Have you ever thought about how companies ensure the safety and well-being of their employees and the environment they operate in? That’s where environmental, health, and safety (EHS) management comes in. In today’s world, with the increasing focus on growth and profitability, companies need to prioritize EHS management to minimize potential hazards and risks.
Effective EHS management not only ensures the safety of the workforce and the surrounding environment but also positively impacts a company’s bottom line. In fact, work-related injuries and illnesses result in a 3.9% loss of all work years.
Now, you may be wondering how companies manage EHS effectively. Enter data analytics! Data analytics is a powerful tool that collects, analyses, and interprets data to gain insights into a company’s EHS performance. By using data analytics, companies can identify potential hazards and risks, predict incidents, and implement precautionary measures to alleviate them.
Advanced Data Analytics in EHS Management
As technology continues to evolve, data analytics has become more sophisticated, leading to the development of “advanced data analytics”. Advanced data analytics is the use of complex algorithms and machine learning techniques to analyze vast amounts of data and recognize patterns, trends, and anomalies that may not be apparent using traditional data analytics methods.
A. Types of advanced data analytics used in EHS management
There are several types of advanced data analytics used in EHS management, including:
- Predictive analytics: The core of predictive analytics lies in machine learning and statistical models to forecast future outcomes. It analyzes historical data and identify configurations that can help predict future events, such as safety incidents.
- Prescriptive analytics: Prescriptive analytics exceeds predictive analytics. Just like a doctor, it can give “prescriptions” on ways to prevent or lessen safety incidents.
- Natural Language Processing (NLP): NLP uses machine learning algorithms to analyze unstructured data, such as incident reports, to identify underlying causes and trends.
- Sentiment analysis: Sentiment analysis is the use of natural language processing to analyze text data and identify positive or negative sentiments. In EHS management, sentiment analysis can be used to gauge employee perception of safety programs and recognize areas for improvement.
B. Benefits of advanced data analytics in EHS management
Advanced data analytics offers several benefits in EHS management, including:
- Improved safety: Advanced data analytics can identify potential hazards and risks before they occur, allowing companies to take proactive measures to prevent safety incidents.
- Increased efficiency: By using advanced data analytics, companies can identify inefficiencies in their EHS programs and take corrective actions to improve efficiency.
- Cost savings: Advanced data analytics can help companies reduce the costs associated with safety incidents, such as workers’ compensation and insurance premiums.
- Enhanced decision-making: Advanced data analytics provides decision-makers with actionable insights, enabling them to make informed decisions on EHS programs and policies.
C. Examples of implementation of advanced data analytics in EHS management
Shell uses predictive data analytics to anticipate workplace injuries on their oil rigs globally. The company’s computers analyze factors like employee engagement in safety procedures to predict the likelihood and location of potential injuries.
Goldcorp, a mining company based in Canada, used prescriptive analytics to identify the root causes of safety incidents and provide recommendations on how to prevent them. Goldcorp utilized insights from advanced data analytics to enhance management training, develop new policies, and concentrate supervisor support on employees with greater risk profiles.
The oil and gas industry uses machine learning / natural language processing to analyze accidents and reduce risk in the field. They examine incident reports and identify underlying causes of safety incidents.
Challenges of Implementing Advanced Data Analytics in EHS Management
While advanced data analytics offers many benefits for EHS management, there are several challenges that organizations may face when implementing this technology:
- Data quality: Advanced data analytics relies on high-quality data to generate accurate insights. However, many organizations struggle with data quality issues, such as incomplete or inaccurate data, which can undermine the effectiveness of advanced data analytics.
- Data integration: Organizations often collect EHS data from various sources, such as sensors, incident reports, and compliance audits. Integrating this data can be puzzling, as it may come in different formats and require data cleansing and normalization.
- Skillset: Advanced data analytics requires specialized skills and knowledge, such as statistical analysis, machine learning, and data visualization. Many organizations may not have employees with these skills, making it difficult to effectively implement advanced data analytics.
- Cost: Implementing advanced data analytics can be expensive, requiring significant investment in hardware, software, and training.
Ways to overcome these challenges
While implementing advanced data analytics in EHS management may present challenges, there are ways to overcome these issues:
- Improve data quality: Organizations can improve data quality by implementing data governance policies, conducting regular data quality assessments, and investing in data cleansing and normalization tools.
- Implement data integration tools: Organizations can use data integration tools, such as data warehouses and data lakes, to integrate EHS data from various sources and make it accessible for advanced data analytics.
- Hire or train employees with the necessary skills: Organizations can hire employees with specialized skills in advanced data analytics or provide training to existing employees to improve their skills.
- Prioritize investment: Organizations can prioritize investment in advanced data analytics by conducting a cost-benefit analysis, identifying areas of high risk or potential cost savings, and implementing advanced data analytics in a phased approach.