Big Data offers enormous opportunities for the field of industrial safety, allowing proactive and preventive “data-driven” improvements. Understand its main features and the benefits it brings to the area of Occupational Health and Safety.
In the field of industrial safety, accurate information is vital to prevent risks and protect workers. Information is power, and it allows us to make “data driven” decisions to adjust processes, improve procedures and, ultimately, achieve a safer workplace.
Thanks to digitization, many processes that were previously manual, such as collecting data and information for documentation, can now be automated. In addition, the various devices, software and sensors available make it possible to collect an exponential amount of data. This is where the concept of Big Data comes into play.
What is Big Data and how it obtains data.
Big Data refers to the massive set of data that is constantly being generated through various sources, such as sensors, mobile devices, social networks, surveillance systems and more. This data is so large and complex that traditional data processing methods are not sufficient to analyze it and extract meaningful information.
Industrial safety data can come from multiple sources, such as:
- Sensors and monitoring systems: Devices in industrial environments collect data on temperature, pressure, humidity, chemical levels, among others, providing a detailed view of working conditions.
- Incident and accident records: Reports of past incidents and accidents are a valuable source of information to identify patterns and prevent future problems.
- Safety management systems: Platforms and software used to manage safety in industrial facilities record data related to policies, procedures, inspections and audits.
- External data: In addition to internal company data, external sources such as government reports, industry statistics and specialized databases provide valuable information for assessing risks and improving safety practices.
Main characteristics of the Big Data.
According to an article by Oracle Argentina, Big Data is characterized by the famous “3Vs”: Volume, Velocity and Variety.
- Volume: Data is generated in large quantities and at an accelerated pace. Managing and processing this volume of data requires scalable storage and analytics solutions, such as cloud storage systems and distributed computing technologies.
- Velocity: Velocity is another essential characteristic of Big Data. Data in the industrial safety domain is generated and must be analyzed in real time or near real time to obtain relevant information and take timely action. For example, digital PPE can detect changes in worker behavior patterns and alert about dangerous situations in real time.
- Variety: Data can be structured (such as databases) or unstructured (such as text, images or videos). In addition, they come from different sources and may have different formats, which requires flexible analysis and processing tools. Handling this variety of data requires flexible processing and analysis techniques, such as the use of natural language processing (NLP) algorithms to extract information from written reports or the use of computer vision algorithms to analyze images or videos.
Main uses of Big Data.
The application of Big Data in industrial safety offers enormous opportunities. For desktop workers, the advancement of technology has brought many improvements, especially in terms of efficiency. They have a huge range of digital tools, which simplifies the operational and allows them to focus on the strategic.
This could happen, and is happening, in a similar way with industrial workers. In the last decade, above all, we have begun to analyze how technology applied to industrial safety could provide tools for them to be better protected, and for safety leaders to have relevant, automated information and data, allowing them to review processes and take proactive measures.
Following that, Big Data offers multiple benefits:
- Predictive analytics: Using advanced algorithms, patterns and trends can be identified that indicate imminent risk situations, allowing preventive measures to be taken before accidents occur. For example, by analyzing data from past incidents and accidents, common factors can be identified and predictive models can be developed to anticipate similar situations. This makes it possible to implement appropriate preventive measures and reduce the likelihood of accidents.
- Real-time monitoring: Real-time analysis of collected data can alert on anomalies or deviations from normal parameters, allowing a rapid response to dangerous situations. For example, through real-time monitoring of temperature sensors in a chemical production plant, it is possible to detect a sudden rise in temperature that could indicate a risk of explosion.
- Process optimization: Data analysis can reveal areas for improvement in industrial processes, leading to greater efficiency and safety at work.
- Training and awareness: Data can be used to develop customized training and awareness programs targeting specific hazards identified through data analysis.
The use of Big Data in industrial safety makes it possible to take advantage of the vast pool of data generated to improve risk prevention, informed decision making and worker protection.
With the proper application of analytics techniques, it is possible to identify patterns, predict hazardous situations and optimize processes to ensure a safe working environment. Let’s take advantage of the information age to make industrial safety a constant priority.