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

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