The hidden value of test data: a case study on tech debt & business value

Technical debt (tech debt) is a common challenge in software development, often cited as a top pain point for developers. Understanding and addressing this issue not only improves code quality and shortens development cycles but also enhances an organization’s overall business value. This article explores various facts of tech debt and the impact that quality test data can have in mitigating these challenges to reduce technical debt.

What is technical debt?

Technical debt refers to the future cost incurred as a software’s code evolves and drifts from the original tenants with which it was built.

Imagine a product that has been around for a decade. The first developer to work on it may have used some specific principles in her approach. Over time, other developers contribute who may not be aware of those principles, or important customers request capabilities that directly contradict these principles but still find their way into the product. This new code won’t fit in an ideal way with the original code and interacts with the system in imperfect ways as a result.

Tech debt also accrues when teams choose quicker, often suboptimal solutions over more robust approaches that would take longer to implement. In fact, the term was initially coined by software developer Ward Cunningham to draw a parallel with financial debt, suggesting that just as borrowed money accrues interest, so does the cost of cutting corners in software development.

Tech debt arises from a constantly evolving product, and the impossible challenge of redesigning your whole system from scratch every time the requirements change. Instead, you evolve it, and those evolutions don't always yield perfectly clean solutions.

The accumulation of tech debt often goes unnoticed but significantly impacts maintenance, scalability, and system performance over time. In the 2024 Stack Overflow Developer Survey, developers reported technical debt as their greatest source of frustration at work, with 62% of developers citing it as a problem.

When compared to initiatives like new feature development or critical bug fixes, tech debt often falls behind in priority. But given its underlying impact on product quality and developer productivity, we’re of the belief that it should be viewed as equally valuable to the business and product objectives. Let’s explore why.

Current rates of tech debt

The prevalence of tech debt is a growing concern, with many engineering teams witnessing its rise, as evidenced by the recent Stack Overflow survey. While not commonly quantified as an "interest rate" in financial terms, tech debt can be thought of as having levels—each reflecting the depth and breadth of the problem's impact on project health and developer productivity.

Let’s quantify this by putting some numbers behind these statements:

• Financial impact: A 2022 study by the Consortium for Information & Software Quality estimated that enterprises face a staggering $1.52 trillion in software technical debt.
• IT budget allocation: McKinsey research from 2023 indicates that technical debt accounts for approximately 40% of IT balance sheets, underscoring its substantial financial burden.
• Organizational challenges: In a report from STX Next, 91% of global CTOs named technical debt as one of their biggest challenges heading into 2024, making it the most commonly cited challenge and highlighting its pervasive impact on technology leadership.
• Compounding rates: A 2020 McKinsey survey of 50 CIOs revealed that 60% observed an increase in their organization's technical debt over the previous three years.

This growing technical debt poses significant challenges for organizations, including increased maintenance costs, reduced agility, and impediments to innovation. Addressing and managing technical debt is crucial for maintaining competitive advantage, operational efficiency, and long-term sustainability.

Common causes of tech debt

We’ve established that tech debt is on the rise, but what is spawning this growing problem?  Some of the most prevalent causes include:

Inadequate refactoring

Failure to regularly update the internal structure of code to enhance understandability or performance without affecting its behavior piles up tech debt. This is one of the quieter, more insidious ways that tech debt can accumulate. 

Insufficient testing

A lack of comprehensive testing leads to undiscovered bugs and bad code, making maintenance increasingly difficult and costly. Testing can be insufficient due to poor methodologies or due to weak test data that doesn’t account for all scenarios and edge cases.

Changing requirements

Frequent changes in project scope or objectives can lead software engineers to implement quick fixes that are likely to increase tech debt. Similarly, changes in production data can cause a disconnect between production and staging environments, if the test data isn’t kept in sync, resulting in weaker testing and more tech debt.

Outdated technologies

Utilizing obsolete technologies can trap projects in legacy systems that are expensive to update and maintain. They can also become a ball and chain on modern solutions, hindering the gains that are the goal of digital transformation.

Lack of automated processes

Manual processes are often slower and error-prone, contributing to tech debt by creating bottlenecks and inconsistencies. Automations can also be stymied by the tech debt of outdated technologies, creating a vicious cycle.

How to manage and reduce technical debt

Addressing technical debt requires targeted strategies that correspond directly to its common causes. Here are effective tactics to manage and reduce technical debt:

• Regular technology audits: Conduct frequent reviews of the technology stack to identify and phase out outdated technologies, adopting newer, more efficient tools that enhance development practices.
• Continuous refactoring: Schedule regular refactoring sessions to improve the structure of the codebase, making it easier to update and less prone to bugs as requirements evolve. 
• Automation of development processes: Utilize automation tools, like automated test suites, for continuous integration and deployment to minimize human error and streamline development workflows.
• Enhanced testing protocols: Implement comprehensive testing frameworks to ensure all new code is tested before deployment, reducing bugs and future maintenance requirements. Comprehensive testing requires comprehensive test data; more on this later.
• Adaptive planning: Develop flexible planning processes that accommodate changes without resorting to quick fixes that increase technical debt.
• Improving documentation: Ensure that the code is well-documented to facilitate maintenance and updates.

The role of test data in reducing technical debt

We’ve alluded to test data several times in this article, naturally in the context of testing. Some developers may even consider their test data to be a part of the tech debt, due to it being outdated or difficult to maintain. But given the test data solutions available today, test data no longer belongs in the tech debt box.

What’s more, the above tactics for tackling tech debt necessitate effective testing to achieve their desired results. For every bug created due to a missing refactor is a bug created by refactoring to reduce bugs. Having comprehensive testing (and test data management) is critical. Completing refactors "without affecting its behaviors" requires robust tests to make sure you do not, in fact, change the behaviors.

Test data enables you to handle tech debt by giving you the confidence to make changes to your code without having negative impacts on behavior and performance. This applies equally to the tactics of refactoring, automations, and regular technology audits. If you're going to swap to the latest and greatest, you need to be sure it won't unexpectedly break things on you.

Quality test data is balm for tech debt (a tonic, you might even say). It allows for rigorous testing environments that ensure you don’t introduce bugs while implementing refactors or replacing outdated technologies.

The business value of a reduction in tech debt

So how do you convince your development team to prioritize the unshiny initiative of addressing tech debt? Talk value to them. Reducing technical debt directly correlates with significant business benefits, including:

• Cost savings: The financial impact of tech debt is no joke, as evidenced by the vFunction study referenced earlier on. Addressing tech debt results in lower maintenance costs and fewer resources required for troubleshooting and fixing bugs.
• Accelerated development: Organizations that actively manage and reduce technical debt report improved time-to-market and increased agility in responding to market changes.
• Improved software quality: Higher quality of code leads to better software performance and fewer failures.
• Enhanced customer satisfaction: Reliable and efficient software improves user experience and boosts customer satisfaction.
• Developer happiness: Per Stack Overflow, tech debt is the number one pain of developers, slowing down their productivity and weighing on software’s time-to-market. Less tech debt means happier devs means better products faster.

Best practices for reducing technical debt

Reducing and managing technical debt effectively involves a holistic approach combining several best practices:

• Continuous learning and improvement: Foster an environment of continuous education to keep your team updated on the latest coding standards and technologies.
• Code reviews: Implement regular code review processes to catch issues early and ensure code quality remains high.
• Prioritizing tech debt reduction: There’s really no way around this. Teams need to dedicate time specifically for addressing tech debt, integrating these tasks into the regular development schedule to prevent accumulation.
• Automated tests: Implement automated tests into your development workflow to ensure that your tech debt reduction efforts don’t introduce new issues into your code.
• Prioritizing quality test data: Ensure developers have access to high-quality test data that reflects real-world usage scenarios, which is crucial for thorough testing and quality assurance. Tools like Tonic Structural can generate realistic and compliant data sets that significantly reduce the likelihood and impact of bugs, ultimately helping to manage technical debt more effectively.

Eliminate tech debt with quality test data

Eliminating tech debt requires a multi-pronged approach. Within that approach, having access to quality test data is indispensable. And getting that data is no longer a burden on developers, thanks to innovative solutions built for today’s data ecosystems and data privacy regulations.

Tonic Structural offers an enterprise platform for generating realistic test data through compliant data de-identification and synthesis. Similarly, Tonic Textual supports AI and machine learning projects by providing high-quality synthetic unstructured data that mirrors real-world free-text without compromising sensitive information.

The strategic prioritization of ensuring access to quality test data not only reduces the accumulation of tech debt but also enhances the overall efficiency and productivity of development teams. By investing in industry-leading platforms like those offered by Tonic.ai, companies can ensure that their software development processes are both efficient and secure, ultimately leading to better products and satisfied customers.