Top 100 Enterprise Architecture KPIs

Summary

In managing software delivery projects, it’s essential to capture critical metrics beyond just lead and cycle times. This includes throughput (work quantity delivered in a specific timeframe), Work in Progress (current tasks being worked on), blocker clustering (identifying tasks frequently blocking progress), and defect escape rate (number of post-release defects). Also important are deployment frequency, change failure rate, Mean Time to Recovery (MTTR), and customer ticket volume, as these reflect on system robustness, team agility, and user satisfaction. Understanding the project’s technical debt and code churn gives insights into the project’s long-term health, stability, and the team’s decisiveness. Lastly, tracking feature usage can provide valuable feedback on the real-world applicability of the newly developed features. The value of these metrics lies not just in monitoring but in using them to make informed decisions to enhance the project’s direction and performance.

100 Metrics to Capture & Manage Technical Deliverables

  1. Throughput: This metric measures the quantity of work items delivered in a specific timeframe. This gives insight into the productivity of a team.
  2. Work in Progress (WIP): The number of tasks currently being worked on. This can help identify bottlenecks and manage workload for optimal flow.
  3. Blocker Clustering: The identification of tasks or issues that frequently block progress can lead to significant insights and improvements.
  4. Defect Escape Rate (DER): This is the number of defects that are discovered post-release. It provides insights into the quality of the work and can be used to improve development and testing processes.
  5. Deployment Frequency: The frequency at which you release new software. Frequent deployment is a key element of DevOps and agile methodologies and correlates with higher performance.
  6. Change Failure Rate: The percentage of changes that result in a failure. This could be a failure in the build, in testing, or a failure that is found in production.
  7. Mean Time to Recovery (MTTR): Measures how long it takes for a system to recover after a failure. The goal should be to reduce this number, indicating that the team is able to identify and fix problems quickly.
  8. Customer Ticket Volume: The number of customer support requests related to a product or feature can indicate its stability and usability.
  9. Technical Debt: While difficult to quantify, tracking the growth and resolution of technical debt can provide valuable insights into the long-term health and sustainability of a project.
  10. Code Churn: This metric refers to how much the codebase changes over a given period. High churn could indicate instability, frequent changes in requirements, or indecisiveness about implementation.
  11. Feature Usage: It measures how much a new feature is being used. This can give feedback on how valuable the feature is to the end-users.
  12. Time to Market (TTM): Measures the total time it takes from an idea being conceived until it is available for users. It’s a good indicator of how quickly a team or organization can deliver new features.
  13. Velocity: The speed at which a team turns user stories into deliverable software. Often measured in story points per sprint.
  14. Team Morale: A subjective metric, but vitally important. It can be measured through surveys and feedback sessions.
  15. Resource Utilization: The amount of time resources (people, infrastructure) are being used effectively.
  16. Sprint Burndown: Tracks the completion of work throughout a sprint.
  17. Escaped Defects: The number of defects that make it to production. Lowering this number indicates improving software quality.
  18. Code Review Coverage: The percentage of code changes that have been reviewed by another engineer. It can help maintain code quality.
  19. Automated Test Coverage: The proportion of your codebase covered by automated tests. It can give a rough idea of how much confidence to have in the codebase.
  20. Story Completion Rate: The rate at which user stories are completed. This can indicate if work is being appropriately scoped and managed.
  21. Application Crash Rate: The number of times the application crashes, divided by the total number of sessions.
  22. Efficiency: The ratio of productive work to the total time spent.
  23. Failure Demand: A measure of the amount of business demand caused by non-quality in production.
  24. Net Promoter Score (NPS): While this is generally a company or product-level metric, it can be useful for understanding how changes or features impact user satisfaction.
  25. Active Users: Number of daily or monthly users.
  26. Code Complexity: Complexity of the code, which can be calculated using different tools. High complexity might make the code hard to understand and maintain.
  27. Return on Investment (ROI): Measures the profit or cost savings attributed to a specific project relative to the cost of implementing the project.
  28. API Latency: Time taken for an API to respond. It can help detect performance issues.
  29. First Response Time: The time from when a customer raises a support request to when they receive their first response.
  30. Code Duplication: Repetitive code increases complexity and potential for errors.
  31. Productivity Ratio: The number of productive hours divided by the total hours worked. This can help understand how much time is spent on productive activities vs. administrative or other tasks.
  32. Cost Variance (CV): It shows how much over or under budget a project is.
  33. Schedule Variance (SV): It measures the degree to which a project is ahead or behind the planned delivery date.
  34. Business Value Delivered: Evaluates the return on investment from the features delivered.
  35. Backlog Growth Rate (BGR): The rate at which the backlog grows can indicate how quickly work is being added relative to how quickly it can be completed.
  36. Percentage of Work Reworked: Amount of work that had to be redone or corrected.
  37. Support Load: The ratio of support tickets to the number of users or sessions.
  38. Employee Turnover Rate: High turnover could indicate deeper problems within the team or project.
  39. Code Commits: The number of code commits made over a certain period of time.
  40. Rollback Rate: The number of times a deployment has to be rolled back due to issues.
  41. Uptime: The amount of time the application is up and running without any issues.
  42. Cycle Time Variation: The standard deviation or variation of the cycle time, which can give you a sense of predictability.
  43. Failed Deployments: The number of software deployments that failed.
  44. Mean Time Between Failures (MTBF): The average time between system breakdowns.
  45. Story Point Completion Ratio: The number of story points completed vs the number initially estimated for a sprint.
  46. Defect Density: The number of defects per size of a software product.
  47. Load Time: The time it takes for a software application to load completely.
  48. Process Efficiency: The percentage of total time that is value-adding.
  49. On-Time Delivery Rate: The percentage of tasks or projects completed on time.
  50. Test Case Success Rate: The percentage of test cases that pass in each run.
  51. Cost Performance Index (CPI): It measures the value of the work completed compared to the actual cost spent on the project.
  52. First Pass Yield: This is the percentage of units coming out of a process that are without defects.
  53. Requirements Stability Index: This metric shows the degree of change in the requirements of a project.
  54. A/B Testing Results: This quantifies the performance of two different versions of a feature or application.
  55. Individual Performance: Measures the performance and productivity of each team member, but use with care, as focusing too much on individual performance can negatively impact team cohesion.
  56. Security Violations: Number of identified security issues or violations.
  57. Code Lines Written: The amount of code lines written might give a rough idea of productivity, but remember that more lines of code does not necessarily mean more productivity or better code.
  58. Test Efficiency: It is the ratio of defects found during testing vs. total defects found (which includes defects found after release).
  59. Lead Time/Cycle Time Ratio: Comparing these two can provide a measure of process efficiency.
  60. Server Response Time: The time it takes for the server to respond to a user’s request.
  61. Application Usage Time: The duration of a user’s interaction with the application.
  62. Customer Retention: How many users continue to use the product over time.
  63. Conversion Rate: The percentage of users who complete a desired action.
  64. Sprint Goal Success Rate: How often your team is able to accomplish its sprint goals.
  65. Session Duration: The average length of a user session.
  66. Test Automation Percentage: The percentage of tests that are automated.
  67. Task Completion Rate: The number of tasks completed over a given period.
  68. Team Velocity Change: The change in team velocity over time.
  69. Incident Response Time: How long it takes to respond to a reported incident.
  70. Ratio Between Fixing Work and New Work: This metric provides insight into the quality of work being produced.
  71. Percentage of Post-Release Hotfixes: The amount of work done to fix urgent issues after a release. This can point towards problems in testing or quality control.
  72. Average Issue Resolution Time: The average amount of time it takes to resolve an issue from the moment it’s reported.
  73. Code Review Time: The time taken to perform a code review can give insight into code quality and complexity.
  74. Peer Review Feedback: This can indicate how well the team is functioning and how much they’re learning from each other.
  75. Average Session Length: This is a user engagement metric, showing how long, on average, a user interacts with your product in a single session.
  76. Software Reliability: The probability that software will not cause system failure over a specified time in a specified environment.
  77. CPU Usage: Percentage of the CPU capacity that your application uses.
  78. Memory Usage: The amount of RAM that your application requires to function properly.
  79. Code Coverage: A measure of how much of your code is covered by your unit tests.
  80. Number of Logins: Tracks user engagement by measuring how often users are logging in to your system.
  81. Cost per line of code: Calculates how much each line of code costs the company, taking into account developers’ salaries, tooling costs, etc.
  82. Ratio of Planned vs. Unplanned Work: How much of your team’s time is spent on planned tasks vs. unexpected or unplanned tasks.
  83. Downtime: The amount of time when the system is not available or operational.
  84. Idle Time: The amount of time when team members are not actively working on the project.
  85. Network Latency: Measures the delay data experiences as it travels from source to destination.
  86. Requirements Coverage: Indicates the percentage of requirements that are covered by the test cases.
  87. Risk Exposure: Identifies the degree of risk associated with a given project or feature.
  88. Test Case Effectiveness: Measures the effectiveness of test cases in finding bugs.
  89. Customer Complaints: The number of complaints received from customers can provide a direct line of sight into areas that may need improvement.
  90. Cloud Resource Usage: How much of your cloud resources (if applicable) are you using vs. how much you’ve provisioned.
  91. Story Points per Developer Day: This ratio can give a sense of how much work the team can handle on a daily basis.

Technical Debt KPI

  1. Total Technical Debt: The estimated effort to fix all issues, usually measured in person days or dollars.
  2. Technical Debt Ratio (TDR): Ratio of the cost to fix the debt to the cost of redeveloping the software from scratch.
  3. Code Churn: The amount of code that is frequently rewritten or changed can be a sign of underlying technical debt.
  4. Code Duplication: Duplication is a common source of technical debt that complicates maintenance.
  5. Code Complexity: High cyclomatic complexity can indicate convoluted logic that’s hard to maintain and can contribute to technical debt.
  6. Code Smells: Code smells are indicators of poor design and coding which may result in technical debt.
  7. Outdated Libraries: Technical debt can accumulate when dependencies aren’t kept up to date.
  8. Outdated Platforms: Technical debt can also accumulate when the underlying platform (like a web framework or the OS) is not kept up to date.
  9. Static Code Analysis Issues: Many static code analysis tools can provide indications of potential technical debt.
  10. Defect Density: A high defect density can indicate the presence of significant technical debt.
  11. Escaped Defects: Defects that reach production can indicate underlying technical debt.
  12. Refactoring Frequency: How often code needs to be refactored can indicate the presence of technical debt.
  13. Inefficient Architecture: Architecture that does not adequately support current needs can be a form of technical debt.
  14. Non-compliance Issues: Technical debt can result from non-compliance with coding or architectural standards.
  15. Rework: The need for rework can indicate technical debt.
  16. Test Coverage: Low test coverage can be both a form of technical debt and a risk factor for accruing more technical debt.
  17. Build Time: Longer build times can sometimes be a symptom of technical debt.
  18. Deployment Frequency: If frequent deployments become difficult, it may be due to technical debt.
  19. Code Comments Percentage: A high number of comments relative to lines of code might suggest complex or convoluted logic.
  20. Unresolved Issues: A growing number of unresolved issues can indicate mounting technical debt.
  21. Database Schema Changes: Frequent changes to the database schema may suggest technical debt.
  22. Obsolete Features: Features that are no longer used but are still maintained contribute to technical debt.
  23. Coupling: High coupling can increase technical debt as changes in one component may require changes in other components.
  24. Manual Processes: Manual processes can indicate technical debt, especially if automation would be a more efficient solution.
  25. Hard-Coded Values: The use of hard-coded values, especially when they should be configuration parameters.
  26. Security Issues: Security issues or hacks can both contribute to and be symptoms of technical debt.
  27. Code Review Issues: Issues that frequently come up in code reviews can indicate technical debt.
  28. Time to Understand the Code: The longer it takes for someone new to understand the code, the higher the likelihood of technical debt.
  29. Performance Issues: Performance issues can be a symptom of underlying technical debt.
  30. Lack of Documentation: Insufficient or outdated documentation can also be a form of technical debt.
  31. Number of Hotfixes: A high number of hotfixes may suggest the presence of technical debt.
  32. Incident Response Time: If the response time to incidents is increasing, it might be due to underlying technical debt.
  33. Ratio of Bug Fixes to New Features: A high ratio might suggest an accumulation of technical debt.
  34. Rate of Deprecated Functions: Functions that are no longer useful but still exist in the codebase are a form of technical debt.
  35. Unmanaged Dependencies: Dependencies that are not properly managed can add to the technical debt.
  36. Infrastructure Debt: Debt that is tied to outdated or inefficient infrastructure.
  37. Test Execution Time: An increasing time to execute tests may be an indication of growing technical debt.
  38. Support Ticket Volume: An increase in support tickets can be a sign of technical debt.
  39. Onboarding Time for New Developers: If it’s taking longer for new developers to become productive, the codebase may have significant technical debt.
  40. Lines of Code (LOC): A high LOC can indicate more complexity and potentially more technical debt.
  41. Ratio of Automated to Manual Tests: A lower ratio could indicate technical debt related to testing.
  42. Design Debt: Debt that comes from shortcuts or compromises in the software’s design or architecture.
  43. Workarounds: The number of workarounds needed to address system limitations can indicate technical debt.
  44. Dependency Cycle: Cyclical dependencies can create technical debt.
  45. Data Debt: This could be unoptimized databases, unused data fields, etc., contributing to overall technical debt.
  46. Lack of Coding Standards Enforcement: If coding standards are not consistently followed, technical debt can accumulate over time.
  47. Code without Ownership: Code that doesn’t have a clear owner can contribute to technical debt.
  48. Blocked Stories Due to Technical Debt: If new features or stories often get blocked due to existing technical issues, that’s a clear sign of technical debt.
  49. Incidents Root-Caused to Technical Debt: The number of incidents or outages that are traced back to technical debt.
  50. Code Stability: The rate at which the codebase changes. High rates of change can sometimes indicate technical debt.

Financial

  1. Rework Time: The additional time required to rework or refactor poorly designed or implemented code is a direct cost of technical debt.
  2. Increased Maintenance Time: Technical debt can make the codebase more complex and difficult to maintain, leading to an increase in maintenance time.
  3. Additional Testing Time: Technical debt can lead to more bugs and instability, which in turn leads to the need for additional testing.
  4. Delayed Time to Market: Technical debt can slow down the implementation of new features, leading to a delayed time to market. This can indirectly increase costs by reducing the time your product is generating revenue.
  5. Increased Incident Response Time: If your software has a high level of technical debt, you may face more incidents that require immediate response. This can lead to increased costs, both in terms of the resources needed to handle the incidents and the potential loss of customer trust or business.
  6. Increased Training Costs: A codebase with a high level of technical debt can be difficult to understand and navigate. This can lead to increased costs for onboarding new team members.
  7. Cost of Opportunity: With a high amount of time spent on dealing with issues arising from technical debt, there’s less time available to work on new features, improvements, or innovations.
  8. Increased Downtime Costs: If technical debt leads to increased downtime, there can be direct costs associated with loss of business and potentially compensation for SLA (Service Level Agreement) breaches.
  9. Decreased Developer Productivity: Dealing with technical debt can decrease developers’ productivity, leading to increased costs due to lower output.
  10. Increased Code Review Time: More time might be required to review the code if there’s high technical debt, as understanding such code can be difficult.
  11. Cost of Extra Resources: Technical debt often requires extra resources (like hiring more developers or using more expensive hardware) to handle the increased workload.
  12. Percentage of Budget Spent on Debt Management: The proportion of your budget spent on managing and reducing technical debt compared to feature development.
  13. Average Bug Fix Cost: If the cost of fixing bugs increases, it may be due to the additional complexity introduced by technical debt.
  14. Cost of Lost Customers: If technical debt leads to poor user experience or increased downtime, it can lead to the loss of customers and associated revenue.
  15. Frequency of Rollbacks: Frequent rollbacks due to failed deployments can be a sign of technical debt, and each rollback has associated costs.
  16. Legacy System Cost: The cost of maintaining legacy systems that are part of your technical debt.
  17. Overhead Cost of Manual Processes: Technical debt often includes areas that could be automated but aren’t, leading to higher costs.
  18. Cost of Code Duplication: Code duplication, a common form of technical debt, can increase the cost of code changes and bug fixes.
  19. Impact on Sales Cycle: If technical debt leads to a slower update or feature release cycle, it may slow down the sales cycle and lead to increased costs.
  20. High Server Usage Costs: Poorly optimized code, which is a form of technical debt, can lead to increased server usage and higher costs.
  21. Increased Code Review Costs: Poor code quality may require more time in code reviews, leading to increased labor costs.
  22. Data Migration Costs: If technical debt includes outdated or inefficient database systems, migrating to a new system can have a high cost.
  23. Cost of Security Issues: Technical debt can lead to increased security risks, and the costs associated with mitigating these risks can be significant.
  24. Inefficient Resource Utilization: Technical debt can lead to inefficient utilization of both human and hardware resources.
  25. Cost of Unplanned Work: Unplanned work, such as emergency bug fixes, is often a result of technical debt and has associated costs.
  26. Decreased Team Morale: Although difficult to quantify, decreased morale due to dealing with technical debt can lead to increased employee turnover and associated hiring and training costs.
  27. Cost of Scalability Issues: If technical debt hinders your application’s ability to scale, this can lead to lost opportunities and increased costs to refactor the application.
  28. Cost of Missed Market Opportunities: Delays in releasing new features due to technical debt can lead to missed market opportunities.
  29. External Consultant or Audit Costs: If the technical debt leads to the need for external consultants or auditors, this can significantly increase costs.
  30. Debt Service Ratio: This ratio represents the proportion of a company’s income used to service debt (in this case, technical debt). A high ratio means a significant portion of the income is being spent on managing technical debt, leaving less available for other business activities.
  31. Risk Remediation Cost: The cost associated with remedying risks that have surfaced due to technical debt.
  32. Reduced Value of Current Investment: The depreciation of your existing codebase due to increasing technical debt.
  33. Lowered Future Value of Investment: The potential reduction in the future value of your codebase because of the accrued technical debt.
  34. Inadequate System Performance Cost: If your system performance is hampered due to technical debt, the cost associated with additional resources to compensate.
  35. Increased System Support Costs: The rise in costs related to supporting systems burdened with technical debt.
  36. Increased Monitoring Costs: With increased technical debt, you might need to invest more in monitoring tools and processes.
  37. Cost of Dependency Management: Increased costs to manage dependencies due to a lack of proper software design and modularization.
  38. Loss of Competitive Advantage: The cost of losing business to competitors due to the inability to deliver features quickly because of technical debt.
  39. Cost of Postponement: The cost associated with postponing new features due to the resources spent on technical debt.
  40. Vendor Support Cost: If your technical debt includes outdated third-party tools or systems, you might have to pay more for vendor support.
  41. Infrastructure Scaling Costs: Infrastructure might need to be scaled up to handle inefficient processes caused by technical debt, leading to increased costs.
  42. Legal Costs: In certain industries, not addressing technical debt (especially regarding security issues) could lead to regulatory fines or legal issues.
  43. Operational Overhead Costs: Operational inefficiencies caused by technical debt can lead to additional costs.
  44. Knowledge Debt Costs: Costs incurred when new team members must learn to navigate around technical debt or when knowledge is lost due to team turnover.
  45. Business Continuity Risk Costs: Technical debt could increase the risk of significant business disruption, and the costs associated with this risk should be considered.
  46. Audit and Compliance Costs: Technical debt could lead to compliance issues, especially in regulated industries. The cost of audits and potential non-compliance should be considered.
  47. Lost User Productivity: If your application’s performance suffers due to technical debt, this could lead to lost productivity for your users.
  48. Early Obsolescence Costs: Technical debt could lead to your software becoming obsolete more quickly.
  49. Technical Support Costs: An increase in user support requests could be a sign of usability or performance issues related to technical debt.
  50. Integration Costs: If technical debt makes it harder to integrate with other systems, this can lead to increased costs.
  51. Decreased Market Value: A high level of technical debt can negatively impact your company’s valuation, making it more challenging to attract investors or potential acquirers.
  52. Reduced Employee Attraction and Retention: Technical debt can make it difficult for you to attract and retain top software engineers, as it can be seen as a sign of organizational inefficiency and a lack of commitment to innovation.
  53. Increased Operational Risk: Technical debt can increase the likelihood of unexpected outages, security breaches, and other operational disruptions, which can lead to financial losses, reputational damage, and potential legal liabilities.
  54. Reduced Ability to Pivot: If your codebase is burdened with technical debt, it can be more challenging to adapt to changing market trends or customer preferences, limiting your ability to capitalize on new opportunities.
  55. Increased Complexity of Mergers and Acquisitions: If you’re considering merging or acquiring other companies, technical debt can complicate the integration process and increase the overall cost of the transaction.