Tech debt exists at the intersection of engineering, business incentives, and system architecture. In complex organizations, it becomes a multidimensional problem involving operational risk, system reliability, long-term scalability, and developer productivity. In this analytically grounded episode, Duncan and Jason dissect tech debt through the lens of system thinking.They introduce a working model for categorizing tech debt into functional, structural, and data-related risk, explaining how each impacts throughput, incident frequency, and time-to-recovery. They also examine how vulnerabilities and poor data contracts masquerade as “bugs” but are often symptoms of deeper architectural debt. The conversation presents a practical playbook for leaders: how to assess tech debt, measure its economic impact, define acceptable thresholds, and integrate it into strategic planning.Top Takeaways:Tech debt can be defined in various ways depending on context.Shortcuts taken to meet business needs contribute to tech debt.Tech debt is not just about code quality but also about business outcomes.Standards change over time, leading to new tech debt.Quantifying tech debt is essential for effective management.Managing tech debt requires strategic planning and documentation.Business leaders need to understand the implications of tech debt.Justifying tech debt investments is a common challenge.Effective communication with business partners is crucial for tech debt management.A structured approach to documenting tech debt can aid in prioritization.Connect with us: Duncan Mapes Jason Ehmke DevGrid.io DevGrid on LinkedIn DevGrid on X

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