Developing a Comprehensive Aviation Safety Data Analysis System

Executive summary

In today's dynamic aviation industry, safety is paramount and data-driven decision-making is more vital than ever. Aviation Safety Management Systems generate substantial volumes of structured and unstructured data — incident reports, crew observations, maintenance records, flight data — that most operators analyse manually or in isolation.

This whitepaper presents a comprehensive framework for building an integrated Aviation Safety Data Analysis System (ASDAS) that combines crew reporting analytics, Python-based data pipelines, and Power BI visualisation into a coherent, automated intelligence system.

The framework is designed to be implemented incrementally — organisations can adopt individual components without committing to a full-scale transformation on day one.

1. The state of aviation safety data

Aviation is among the most data-rich industries in existence. A single medium-sized operator may generate thousands of safety reports per year alongside continuous streams of flight data monitoring (FDM) events, maintenance defects, and air traffic control records.

Despite this, the analytical infrastructure at most operators lags behind the data volume. Common limitations include:

• Safety reports stored in SMS tools but analysed manually in spreadsheets
• Reporting cycles that produce retrospective insights rather than leading indicators
• Siloed data sources that are never correlated (e.g. FDM events never linked with crew reports)
• Dashboard tools that visualise data but don't surface insights automatically

The result is that operators with excellent safety cultures — high reporting rates, thorough investigation processes — still struggle to extract the predictive intelligence that high-volume reporting should make possible.

2. Framework architecture

The ASDAS framework operates across four functional layers:

Layer 1 — Data acquisition

Automated ingestion of SMS exports (Coruson, Empowerment, IQSMS, or similar), FDM event files, maintenance records, and external reference data (weather, ATC, airport statistics). Python scripts handle extraction, format normalisation, and scheduling.

Layer 2 — Data transformation

Structured transformation using Python (pandas) and Power Query. Key transformations include date normalisation, taxonomy alignment (ICAO ADREP, HFACS), risk scoring calculation, and text classification for free-narrative fields.

Layer 3 — Data modelling

A Power Pivot or Power BI data model that connects transformed tables through defined relationships. A shared Calendar table enables all time-intelligence calculations. DAX measures provide context-aware metrics (frequency rates, risk scores, period comparisons).

Layer 4 — Reporting and alerting

Power BI dashboards structured for three audiences: executive/SRB level (headline KPIs and trends), operational safety team (drill-down analysis), and compliance management (action tracking and overdue items). Automated alerts trigger when KPIs breach defined thresholds.

3. Crew reporting analytics in depth

Crew reports — cabin crew safety reports, flight crew observations, and voluntary occurrence reports — are the primary input to this system for most operators. Their analytical value is high but underutilised because:

• Free-text narratives require interpretation, traditionally done manually
• Classification systems (ADREP categories, phase of flight, contributing factors) are often inconsistently applied
• Cross-fleet and cross-base comparisons are rarely done systematically

Automated classification pipeline

The framework includes an optional classification enrichment module using a large language model (LLM) API. Sending report narratives to a model with structured prompts can automatically assign event categories, extract phase-of-flight mentions, and flag potential precursor patterns at scale.

import anthropic

client = anthropic.Anthropic()

def classify_report(narrative_text):
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        max_tokens=300,
        messages=[{
            "role": "user",
            "content": f"""Classify this aviation safety report.
Return JSON only with keys: event_category, phase_of_flight, contributing_factor.
Use ICAO ADREP taxonomy where applicable.

Report: {narrative_text}"""
        }]
    )
    return response.content[0].text

This doesn't replace analyst judgement but dramatically reduces the triage workload on high-volume reporting databases, allowing safety professionals to focus on investigation rather than sorting.

4. Frequency analysis and leading indicators

The most actionable safety intelligence comes from frequency analysis — tracking not just what happened, but at what rate, compared to previous periods, and whether that rate is changing.

Key frequency metrics implemented in the framework:

• Events per 1,000 sectors: Normalises for changes in operations volume, enabling meaningful period comparisons
• Risk-weighted frequency: Weights events by severity × likelihood score, rather than treating all events equally
• Rolling 12-month trend: Smooths seasonal variation to reveal genuine directional trends
• Cluster detection: Identifies statistically unusual concentrations of events by aircraft type, route, crew base, or time period

5. Implementation roadmap

The framework is designed to be adopted incrementally:

6. Regulatory alignment

The framework is designed to support compliance with ICAO Annex 19 Safety Management requirements and EASA Part-OPS and Part-CAMO safety performance monitoring obligations. Specifically, the reporting layer produces outputs suitable for:

• Safety Performance Indicators (SPI) monitoring
• Safety Performance Targets (SPT) tracking
• Safety Review Board (SRB) quarterly reporting packages
• Regulatory authority oversight submissions

Conclusion

The data required to build a predictive aviation safety intelligence system already exists in most operators' SMS infrastructure. The gap is analytical — the frameworks, tools, and automation to transform raw report volumes into timely, structured intelligence.

The ASDAS framework presented in this whitepaper addresses that gap using tools widely available across the industry: Python for automation, Excel and Power Pivot for analysis, and Power BI for reporting. The result is a safety intelligence capability that scales with reporting volume rather than being overwhelmed by it.