Jon Bate, Stephanie Hightower and Caleb Gage
Harmful behaviors such as violence, substance abuse, and suicidal ideations profoundly affect both individual soldier well-being and military unit cohesion, corroding the foundation of our combat readiness and pulling leaders away from their primary mission: preparing for combat. Too often, leaders have only reactive solutions available, such as ordering safety stand-downs after a harmful behavior has occurred. What if, instead of simply responding to such events, they could predict where it was most likely to occur and proactively intervene?
This question sparked a multiyear grassroots effort called Project Prevention. A brigade data analytics and innovation team in the 4th Infantry Division at Fort Carson, Colorado developed a new data-centric method to proactively identify units at risk and enable timely, preventive interventions. The result was the Unit Risk Forecasting Tool (URFT), which applies predictive data analytics to give leaders an additional tool to keep their soldiers ready to train and fight.
Over the past year and a half, this tool identified the specific companies/batteries/troops within a brigade that were at increased risk of harmful behaviors in a given week, enabling dozens of opportunities for leaders to proactively step in to address the root causes fueling the risks. It was a low-cost investment in combat readiness with a documented empirical impact on the health and readiness of the force. It does not replace, but rather augments and focuses, human intuition—helping highlight otherwise unnoticed risk trends that busy leaders may have missed. By systematically analyzing existing data and applying rapid data modeling, the URFT functions as an advanced early-warning system, monitoring the subtle, almost invisible atmospheric changes within a unit that signal a coming storm.
The tool is currently scaling more widely across Army units, but we have only scratched the surface of its potential to both help soldiers and increase military readiness. Deepening the data architecture, refining the risk algorithm, and adapting the tool to specific unit needs can amplify its future impact.
Finding the Signal in the Noise: The Momentum Effect in Harmful Behaviors
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