People love rankings, numbers, polls, and data.

We use them in sports, politics, education, and many other aspects of everyday life. Who’s number one in college football? How many stars did that movie or restaurant get? Sports teams are famously using analytics to drive operational decisions in developing their product (what players they draft) and in real time (whether to go for it on fourth down or punt the ball). Author Michael Lewis wrote about data-driven decision making in Major League Baseball in his bestseller “Moneyball: The Art of Winning an Unfair Game.”

Of course, businesses of all types frequently use data, numbers, and rankings to meet quality, sales, and productivity goals. Peter Drucker first talked about management by objectives (MBO) in his book “Practice of Management,” published in 1954. Drucker talks about creating SMART goals – specific, measurable, achievable, realistic, and timely – and using measurable data to set and track your organization’s achievement of these goals. Developing and using SMART goals is one of many concepts covered in MEMIC’s Leadership Training programs for managers and supervisors. You can contact your MEMIC loss control consultant or loss control office to learn more.

Recently we’ve seen new emphasis for companies to make “data-driven” decisions using real time data and analytics to help navigate their ever-changing business environments. This concept has taken on even more momentum with the pandemic and changes to supply chains, logistics, workplace locations and environment, employee and customer attitudes, and more. In many cases, businesses had to adapt, change, and reinvent themselves at unusually fast speed just to survive.

Decentralized data-driven decision making in larger organizations allows for local nuances in the business environment and uses the frontline worker’s knowledge of real-time customer needs. Forbes author Jennifer Day wrote an informative article, “How to Harness a New Wave of Data-Driven Decision Making”, which gives companies good suggestions about how to maneuver in a faster-paced, flatter, post-pandemic environment with proactive, collaborative use of analytical insights.

In the MEMIC Safety Expert podcast on Nov. 7, 2022, titled "Data Driven Safety Leadership" with host Peter Koch, guest Gary Bonnet of Safety Culture, a digital safety inspections and mobile training company, suggests data-driven decisions shouldn’t be punitive or reactive but used to grow and learn. Humanizing the data and sharing it with frontline workers empowers them to effect change in real time.

As safety practitioners, we need to be able to use data and key performance indicators to decide where our time and resources are best spent to have the biggest impact on reducing the number and cost of workers’ compensation claims. One key performance indicator is claims data from which key trends can be identified, including cause group, frequency and severity of loss, and claim type. However, this kind of broad loss trend may be insufficient to drive decision making.

 A deeper dive into the true root causes of an occurrence may be necessary, which includes who was involved, where the incidents occurred, what specific task was being performed at the time, and other details. This practice can be done manually or with newer software analysis, as described in our recent podcast. But the effort should point us in the same direction either way and allow for potential corrective actions to be developed.

Behavior-based safety programs identify critical task behaviors which are then observed by peers. The hazards are measured and communicated to reinforce safe behaviors. Electronic platforms exist to integrate real-time observations into more immediate communication and training opportunities, as our recent podcast discussed.

I conducted a loss trend analysis on a nursing home policyholder, and discovered injuries related to patient handling were the loss leader for severity and frequency. While not a surprise in this industry, a closer look revealed that most claims arose from a specific unit. We utilized a questionnaire to survey the caregivers and learned that 80% of the issues were from “over the bed” care – more specifically, resident repositioning in bed. This sparked further conversations with the caregivers and the Safe Patient Handling Committee leading to a trial of slide sheets which significantly reduced the physical exertion required for this repositioning task. Humanizing the data helped promote “buy-in” from the CNAs for new equipment trials. Although the process began with “lagging indicators” (losses that had already occurred), it led to proactive measures to reduce future incidents.

Safety practitioners can create a simple approach for using data to drive safety decisions.

Simply put:

• Know your vision or goal and begin with the end in mind.
• Determine what data sources or key indicators will help in reaching your goal.
• Organize, or “scrub,” your data.
• Perform data analysis, manually or with an electronic program or system.
• Draw conclusions and trial potential solutions.

As the Data Governance Institute states: “Most people seem to agree that obtaining cold data and information are not the same as obtaining knowledge. Anyone can obtain facts and figures. The trick is taking it all and turning it into knowledge.” 

Related resources:

• MEMIC podcast: SMART Goals with Randy Klatt
• MEMIC blog: Learn from History
• MEMIC blog: Tracking Your Safe Patient Handling and Mobility Program
• MEMIC blog: Target Mapping to Prevent Slips, Trips, and Falls