Find the latest advances in technology for collecting and storing EHS-related data, the best practices, emerging trends, and how data can be used for improving processes, building culture, and eliminating workplace fatalities and injuries.
In this ISHN podcast episode, we talk to Duane Duhamel, whose company has been researching and analyzing workplace injuries and fatalities data every year, to discuss the findings in the latest report.
Data collected from safety devices not only highlight operational adjustments to help prevent any safety incidents, but they can also be used to increase the efficiency and overall performance of logistics operations.
What if we could crowdsource our EHS data? A prime example focuses on the safety successes of the U.S. airline industry - no commercial U.S. airline has experienced a fatal crash since 2009! Their safety strategy has been so effective that the healthcare industry, which currently experiences 250,000 unnecessary patient deaths a year in the U.S., is attempting to follow its protocols. So, what has helped the airline industry achieve such immense safety feats and how can EHS leaders in industries outside of the airline industry take a page out of their book?
In a recent poll we asked EHS professionals: what is your biggest barrier to collecting accurate EHS data? The clear winner was “cultural barriers to data entry” followed by “training of data collectors” which received 39% and 30% of the vote respectively. Culture is a word that is commonly used in the EHS industry and building a safety culture is often a focus of EHS professionals.
So, what is data accuracy? Data accuracy is one of the “six dimensions” of data quality and it can be defined as the “degree to which the data correctly describes the ‘real world’ objects being described.” This definition makes it easy to see how poor data accuracy could greatly impact your ability to use your data effectively.
Many companies starting their data analytics journey make the mistake of skipping the data cleaning process all together. None of us want to see how the sausage is made, we just want the bratwurst to magically appear. But as we have seen over, and over, insightful analytics cannot be achieved with poor data quality.