Angelo Cianfrocco

If you ask a group of EHS professionals whether they are getting quality, actionable data from their observation programs and BBS initiatives, a significant number will say no. But why?

The Total Recordable Incident Rate (TRIR) has become a universal metric. For the few that are not familiar with the TRIR calculation, let’s do a brief review.

What do Product Managers and Environmental, Health and Safety (EHS) professionals have in common?

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.

The data “will speak for itself.” If you have spent any time in the data analytics world you have heard this phrase thrown around. But is it true?

What is the difference between a target and a measure? Moreover, why is it important to distinguish between the two? At the beginning of every calendar year our teams get together and discuss goals. Our goal meetings are often conducted with the best intentions; however, we often miss our mark by confounding targets and measures.

The influx of technology and EHS information management systems have made it more convenient than ever before for companies to collect and store EHS related data. However, it is one thing to collect data, but it is quite another to use data to make positive change.