Failure: The Hidden Layer in Experiential Learning

In the 1970s, psychologist David A. Kolb formalized the concept with his Experiential Learning Theory (ELT), introducing the now widely adopted Kolb’s Learning Cycle: Concrete Experience → Reflective Observation → Abstract Conceptualization → Active Experimentation. This model provided a structured way to understand how people learn from experience in both educational and professional settings. 

Kolb's model highlights that learning is not linear but cyclical—it evolves through iteration. Our ability to capture data at every stage has made this cycle more powerful than it ever has been. From tracking outcomes of concrete experiences to reflecting with analytics dashboards, forming abstract concepts based on trends, and testing those ideas in new experiments, data enriches every layer. And nowhere is this more evident than in how we engage with failure.

This brings me to the main point of this cyclical evolution: how we treat failure not as a setback, but as a strategic input in the learning loop. In the modern enterprise, failure has always been a necessary step towards faster innovation and better products but has lacked the scientific rigor and weightage that needs to be assigned to failure just like success.

At Trunknode, we believe failure is a critical part of the modern enterprise data stack. Today, most organizations meticulously capture data on success metrics—conversion rates, retention, efficiency—they must also gather structured data from what doesn’t work. Failures, when instrumented properly, generate insights that no dashboard on success alone can reveal. Every misstep, false start, or unmet goal is rich with signals—data that reveals gaps in assumptions, limitations in design, or shifts in context. Failure becomes an accelerator and a super power.  By treating failed experiments as structured learning events, teams gain clarity, resilience, and direction. Failure is one of the hidden layers of intelligence—quite often overlooked, but critical to innovation.

With the right data infrastructure, organizations can now capture outcomes from every experiment—whether a product test, a process redesign, or a customer engagement strategy—and analyze them to inform rapid iteration.  

Time to move from Episodic Insights –> Continuous Intelligence.