Author Archives: Angelo Cianfrocco

Defining and Overcoming Cultural Barriers to EHS Data Collection

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Lowering the cultural barriers to EHS data collection is critical if you want your organizations to make data driven decisions to improve workplace safety.
Lowering the cultural barriers to EHS data collection is critical if you want your organizations to make data driven decisions to improve workplace safety.

Culture is a word that is commonly used at most companies and building a safety culture is often a focus of EHS professionals. However, a specific focus on lowering the cultural barriers to data collection can have a huge impact on the greater safety culture and help embed safety in everyday operations.

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. Lowering the cultural barriers to data entry is critical if you want to collect quality, honest and timely data to make better data driven decisions. So, how can we lower the cultural … Read more...

Do You Believe that Data Accuracy Is ‘the Golden Ticket’ To Risk Reduction?

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Many companies implement the newest software and technology with the idea that once the new tech is up and running, there will be gold rush of data to influence the EHS programs, ie., the “golden ticket.” But is this really the case?

In a recent poll we conducted of environmental, health and safety (EHS) professionals, 39% of respondents indicated that data accuracy was the biggest data quality huddle that they faced.

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.

As EHS technologies have become more prevalent in the EHS landscape, so has the “golden ticket fallacy.” Many companies implement the … Read more...

Data Quality – It’s a Dirty Job, but Someone’s Got To Do It

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Data quality is the foundation for EHS insights.
Forty-seven percent of newly-created data records have at least one critical (e.g., work-impacting) error.

Data quality is the foundation that EHS insights are built upon. When EHS data suffers from poor data quality, insights are eroded and the information that was once on a stable foundation is no longer suitable for analytics.

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.

Only 3% of Data Meets Quality Standards

A 2017 Harvard Business Review article, “Only 3% of Companies’ Data Meets Basic Quality Standards,” outlined the data quality crisis best, stating that, “Forty-seven percent of newly-created data records have at least one critical (e.g., work-impacting) error.”… Read more...

Data Does not Speak for Itself: You Have to Speak for It

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Letting the data speak for itself can result in unrecognizable insights for many in your organization. Data needs to be interpreted to make sense.

The data will speak for itself. This is a phrase that is commonly thrown around in data analytics circles.

Often the people who say the data will speak for itself are the ones who are closest to the data, and for them, this might be true. EHS analysts, business analysts and other disciplines that focus on data collection, cleaning, preparation and analysis are closest to the data.

However, for employees of departments whose focus is not data analysis, the data can look like it is speaking Italian instead of English. This means that “letting the data speak for itself” can result in unrecognizable insights for many in your organization.

The practice of environmental, health and safety (EHS) is experiencing growing pains when it comes to analysis … Read more...

Forest for the Trees: Stuck in the Briars of Measuring Safety

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measurement versus targets
Does your organization confuse measurements with targets? If so, it means you can’t see the forest for the trees.

Can you see the forest for the trees when it comes to data targets versus measuring safety? Or are you stuck in the briars, setting targets that ultimately undermine your intentions?

The beginning of a new year offers a fresh start. As teams got together to vision set the year, we congregate for goal meetings. The goals that come out of these meetings shape our strategy for the year moving forward.

While our departmental or company goal meetings are often conducted with the best intentions, they often miss the mark by confusing targets with measures. As it is expressed in the Goodhart law: when a measure becomes a target, it ceases to be a good measure. In other words, when we use measures (quantitative indicators) as the targets we are trying … Read more...