AgenticX5 by GenAISafety

HSE Data Preparation Guide

Comprehensive guide for preparing data for AI projects in industrial occupational health and safety. Built on international standards and industry best practices.

1.2M+
OHS Incidents
100+
AI Agents
95%
Metadata Coverage
24/7
Real-time Monitoring
📚

1. Introduction & Objectives

Purpose and scope of this HSE data preparation guide

▼
📌 Guide Purpose
This guide provides a comprehensive framework for preparing, managing, and validating HSE (Health, Safety & Environment) data for artificial intelligence projects in the AgenticX5 ecosystem.

🎯 Main Objectives

✅

Data Quality

Ensure high quality, complete, accurate, and consistent HSE data to maximize the effectiveness of AI-based solutions.

🔄

Interoperability

Implement international standards (Dublin Core, DDI, ISO 11179) to facilitate data exchange and cross-jurisdictional harmonization.

🔐

Compliance

Respect privacy regulations (Law 25, GDPR) and OHS standards (ISO 45001, C-25) to ensure ethical governance.

⚡

Scalability

Design a modern data architecture (Modern Data Stack) capable of handling millions of records in real-time.

🎓 Target Audience

  • Data Scientists: To understand data structure and prepare ML/AI features
  • Data Engineers: To implement robust and automated data pipelines
  • HSE Specialists: To validate semantic quality and compliance of data
  • Project Managers: To plan and coordinate data preparation phases
  • Governance Teams: To ensure compliance and traceability
🚀 Expected Benefits
  • Reduction in data preparation time by 60%
  • Improvement in model accuracy by 25%
  • Increase in metadata coverage to ≥95%
  • Complete data lineage for 100% auditability
🏷️

2. Metadata Standards & Dublin Core

International standards for interoperability and data discovery

▼

📋 International Standards

🌐

Dublin Core (DC)

Priority 1 Universal

Lightweight and generic metadata schema with 15 core elements, widely used for data discovery and exchange. Ideal for cross-domain interoperability.

  • Coverage: Title, Creator, Subject, Description, Publisher, Date, Type, Format, Identifier, Source, Language, Relation, Coverage, Rights
  • Use Cases: Data catalogs, open data portals, digital archives
📊

DDI (Data Documentation Initiative)

Research Advanced

Detailed standard for documenting research and statistical data. Ensures methodological reproducibility and traceability.

  • Coverage: Study design, sampling methods, variable definitions, processing workflows
  • Use Cases: Scientific datasets, surveys, longitudinal studies
🗂️

ISO 11179

Semantic

International standard for metadata registries. Ensures consistency, semantic coherence, and data quality.

  • Coverage: Data element definitions, controlled vocabularies, concept relationships
  • Use Cases: Enterprise data dictionaries, data governance
📁

DCAT (Data Catalog Vocabulary)

Open Data

RDF vocabulary for describing data catalogs and datasets. Facilitates aggregation and federation of data portals.

  • Coverage: Dataset descriptions, distributions, access endpoints, temporal coverage
  • Use Cases: Government open data portals, data marketplaces

🔑 Dublin Core - 15 Core Elements

Element Description Example (OHS Incident)
dc:title Title or name of the resource Fall from height - Construction Site A
dc:creator Entity responsible for creating the resource CNESST Inspector - Jean Tremblay
dc:subject Topic or keywords Fall, Construction, Safety, Prevention
dc:description Abstract or summary Worker fell 3 meters from scaffold due to missing guardrails
dc:publisher Entity responsible for making the resource available CNESST - Québec
dc:contributor Entity contributing to the resource Site Safety Manager
dc:date Date associated with the resource 2024-03-15T14:30:00Z
dc:type Nature or genre of the resource Incident Report
dc:format File format or media type application/json
dc:identifier Unique identifier CNESST-2024-001234
dc:source Related resource from which the current resource is derived Initial investigation report #98765
dc:language Language of the resource fr-CA (French - Canada)
dc:relation Related resource Safety alert #2024-045
dc:coverage Spatial or temporal coverage Montreal, QC / Q1 2024
dc:rights Information about rights © CNESST 2024 - Confidential
💡 Practical Tip
Dublin Core allows each element to have qualifiers to refine its meaning. For example:
  • dc:date.created vs dc:date.modified
  • dc:coverage.spatial vs dc:coverage.temporal

🔗 Multi-Jurisdictional Harmonization

📍 Industry Classifications

To ensure interoperability between Canada, USA, and Europe:

Country/Region Classification Description Examples
🇨🇦 Canada NAICS (SCIAN) North American Industry Classification System 221122 - Electric Power Distribution
🇺🇸 USA SOC Standard Occupational Classification 47-2061.00 - Construction Laborers
🇪🇺 Europe NACE Statistical Classification of Economic Activities 35.13 - Distribution of Electricity
✅ Data Sources - AgenticX5
  • 793,000+ CNESST incidents (Québec) - NAICS Classification
  • 220,000+ OSHA incidents (USA) - SOC Classification
  • 150,000+ EU-OSHA incidents (Europe) - NACE Classification
  • Automatic mapping between the 3 taxonomies via harmonization tables
📊

3. HSE Data Types - Detailed Inventory

Common and industry-specific data types for OHS

▼

🚨 3.1 Incident & Accident Data

Key Attributes

  • Unique ID: CNESST-2024-001234
  • Date/Time: ISO 8601 format (2024-03-15T14:30:00Z)
  • Location: GPS coordinates + facility address
  • Incident Type: Controlled taxonomy (fall, entrapment, chemical exposure, etc.)
  • Severity: 1-5 scale or lost time days
  • Individuals Involved: Number + roles (anonymized)
  • Injuries: Nature, location, diagnosis (ICD-10)
  • Root Causes: Immediate + systemic (Bowtie Analysis)
  • Contributing Factors: Environmental, organizational, behavioral
  • Corrective Actions: Description + responsible + deadline
  • Follow-up Status: Open, In Progress, Closed
  • Regulatory References: Violated regulations
Example JSON: 
{
  "id": "CNESST-2024-001234",
  "dc:identifier": "CNESST-2024-001234",
  "dc:title": "Fall from height - Construction Site A",
  "dc:date": "2024-03-15T14:30:00Z",
  "dc:creator": "CNESST Inspector - Jean Tremblay",
  "dc:type": "Incident Report",
  "dc:subject": ["Fall", "Construction", "Safety"],
  "dc:coverage.spatial": "Montreal, QC",
  "incidentType": "FALL_HEIGHT",
  "severity": 4,
  "injuries": [{"type": "FRACTURE", "location": "left_arm", "icd10": "S42.0"}],
  "rootCauses": ["MISSING_GUARDRAILS", "INADEQUATE_SUPERVISION"],
  "correctiveActions": [{
    "description": "Install compliant guardrails on all scaffolds",
    "responsible": "Site Manager",
    "deadline": "2024-03-30"
  }]
}

🔍 3.2 Inspection & Audit Data

Key Attributes

  • Inspection ID: Unique tracking number
  • Date: Inspection date
  • Type: Planned, reactive, regulatory
  • Scope: Equipment, process, site
  • Inspector(s): Name + certification
  • Checklist Used: Reference to standard template
  • Findings: Compliant / Non-compliant items
  • Observations: Detailed notes
  • Risk Rating: For each finding
  • Recommendations: Prioritized actions
  • Photographic Evidence: References to images
  • Follow-up Date: Next inspection date

⚠️ 3.3 Risk Assessment Data

📋

Risk Analysis

  • Analysis ID
  • Workstation / Activity
  • Identified Hazards (taxonomy)
  • Probability (1-5 scale)
  • Severity (1-5 scale)
  • Risk Level (Probability × Severity)
  • Existing Controls
  • Proposed Controls
  • Residual Risk
5×5 Matrix HAZOP
👁️

Behavioral Observations

  • Observation ID
  • Date/Time
  • Zone Observed
  • Safe Behaviors (count)
  • At-Risk Behaviors (count)
  • Behavior Details
  • Feedback Provided
  • Follow-up Actions
BBS

🛠️ 3.4 Equipment & Hazardous Materials

Critical Equipment Inventory

  • Equipment ID: Unique identifier
  • Name/Description
  • Precise Location
  • Serial Number
  • Manufacturer
  • Commissioning Date
  • Equipment Type (taxonomy)
  • Criticality Level (1-5)
  • Inspection Frequency
  • Inspection History
  • Related Incidents
  • Current Status
  • Certifications

Hazardous Materials Inventory

  • Product ID: CAS number / IUPAC name
  • Quantity & Unit
  • Location
  • Hazard Classification (GHS)
  • SDS (Safety Data Sheet)
  • Storage Conditions
  • Expiration Date
  • Emergency Procedures
GHS SIMDUT

🎓 3.5 Training & Certifications

  • Training ID
  • Training Title
  • Type (induction, refresher, specialized)
  • Duration
  • Trainer(s)
  • Participants (anonymized list)
  • Completion Date
  • Assessment Results
  • Certificate Issued
  • Expiration Date
  • Regulatory Requirements
✅ Best Practices
  • Use controlled taxonomies for all categorical fields
  • Implement unique identifiers with consistent format
  • Document all relationships between datasets
  • Maintain complete audit trails for all modifications
  • Ensure privacy compliance for personal data
🔄

4. Data Preparation Process - 6 Phases

From collection to production deployment

▼
📥

Phase 1: Collection

  • Source identification
  • API/ETL setup
  • Initial ingestion
  • Raw storage (Bronze)
Week 1-2
🧹

Phase 2: Cleaning

  • Duplicates removal
  • Missing values handling
  • Outliers detection
  • Format normalization
Week 2-3
🏗️

Phase 3: Structuring

  • Schema definition
  • Taxonomy mapping
  • Relationship modeling
  • Partitioning strategy
Week 3-4
✅

Phase 4: Validation

  • Quality tests
  • Business rules verification
  • Statistical validation
  • Anomaly detection
Week 4-5
📝

Phase 5: Documentation

  • Dublin Core metadata
  • Data dictionary
  • Lineage tracking
  • Version control
Week 5-6
💾

Phase 6: Storage

  • Gold layer deployment
  • Feature store setup
  • Backup strategy
  • Access control
Week 6+

📊 Modern Data Stack Architecture

🏛️ Layered Architecture

Layer Description Technologies Format
🥉 Bronze Raw data, as-is from sources S3, Azure Blob, GCS JSON, CSV, Parquet
🥈 Silver Cleaned and validated data Delta Lake, Iceberg Parquet (partitioned)
🥇 Gold Curated and business-ready Snowflake, BigQuery, Databricks Tables optimized for analytics
⭐ Feature Store ML-ready features Feast, Tecton, SageMaker Optimized for ML serving
💡 Key Principle
Never modify Bronze layer data - maintain complete traceability from source to final features. All transformations must be reproducible and versioned.
🛠️

5. Technology Stack & Tools

Modern tools for data preparation and quality assurance

▼

🔧 Essential Tools by Category

🔄

Orchestration & Pipelines

  • Apache Airflow: Workflow automation and scheduling
  • Prefect: Modern dataflow orchestration
  • Dagster: Data pipeline development framework
  • dbt (data build tool): SQL-based transformation workflows
Recommended
✅

Data Quality & Validation

  • Great Expectations: Automated testing framework
  • Pandera: Statistical data validation for pandas
  • Deequ: Data quality library for Spark (Amazon)
  • ydata-profiling: Automated EDA reports
Priority
📊

Data Catalog & Lineage

  • DataHub (LinkedIn): Metadata platform
  • Amundsen (Lyft): Data discovery & metadata engine
  • OpenLineage: Open standard for data lineage
  • Atlas (Apache): Metadata framework
Governance
🤖

MLOps & Model Registry

  • MLflow: Model lifecycle management
  • Weights & Biases: Experiment tracking
  • DVC (Data Version Control): Git for data/models
  • Feast: Feature store
ML Pipeline

💻 Example Code: Data Quality Pipeline

Great Expectations - Quality Suite

import great_expectations as gx
from great_expectations.dataset import PandasDataset

# Initialize context
context = gx.get_context()

# Load HSE incident data
df = pd.read_parquet('silver/incidents_2024.parquet')

# Create expectations
expectations = {
    'expect_column_to_exist': ['incident_id', 'date', 'severity'],
    'expect_column_values_to_be_unique': ['incident_id'],
    'expect_column_values_to_not_be_null': ['date', 'incident_type'],
    'expect_column_values_to_be_between': {
        'severity': {'min_value': 1, 'max_value': 5}
    },
    'expect_column_values_to_be_in_set': {
        'incident_type': ['FALL', 'CHEMICAL', 'ENTRAPMENT', 'FIRE']
    }
}

# Run validation
results = context.run_validation_operator(
    'action_list_operator',
    assets_to_validate=[df],
    expectation_suite_name='hse_incidents_suite'
)

# Generate report
if results['success']:
    print("✅ All quality checks passed!")
else:
    print("❌ Quality issues detected:")
    for result in results['run_results']:
        print(f"  - {result['expectation_config']['expectation_type']}: FAILED")

dbt - Data Transformation

-- models/silver/incidents_cleaned.sql
{{ config(
    materialized='incremental',
    unique_key='incident_id',
    partition_by={'field': 'date', 'data_type': 'date'}
) }}

WITH source_data AS (
    SELECT * FROM {{ ref('bronze_incidents') }}
    {% if is_incremental() %}
    WHERE date >= (SELECT MAX(date) FROM {{ this }})
    {% endif %}
),

cleaned AS (
    SELECT
        incident_id,
        PARSE_TIMESTAMP('%Y-%m-%dT%H:%M:%S', date) AS timestamp,
        UPPER(TRIM(incident_type)) AS incident_type,
        CAST(severity AS INT64) AS severity,
        NULLIF(TRIM(location), '') AS location,
        -- Dublin Core metadata
        incident_id AS dc_identifier,
        CONCAT('Incident Report - ', incident_type) AS dc_title,
        timestamp AS dc_date,
        'CNESST' AS dc_publisher,
        'Incident Report' AS dc_type
    FROM source_data
    WHERE severity BETWEEN 1 AND 5
        AND incident_type IS NOT NULL
)

SELECT * FROM cleaned
🎯 Recommended Stack for AgenticX5
  • Orchestration: Apache Airflow + dbt
  • Quality: Great Expectations + ydata-profiling
  • Storage: Snowflake (Gold) + S3 (Bronze/Silver)
  • Lineage: OpenLineage + DataHub
  • MLOps: MLflow + Feast
  • Monitoring: Prometheus + Grafana
🔐

6. Governance & Compliance

Privacy, security, and regulatory compliance

▼

📜 Regulatory Framework

🇨🇦

Québec - Law 25

Mandatory

Private Sector Privacy Law

  • Explicit consent for data collection
  • Impact assessments for sensitive data
  • Breach notification (72h)
  • Right to access, rectification, deletion
  • Privacy by design
🇪🇺

Europe - GDPR

International

General Data Protection Regulation

  • Lawful basis for processing
  • DPIA (Data Protection Impact Assessment)
  • Right to portability & erasure
  • Data minimization principle
  • DPO (Data Protection Officer)
🏢

ISO 45001

OHS Standard

Occupational Health & Safety Management

  • Risk assessment documentation
  • Incident investigation procedures
  • Performance monitoring metrics
  • Worker participation & consultation
  • Continuous improvement
⚖️

Bill C-25

AI Governance

Artificial Intelligence and Data Act (Canada)

  • High-impact AI system assessment
  • Algorithmic transparency
  • Bias mitigation requirements
  • Human oversight mechanisms
  • Accountability framework

🔒 Privacy & Security Best Practices

Data Minimization & Anonymization

  • Pseudonymization: Replace identifiers with pseudonyms
  • Aggregation: Use statistical summaries instead of individual records
  • Differential Privacy: Add statistical noise to protect individuals
  • K-anonymity: Ensure each record is indistinguishable from k-1 others
  • Data retention limits: Automatic deletion after regulatory period
# Example: K-anonymity implementation
from anonymizedf.anonymizedf import anonymize

anon = anonymize(df)
anon.categorical_columns(['location', 'job_role'])
anon.numeric_columns(['age'], method='binning', bins=5)
anon_df = anon.to_dataframe()

# Verify k-anonymity
k_value = anon_df.groupby(['location', 'job_role', 'age_bin']).size().min()
print(f"K-anonymity value: {k_value}")  # Should be >= 5

Access Control & Auditing

  • RBAC (Role-Based Access Control): Permissions by role
  • ABAC (Attribute-Based Access Control): Dynamic access based on attributes
  • Audit logs: Complete tracking of all data access and modifications
  • Encryption: At-rest (AES-256) and in-transit (TLS 1.3)
  • MFA (Multi-Factor Authentication): For all data access

📊 Quality Metrics & KPIs

Metric Target Measurement Frequency
Completeness ≥ 95% (Non-null values / Total values) × 100 Daily
Accuracy ≥ 98% (Valid records / Total records) × 100 Weekly
Consistency ≥ 97% (Consistent records / Total records) × 100 Weekly
Timeliness ≤ 24h Time between event and ingestion Real-time
Uniqueness 100% (Unique IDs / Total records) × 100 Daily
Metadata Coverage ≥ 95% (Fields with metadata / Total fields) × 100 Monthly
⚠️ Critical Compliance Checkpoints
  • ✅ Privacy Impact Assessment (PIA) completed before data collection
  • ✅ Data Processing Agreement (DPA) signed with all vendors
  • ✅ Consent management system implemented
  • ✅ Data breach response plan documented and tested
  • ✅ Regular security audits (quarterly minimum)
  • ✅ Staff privacy training (annual)
✅

7. Validation Checklist

Pre-deployment quality assurance checklist

▼
📋

Data Quality

  • ☐ All primary keys are unique and non-null
  • ☐ No duplicate records in the dataset
  • ☐ Missing values below 5% threshold
  • ☐ All dates in ISO 8601 format
  • ☐ Categorical values match controlled vocabulary
  • ☐ Numeric values within expected ranges
  • ☐ Text fields properly encoded (UTF-8)
  • ☐ Outliers identified and documented
🏷️

Metadata

  • ☐ All 15 Dublin Core elements populated
  • ☐ Data dictionary created and published
  • ☐ Schema versioned and documented
  • ☐ Lineage tracked from source to gold
  • ☐ Taxonomy mappings documented
  • ☐ Data provenance recorded
  • ☐ Update frequency specified
  • ☐ Data owner identified
🔐

Privacy & Security

  • ☐ PII data anonymized or pseudonymized
  • ☐ K-anonymity >= 5 for sensitive data
  • ☐ Access controls configured
  • ☐ Encryption enabled (at-rest & in-transit)
  • ☐ Audit logging activated
  • ☐ Data retention policy applied
  • ☐ Privacy impact assessment approved
  • ☐ Consent documented
🎯

Technical Validation

  • ☐ Great Expectations test suite passing
  • ☐ dbt tests passing (100%)
  • ☐ Data profiling report generated
  • ☐ Performance benchmarks met
  • ☐ Partitioning strategy implemented
  • ☐ Indexes created on key columns
  • ☐ Backup and recovery tested
  • ☐ Monitoring dashboards deployed
🎉 Ready for Production
Once all checklist items are completed, your HSE data is ready for deployment in the AgenticX5 AI ecosystem. Don't forget to schedule regular audits and updates!
📚

8. References & Resources

Standards, documentation, and further reading

▼

📖 International Standards

Metadata & Interoperability

OHS & Quality Standards

  • ISO 45001: Occupational Health & Safety Management Systems
  • ISO 9001: Quality Management Systems
  • ANSI Z10: Occupational Health & Safety Management Systems
  • OHSAS 18001: (superseded by ISO 45001)

🛠️ Tools & Platforms

Data Quality

Orchestration

Governance

📜 Regulatory Resources

Canada

International

📚 Further Reading

  • Kleppmann, M. (2017). Designing Data-Intensive Applications. O'Reilly Media.
  • Reis, J., & Housley, M. (2022). Fundamentals of Data Engineering. O'Reilly Media.
  • Provost, F., & Fawcett, T. (2013). Data Science for Business. O'Reilly Media.
  • Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O'Reilly Media.
💡 Stay Updated
This guide will be updated regularly to reflect:
  • New regulatory requirements
  • Emerging tools and technologies
  • Feedback from AgenticX5 implementations
  • Industry best practices evolution