HSE Data Preparation Guide - AgenticX5

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1. Introduction & Objectives

Purpose and scope of this HSE data preparation guide

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📌 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

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📋 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

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🚨 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

🔍 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

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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

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4. Data Preparation Process - 6 Phases

From collection to production deployment

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📥

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

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🔧 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

🎯 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

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📜 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

📊 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)