transformation enabled the government to make evidence-based strategic decisions:
- Priority Ranking: Objective identification of high-impact projects requiring immediate funding
- Risk Assessment: Early detection of implementation challenges across similar project types
- EU Compliance: Automated alignment scoring with acquis communautaire requirements
- Resource Optimization: Data-driven allocation of technical assistance and monitoring resources
Strategic Report Ready
Explore the full transformation result of all 91 projects in our comprehensive analysis report. The document includes detailed methodology, visual analytics, and strategic recommendations.
View Final PDF Report