Canadian Utilities Provider
Health & Safety Analytics Pilot to Prevent Workplace Injuries
Our client provides natural gas to both residential and commercial customers within their province of operations. Federally regulated, they have extremely high volumes of data, and legacy technology platforms were not providing the ability to modernize their environment and move towards data maturity, advanced analytics, or AI/ML. Hours were spent across departments gathering, curating, and preparing reports.
The Challenge
The clients’ worksite injuries were recorded in Excel spreadsheets, paper-based Job Safety Assessments, and manually uploaded to an internally developed system. The data was not reliable, real-time, or accurate, and therefore trends were not easy to extract. There was no capability to predict or prevent workplace incidents or understand the impacts of the location of work, employee demographics, amount of shifts worked prior to the incident, etc.
While addressing their HSE data challenges, the client wanted to test and prove a more modern, reliable, and scalable cloud data platform. They had a legacy data environment and very few cloud skills, and turned to Data Elephant to help further define the HSE use case, understand their current data challenges, define their future state vision, and execute a quick-win data platform pilot in an agile fashion.
The Solution
The team worked across 6 weeks, including a 2-week analysis and mobilization phase. We partnered with 2 key stakeholders within operations and HSE to understand the current incident occurrences and related data and develop a vision for predictive analytics. We ensured the solution could support structured, flat files, PDFs and computer vision technology to extract data points.
Once mobilization was complete, the team built a production-ready custom data lake leveraging AWS services and developed an advanced analytics model to predict future incidents as a pilot to prove out the platform and gain buy-in from executives to modernize their cloud data environment.
While the pilot focused Time & Attendance and Weather data to analyze the impact of shifts, breaks, and physical conditions, the organization plans to scale the project to include IoT sensor data to monitor body temperature and heart rate and move towards enabling real-time alerting.
Why AWS?
The organization had some cloud presence and experience with another provider’s platform, however, was compelled by the innovation and relevant customer case studies brought to the table by AWS.
AWS Services Used
S3 Data Lake, RDS (SQL Server), AWS Glue Studio, AWS Glue Catalog, Redshift, SageMaker.
Why Data Elephant?
Data Elephant was selected due to our AWS skills, our collaborative approach to building data solutions, and our ability to set up the client for future sustainment and scale of the solution independently.
