It has never been harder to be a safety professional than today. Safety professionals face immense pressure as they work to keep their organizations safe. Is new technology and more data the solution? Maybe. But these three challenges make it nearly impossible for safety professionals to succeed—and data could be at the root of these problems.
First, safety professionals are grappling with resource constraints, fatigue, and overwhelming responsibilities. One professional even likened their role to that of an emergency room doctor or warzone journalist—constantly operating in triage mode, prioritizing critical tasks while setting aside everything non-essential. They don’t have time for excessive, low-value data; they need access to the right data to make efficient, high-impact decisions.
Second, they are navigating an unprecedented challenge: the overwhelming volume of digital data now available to them. While their primary goal is to implement management systems that protect people, property, and the planet from workplace risks, achieving this has become increasingly difficult—not due to a lack of data, but because of an excess of it.
In the past, the challenge was the opposite. Safety professionals struggled with limited, hard-to-access, and time-consuming data collection methods, making it difficult to make informed decisions. Today, they have access to continuous streams of digital data but often lack the tools to consolidate, interpret, and prioritize it effectively. As a result, many safety initiatives are “data rich, information poor” (DRIP), where excessive data fails to translate into meaningful, actionable insights.
Third, the role of EHS is evolving beyond traditional cost-cutting and regulatory compliance. Safety leaders are aligning their efforts with broader business objectives, requiring more than just conventional safety data. They need reliable, value-added insights that help them demonstrate their impact in business terms.
This article explores the importance of different types of data and how organizations can refine their safety data collection processes and tools to generate meaningful insights—rather than simply adding more noise.
How we got here: The causes of safety information overload
The main reason why today’s safety professionals have so much data at their disposal is simple: Digital tools have made it easier than ever to collect data. Most organizations have viewed those tools as an opportunity to collect as much data as possible, whenever possible, and to retain it as long as possible.
Historically, organizations collected safety data in small volumes, relying on paper-based
records and spreadsheets. But as technology has made data collection easier (and as regulatory requirements have expanded), the quantity and variety of data have increased substantially.
For example, consider the following types of occupational safety data that organizations often collect today, but which rarely factored into occupational safety in the past:
- Incident reports: Data about injuries, near misses, and workplace accidents, including root cause analysis and corrective actions taken.
- Hazard identification data: Information gathered from assessments to identify risks like chemical exposure, noise levels, ergonomic hazards, and confined space risks.
- Training records: Documentation that tracks employee safety training, including completion dates, topics covered, and evaluations.
- Medical surveillance data: Employee health screenings such as respiratory function tests, hearing tests, and exposure monitoring to assess health risks.
- Environmental monitoring: Data on environmental factors like air quality, water quality, and hazardous material exposure levels.
- Safety inspections: Reports from routine inspections that identify hazards and areas for improvement.
Not all businesses collect all these types of data, of course. But thanks to the ease with which organizations can now automatically collect information like the types described above, the volume of occupational safety data has exploded at the typical organization over the past few decades.
The problems with extraneous safety data
At first glance, having more safety data may not seem like a bad thing. After all, shouldn’t you collect as much data as possible by default, then ignore whichever data turns out not to be useful?
The answer is “not necessarily.” When safety teams have too much data to contend with, several problems can arise.
Noise and distractions
The greatest problem caused by safety information overload, arguably, is that irrelevant data makes it challenging to identify trends that actually matter. Even with the help of analytics tools that can identify complex patterns, a safety team’s ability to detect relevant trends lessens when the data includes parameters that don’t correlate with safety priorities.
The result is that teams may end up with vast quantities of information at their disposal about safety risks and incidents yet struggle to make informed decisions about where and how to deploy controls or policies that will improve safety.
Lack of holistic data visibility
The more data you must work with, the harder it becomes to store it all in a central repository to analyze it effectively. By extension, businesses struggle to gain a holistic view of their organization’s safety status.
For example, you might have safety incident reports in one system, workplace environmental data in another, and Safety Data Sheets in a third. Gaining a holistic view of safety requires bringing all this information into one place—but that can be difficult to do when the information is “born” in disparate locations.
This is not to say that teams should ignore certain sources of data because they’re too challenging to integrate with other sources. If data is relevant, you absolutely should collect it and move it into a central repository.
Financial waste
Data is not free. Even if your safety team doesn’t have a fee to access safety data (which most don’t), there are costs associated with collecting, storing and analyzing the information. From this perspective, irrelevant safety data leads to bloated spending—an especially acute challenge in an era when many businesses face pressure to rein in their expenditures and improve the ROI of safety investments.
Scalability challenges
The more voluminous and diverse occupational safety data grows, the more challenging it becomes to manage the data in a scalable way. Businesses may struggle to establish clear policies over who should have access to which data, for example. These challenges intensify when the data is scattered across multiple systems or tools.
Regulatory risks
Along similar lines, ensuring that data meets regulatory compliance mandates becomes more challenging when businesses store more data and more diverse types of data. This is especially true given that regulations and compliance laws are constantly evolving, meaning that even if the way you managed your occupational safety data in the past left you in compliance, that may no longer be the case. And simply determining whether you’re violating a regulation or compliance law, let alone mitigating the issue, is much harder when your data is large in volume and scattered across many locations.
Cybersecurity threats
Occupational safety data often includes sensitive information, including data linked to individuals or internal business operations. The more of this data you store—and the more platforms or applications you have hosting that data—the more prone it becomes to cyberattacks that could expose the data to third parties.
Focusing on the data that matters
Avoiding challenges like those I just described hinges on developing safety data management strategies that home in on the data that matters—and that extricate the rest. Safety teams can do this by embracing the following strategies.
Integrate data
One of the most effective ways to streamline occupational safety data management is to adopt
integrated software solutions that centralize data from multiple sources. By using a
comprehensive platform that consolidates incident reports, training records, hazard assessments and environmental data, EHS professionals can access real-time insights in one location.
In turn, they can track and analyze key metrics holistically, gaining across-the-board insights into safety risks that impact their organizations.
Look for root causes, not correlations
Part of the reason why teams sometimes find themselves drowning in data is that they become fixated on identifying correlations. The correlations may be interesting, but they don’t necessarily translate to actionable safety improvements.
For example, imagine a team that, by comparing demographic data with safety incident reports, has determined that female workers are more likely to be hurt than males. In this case, being female correlates with increased risk. But being female is not the root cause of the risk, in the sense that women are not inherently more likely to become injured than men. Instead, the root of the risk probably boils down an issue like ergonomic safety controls that do not sufficiently accommodate women.
If the safety team in this example were to look only at data correlations, it might draw a flawed conclusion such as “we shouldn’t hire women because they’re more prone to getting hurt.” A better outcome would be “we need to redesign our ergonomic controls because they’re not adequately protecting female employees.”
Eliminate data that adds no value
Safety data is only useful if it leads to actionable insights. If it doesn’t, you should eliminate it from your data sets.
For example, imagine that years’ worth of safety data shows that the ambient temperature of your job site has no bearing on the rate at which injuries occur. In that case, there is no need to report on the data because it adds no value. Just because you can collect the data doesn’t mean you should.
Assess the assumptions behind data
Every data point that safety teams choose to collect has an assumption behind it—meaning the team believes the data can serve a particular purpose. Sometimes, those assumptions turn out to be flawed, and collecting the data can cause more problems than it’s worth.
For instance, imagine a safety team that tracks “operator error” as a cause of accidents within safety reports. The assumption behind that data categorization is that operators can make errors, and that when they do so, they are the root cause of an accident. In reality, of course, operators don’t deliberately choose to make errors. They do so only because safety controls are not in place to prevent mistakes.
Thus, a more effective way to categorize incidents would be to look at which safety controls were or weren’t in place and assess whether they are the root cause of accidents. That would lead to meaningful action, as opposed to a generic sense that if only the company could convince operators to make better decisions, they’d be safer.
From data to information to knowledge to actionable insights
To be clear, I’m not anti-data. On the contrary, I believe that data is the foundation for making workplaces safer. However, simply collecting data isn’t enough. The right questions need to be asked, and the right data needs to be used. Organizations should avoid systems and processes that are data rich but information poor.
Ultimately, the goal of every safety team should be to start with safety data that is important to their organization’s needs, then interpret that data in ways that transform it into meaningful information.
From there, they should take further steps to turn information into knowledge and insights that they can act on—such as deploying new safety controls. When this happens, data stops being a distraction or cost center and instead becomes a tool for achieving better safety outcomes for everyone.
Blake McGowan, CPE, is Solution Executive at VelocityEHS.