HSE ANALYTICS
HSE Analytics
"Transforming indicators into prevention and decision-making."
"My career as an Occupational Safety Technician and Manager has allowed me to experience firsthand the challenges of risk management in complex environments. Today, I combine this practical experience with Data Science to create solutions that go beyond simple visualization: I translate compliance and incident metrics into preventive strategies. In HSE Analytics, the focus is on intelligence applied to preserving life and ensuring operational continuity, guaranteeing that each dashboard is an active tool in the Safety and Health culture (NR-37 and related standards)."
Case NR-37
Context
In the oil and gas sector, operational safety is the fundamental pillar. This project simulates the management of HSE (Health, Safety, and Environment) indicators in an offshore unit, where constant incident monitoring and compliance with regulatory standards, such as NR-37, are vital for business continuity and the preservation of life.
Project Objective
Develop a Business Intelligence tool that centralizes critical safety metrics, allowing managers to identify risk areas, the most frequent types of injuries, and temporal trends in accidents to predictively target investments in training and PPE.
Key Analytics
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Severity Management: Monitoring of 97 total incidents, focusing on the 22 serious cases and the direct impact of 402 days of lost time.
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Sectoral Mapping: Identification of Drilling (27 occurrences) and Machinery (24 occurrences) as the most critical sectors.
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Agent Diagnosis: Detailed analysis of causative factors, revealing that Cuts (34.02%) and Falls (26.8%) dominate the risk scenario. Shift and Gender: Data cross-referencing indicating the Night shift (55 incidents) as the period of greatest operational vulnerability.
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Seasonality (2024-2026): Historical comparison showing a warning of increased incidents at the beginning of 2026 compared to previous years.
Tool used
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Power BI: For data modeling and creating DAX measures.
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Excel: Used for database processing (ETL).
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Figma / Graphic Design: Layout planning (background) focused on UX/UI, using alert colors and clear typography to facilitate reading in high-pressure environments.
Project highlights
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Focus on Compliance: Direct alignment with the requirements of NR-37 for oil platforms.
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Visual Storytelling: Use of "gauge" type indicators and highlight cards for quick reading of key KPIs.
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Trend Analysis: Inclusion of projections for the first months of 2026, allowing for proactive management before the end of the semester.
Conclusion
The dashboard reveals that, although the volume of training (61) is significant, the concentration of incidents in the Drilling sector and on the night shift requires a strategic review. The tool proves that the intelligent use of data allows transforming raw records into decisions that save lives and reduce operational costs related to absences.
Recommended actions
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Focus on Drilling: Implement specific behavioral audits and reinforce Daily Safety Dialogues (DDS) focused on this sector.
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Night Shift Management: Intensify supervision and lighting during the night shift, where the volume of deviations is 31% higher.
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Injury Prevention: Technical review of gloves and hand tools to mitigate the high rate of cuts (34%), in addition to awareness workshops on fall risks.
Data source
Simulated data based on real metrics from offshore operations and occupational safety records from the energy sector.
Risks and PPE
Context
This project aims to analyze workplace accidents in an offshore production company, using simulated data with realistic characteristics, in order to identify patterns, risks, and opportunities for prevention.
Project Objective
The objective of this project was to develop an operational intelligence tool for critical workplace safety monitoring in offshore production units. The focus is on identifying the correlation between the use of PPE (Personal Protective Equipment) and the severity of accidents, as well as mapping periods of greater vulnerability (seasonality) and sectors with the highest incidence of absences, allowing for predictive rather than just reactive risk management.
Key Analytics
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Severity and Status: Real-time monitoring of accident severity levels, with a "Current Situation" indicator pointing to a critical state (65.4% severity).
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PPE Compliance: Direct analysis of the impact of individual safety, revealing that 37.52% of accidents occurred without the proper use of PPE.
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Sectoral and Shift Distribution: Mapping of occurrences by sector, highlighting Production (1803) as a critical area, and segmentation between Day and Night shifts.
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Incident Typology: Detailed identification of the main causes, such as Falls (1468 occurrences) and Impacts.
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Business Impact (LTI): Measurement of the Total Days Off Time, correlating them with the type of accident (e.g., Explosion generating 499 days).
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Temporal Trend: Monthly evolution graph showing a significant increase in occurrences in the last quarter of the year (reaching 1660 in December).
Tool used
Developed in Microsoft Excel.
Project highlights
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Expert Approach (HSE): The project focuses not only on numbers, but on fundamental occupational safety indicators, such as the relationship between PPE and severity.
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Alert Visualization: Use of a "Gauge" (speedometer) graph to indicate the level of risk of the operation, facilitating immediate reading for decision-making.
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Absence Analysis: It stands out by quantifying the loss of productivity through days of absence, combining a safety perspective with that of resource management.
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Applied UX Design: Dark Mode interface with contrasting colors (traffic light) to highlight danger and safety areas intuitively.
Conclusion
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Critical Vulnerability: Identification of a high incidence of accidents due to inadequate use of PPE.
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Temporal Analysis: Discovery of seasonal variations that impact preventive planning.
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Risk Sectors: Mapping of specific areas with a higher frequency of serious incidents.
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Operational Impact: Productivity Metrics.
Recommended actions
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Focus on the most critical sectors: Implement training and safety reinforcement in sectors that have recorded the highest number of accidents, with constant monitoring of results.
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Awareness campaigns on the use of PPE: Intensify communication and monitoring of the correct use of PPE, aiming to reduce serious accidents associated with its non-use.
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Continuous analysis of seasonality: Establish monthly monitoring of accidents to anticipate periods of higher risk and plan specific preventive actions.
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Improvement of work processes: Review and adapt operational procedures in activities associated with the most frequent and serious types of accidents, to reduce risks.
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Investment in occupational health: Develop programs to monitor employees on leave, aiming for a safe return and reduction of time off work.
These actions, based on data analysis, can contribute to the reduction of accidents, improvement of safety in the offshore environment, and optimization of company resources.
Data source
Fictitious (simulated) database, built for study and portfolio purposes, based on real patterns of workplace accidents in the offshore sector.
Project phases
A. Operational Database: Initial dataset containing offshore incident records. The processing phase focused on correcting record numbering and standardizing risk categories, eliminating noise that could compromise the accuracy of safety indicators.
B. Clean Data Processing: In this step, I applied data processing via Power Query to ensure the accuracy of the risk analysis. The process included removing duplicates, standardizing accident categories, and rigorously formatting date and severity fields, structuring a solid foundation for generating safety KPIs.
C. Analysis (Pivot Tables): I used Pivot Tables to model the safety data and extract critical operational metrics. In this step, the data was aggregated to calculate the accident rate by sector, the severity of occurrences (Days Off Time), and the correlation between the use of PPE and the incident rate. This modeling was essential to structure the indicators that feed the final dashboard.
D. Dashboard (Final Visualization): The dashboard was developed as a decision support tool, prioritizing visual clarity regarding critical risk indicators. I used bar charts for accident frequency, severity indicators, and segmenters by unit/sector. This interface allows management to instantly identify hazard areas and direct training and resources to prevent new occurrences.
This dashboard provides a 360º view of operational safety, transforming raw data into strategic decisions for reducing offshore accidents.