Predictions can’t exist without the data behind them

Ryan Quiring
3 min readDec 17, 2020


Today we see artificial intelligence (AI) technology assisting so many professionals on a day-to-day basis. Similar to the internet services trends that happened in 2010, like Netflix, Spotify, and Uber, that have functionally replaced the need for physical movie rentals, purchasing music, and hailing a taxi cab, we see how AI can make dramatic impacts on specialized or niche tasks.

This is excellent news, and it won’t be long before we begin to see more and more of this AI assistant technology begin to roll out. We are already beginning to see strides made in training AI to perform repeatable tasks such as contract review and analysis for due diligence or its use in scheduling as a virtual assistant. We are seeing the early trend form, where this technology will take hold, very fast. Much quicker than the disruption that occurred with internet services.

Before we address the influence of AI on safety, it’s essential to establish what it is. Perhaps a more suitable description is cognitive computing. This means training computers how to learn, reason, communicate, and make decisions all on their own. Cognitive engines are trained vs. programmed, allowing them to learn how to complete tasks traditionally completed by people. The focus is pattern recognition in data sets, testing the data, and finding results. Essentially a colleague who can sift through the deck and tell you what they found. This is important because, according to IBM, we generate 2.5 quintillion bytes of data every day. In case you’re not up-to-date on a quint, that’s 2,500,000,000,000,000,000 bytes. Any human’s ability to review and comprehend that level of data without help is impossible.

For the realm of health and safety, predictive analytics is nothing new. It may not have been called analytics, but health and safety professionals have been leveraging many tools to predict where an incident may occur and prevent these incidents from ever happening.

Besides the setup, the difficulty with these systems is maintaining them. A continuous effort needs to go into keeping information up to date. This effort is called the administrative burden. This burden is the primary reason these systems either never get set up or aren’t used as long-term solutions.

What comes first

One of the first labor and capital intensive steps to getting an analytics system off the ground is to clean and consolidate field-level data. This data is usually trapped in paper or pdf format. The only reliable way to extract this information once placed in these formats is to read and transcribe it, using human labor. This usually impedes safety directors/managers from getting an approved budget for such an undertaking as bills start to add up well before anything tangible is delivered.

So rather than starting an analytics project with the end goal being a dashboard showing gaps in health and safety, we recommend starting with a digital field-level data acquisition project.

A data acquisition project can deliver results in a fraction of the time and highlight where safety has gaps in real-time — no need for the historical data to analyze behavioral trends in safety performance. Once you’ve started collecting data digitally, it becomes much easier to place it into a dashboard to uncover gaps as well.

Mobile apps have started to gain traction in the world of health and safety over the past five years. Digital data collection has begun to open up a whole new world of analytics. After decades of trying to solve this issue, it’s now as simple as an app.

The way that this data can be used is multifaceted. Anything from detecting anomalies in safety document answers, to understanding trends throughout your workforce, or even giving your workers that little extra nudge to complete safety documentation on time. The AI safety assistant isn’t just imaginary anymore, it's becoming realistic and in fact, affordable as well.

Soon safety professionals will be able to focus all of their attention on high-value tasks, such as building incentives programs for staff, championing safety leaders in their communities, and working closely with other stakeholders in projects and operations to tighten safety compliance through design.

I’m excited for the coming year — stay tuned to SafetyTek, as we plan to make this step into AI assisted safety a reality for all.

Till then, enjoy the holidays and stay safe!



Ryan Quiring

Passionate about solving one of the largest problems that still exist today — workplace safety. CEO & Founder SafetyTek.