Developing software in competitive environments often pushes functional decisions aside in favor of business priorities. Over time, this builds up friction that becomes a critical obstacle to your roadmap. This is technical debt.
According to Protiviti data, technical debt significantly affects the ability to innovate in 70% of organizations. In addition, it represents up to 30% of the IT budget, consuming resources that could be used more strategically.
We have helped multiple global companies manage their debt using different methodologies. One of the most effective approaches leverages DORA metrics to make debt visible and execute minimization strategies. Today, we explain it all.
What is technical debt?
It is the accumulated cost of development decisions that prioritize speed over quality, clarity, or system maintainability. As friction is left unresolved, technical debt grows higher and more complex.
Just like in finance, it may seem manageable at first, but over time the “interest” is paid in the form of:
- Recurring bugs.
- Delayed deliveries.
- Burned-out teams.
Beyond being an operational obstacle, reducing technical debt also requires significant investment. According to the Accenture Digital Core Report, remediating technical debt can account for up to 15% of a company’s annual IT budget.
In a dynamic sector like yours, debt appears when your team sacrifices automated testing, documentation, or sound architectural practices to meet aggressive deadlines or capitalize on a market opportunity.
The trap most technical leaders fall into is that technical debt does not hurt immediately: it manifests as a collection of small inefficiencies that slowly block product and business goals.
Four types of technical debt
Technical debt can be classified in different ways depending on the criteria used. For example, it can be grouped by the specific area where issues accumulate, such as:
- Code debt.
- Architectural debt.
- Design debt.
- Documentation debt.
- Infrastructure debt.
Another way to understand technical debt is through its relationship with context and intent. This approach was proposed by software engineer Martin Fowler in 2009, introducing a matrix that highlights both the amount of debt and the quality of the decisions that caused it.
Technical debt quadrants
Intentionality vs. prudence matrix
| Type of debt | Typical phrase | Characteristics |
|---|---|---|
| Deliberate and reckless | “There’s no time, ship it like this.” |
Absolute priority on delivery date. Best practices knowingly ignored. Generates very high and risky technical interest. |
| Deliberate and prudent | “We ship now and refactor later.” |
Strategically analyzed business decision. Documented debt with a realistic repayment plan. Accelerates time-to-market in a controlled way. |
| Unintentional and reckless | “What’s a design pattern?” |
Lack of training, standards, or mentorship. Errors due to carelessness or technical ignorance. The most difficult and expensive type of debt to manage. |
| Unintentional and prudent | “We learned a better way to do it.” |
Natural result of learning and scaling. The code was solid, but the business evolved. Indicates maturity and adaptability. |
First quadrant: deliberate and reckless debt
This appears when your team knows it is making a poor technical decision, is aware of better alternatives, but chooses to ignore them solely to deliver faster.
Example: a product owner commits to a key partner integration by a fixed date. To meet the deadline, the team connects directly to the database using queries without caching or usage limits, even though a better-designed official SDK exists but would take longer to implement.
This quadrant is the most dangerous for your roadmap, because it turns short-term urgency into a time bomb that impacts future delivery capacity. From a business perspective, it results in:
- Recurring incidents.
- Loss of team trust.
- Rising maintenance costs outside the budget.
Second quadrant: deliberate and prudent debt
Your team consciously sacrifices technical elegance today to capture a business opportunity, with a clear plan to refactor later.
Imagine you want to quickly launch a new pricing module and, instead of immediately redesigning the entire engine, you implement a simplified additional service coupled to existing components. You then proceed to:
- Document the decision made.
- Estimate the future refactoring effort.
- Link the decision to concrete metrics.
- Reserve future technical capacity to address it.
When used correctly, deliberate and prudent debt allows you to move faster than competitors without compromising long-term stability. It is a way to balance business growth with the preservation of your infrastructure.
Third quadrant: unintentional and reckless debt
This occurs when the team fails to understand the impact of its decisions and lacks basic practices to limit the damage. It is associated with inexperience, absence of code reviews, and neglect in addressing obvious design flaws.
There is no conscious decision to incur debt here, but there is negligence in failing to invest in minimum quality standards, automation, or training.
Example: your team grows quickly, you add junior profiles and maintain pressure before each release, but you do not introduce code reviews, increase automated testing, or improve documentation. The natural result is:
- Duplicated endpoints.
- Inconsistent rules.
- Circular dependencies.
Fourth quadrant: unintentional and prudent debt
Your team does the best it can with the available information, but later discovers a superior solution or new technical constraints.
A classic example: you choose a reasonable architecture for the current traffic level, and months later user growth reveals bottlenecks that did not previously exist.
The debt does not stem from bad practices, but from learning and product evolution. It is a signal that the business is changing and that you need to adjust the technical foundation to support new scale or complexity.
DORA metrics that make technical debt visible
In DevOps, DORA (DevOps Research and Assessment) metrics are indicators that measure the balance between system stability and delivery speed.
Beyond identifying bottlenecks, these metrics help classify the maturity of a development team, as they act as signals of efficiency and responsiveness to incidents or change implementation.
When these metrics degrade, technical debt is almost always behind it: fragile processes, complex architectures, or lack of automation. Indirectly, they are used to measure accumulated issues and their impact on delivery.
Change Failure Rate (CFR)
Measures the percentage of deployments that result in incidents, rollbacks, or hotfixes required to restore system stability. A high rate indicates that your code, architecture, or testing cannot support the current pace of business change.
- Fragile modules.
- Hidden dependencies.
- Lack of automated testing.
- Inconsistent environments.
Use this metric to prioritize which parts of the system require refactoring, better test coverage, or increased observability. The goal is to reduce failures and turn each incident into actionable insight on where your debt is concentrated.
Mean Time to Recovery (MTTR)
This measures how long it takes to restore service after an outage or production failure. It does not prevent errors, but it reveals how prepared you are to respond when technical debt becomes a heavy obstacle.
Reducing this time requires investment in:
- Observability.
- Automated rollbacks.
- Runbook documentation.
- Incident response training.
Lead Time for Changes (LTFC)
Shows the time elapsed from a code change to its deployment in production. This indicator evaluates organizational agility by showing how quickly you respond to incidents, customer feedback, and changes in business goals.
- Module simplification.
- Improved API design.
- Greater automation in your delivery pipeline.
Deployment time
This is the duration of the process required to deploy changes to production, from the moment they are ready until they are effective. The shorter the time, the higher the team’s performance, as it can execute multiple successful deployments in short cycles.
Optimizing this time involves simplifying deployment architecture, improving scripts, standardizing configurations, and strengthening CI/CD infrastructure.
