Technical Debt Metrics are quantifiable measures that evaluate the accumulated consequences of expedient technical decisions, deferred maintenance, design compromises, and architectural non-compliance that, while enabling short-term delivery, create long-term costs, constraints, and risks requiring future remediation to maintain system quality and business agility.

For enterprise architects, Technical Debt Metrics provide essential visibility into architectural health while establishing economic frameworks for remediation prioritization. Comprehensive measurement approaches typically address multiple debt dimensions: code debt at the implementation level; architectural debt affecting system structure and patterns; documentation debt concerning incomplete or outdated information; test debt regarding insufficient quality assurance; infrastructure debt involving outdated platforms; and skills debt reflecting outdated capabilities in the workforce. Technical leaders should establish multi-level measurement frameworks incorporating portfolio-level indicators showing enterprise debt patterns, application-level assessments revealing system-specific issues, and component-level metrics pinpointing specific remediation opportunities. The metrics approach must balance technical measures focused on internal qualities (complexity, duplication, coupling) with business measures addressing external impacts (maintenance costs, change velocity, incident rates) to communicate debt implications beyond technical teams. Integration with investment processes is essential, establishing financial models that quantify debt carrying costs, remediation expenses, and expected returns from debt reduction initiatives to support business-centric decision-making. As architectural practices mature, debt management typically evolves from periodic major remediation projects toward continuous refinement approaches where technical debt reduction becomes embedded in regular delivery activities with dedicated capacity allocations. Leading organizations implement AI-assisted debt identification tools that automatically analyze code repositories, architectural models, and operational data to detect debt patterns, predict future impacts, and recommend targeted remediation strategies. This data-driven approach transforms technical debt from subjective concern into objective economic consideration that systematically influences investment decisions, delivery priorities, and architectural governance across the technology landscape.