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Managing Technical Debt Plays Benefits Against Risks

Charles Caldwell
Logi Analytics

Everyone laments technical debt like it were a high-interest credit card. But just like how your CFO uses debt as capital for the business, the intelligent Product Manager knows that technical debt can help finance your path to market if you know how to manage it well.

Product managers who choose when and where it's acceptable to take on technical debt to overcome limited budget, constrained resources or critical deadlines, and budget their resources to resolve that debt at reasonable points in the future, avoid those nightmare scenarios. They recognize that taking on some debt can deliver real benefits so long as it's managed.

Finding the sweet spot between avoiding all technical debt and leveraging the right amount to get to market on a timeline that matters is a key skill for successful product teams.

Don't Build Too Little … or Too Much

Speed to market is a constant driver for product teams, with a high focus on feature delivery that can lead to an anemic architectural ramp. This is the source of technical debt that most teams are used to seeing. All the velocity is on features, and architecture "just happens" (or doesn't). Features are delivered that are not fully fleshed out, and the foundation they are built on won't support the actual feature requirements. While this is fine for an initial feature release to get feedback, repeated iterations result in a brittle product.

While a lot of technical debt comes from investing too little in supporting architecture, we see too many teams swing the other way and build far too much "infrastructure" upfront. Trying to anticipate everything a feature will ever need to do and build out the most beautifully architected backend for high-scale perfection before a single feature ships.

If the team is building too much enabling architecture at the onset, it's setting itself up for technical debt resulting from a change in requirements. Failing to get features out the door, the team doesn't get feedback until a lot of code is built. If you've got it wrong, you end up with a ton of technical debt in the form of an architecture that will never result in value to the customer.

There are, of course, feature sets that require a large amount of enabling technology. Features that have significant, complex components across multiple application tiers often resist iterative, MVP-style implementation. There are times when the MVP requires a lot of backend capability just to get the most basic version of the feature out the door. These are great cases for a buy/partner/open-source approach. Yes, you may accept some technical debt in the form of integration or "someone else's code," but if there is any risk around feature requirements, technical debt will pay dividends in the short term as you validate the feature. Place finite resources, including talent, toward solutions that could be resolved more efficiently by third-party options instead is another way technical debt mounts.

In the simplest term, reasonable technical debt is a trade-off. It's the result of identifying what's acceptable now that's worth addressing later. That's wholly manageable. What's unforeseen or overlooked that demands attention later is technical debt that every product manager wants to avoid.

To solve this and other varieties of technical debt, choose off-the-shelf options, either at the project's beginning or when they're needed. As noted above, embedded analytics allows managers to place solutions right into the development pipeline and move on. Time and talent spent focusing on other areas of the project offset the costs of buying a solution.

Debt Equilibrium

Technical debt is acceptable and even desired in some instances. When creating a genuinely trendsetting product, getting it to market as soon as feasible is the best way to obtain crucial user feedback. Addressing every possible way the new product will be used may be impossible to predict. So, creating an operational framework with simple, adaptable features that can be reliably built out into a compelling business solution for the client is a terrific way to identify and accept technical debt and leverage it for a project's benefit.

Identify technical debt and prepare for it to eliminate unpleasant surprises. Avoid it where possible and accept it where the benefits outweigh its drawbacks.

Manage and pay down debt by planning for it, choosing where and when it serves project purposes. This will ensure team momentum, the efficient delivery of products with state-of-the-art functionality, and expand the number of viable solutions to consider for addressing technical debt on projects in the future.

Charles Caldwell is VP of Product Management at Logi Analytics

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Managing Technical Debt Plays Benefits Against Risks

Charles Caldwell
Logi Analytics

Everyone laments technical debt like it were a high-interest credit card. But just like how your CFO uses debt as capital for the business, the intelligent Product Manager knows that technical debt can help finance your path to market if you know how to manage it well.

Product managers who choose when and where it's acceptable to take on technical debt to overcome limited budget, constrained resources or critical deadlines, and budget their resources to resolve that debt at reasonable points in the future, avoid those nightmare scenarios. They recognize that taking on some debt can deliver real benefits so long as it's managed.

Finding the sweet spot between avoiding all technical debt and leveraging the right amount to get to market on a timeline that matters is a key skill for successful product teams.

Don't Build Too Little … or Too Much

Speed to market is a constant driver for product teams, with a high focus on feature delivery that can lead to an anemic architectural ramp. This is the source of technical debt that most teams are used to seeing. All the velocity is on features, and architecture "just happens" (or doesn't). Features are delivered that are not fully fleshed out, and the foundation they are built on won't support the actual feature requirements. While this is fine for an initial feature release to get feedback, repeated iterations result in a brittle product.

While a lot of technical debt comes from investing too little in supporting architecture, we see too many teams swing the other way and build far too much "infrastructure" upfront. Trying to anticipate everything a feature will ever need to do and build out the most beautifully architected backend for high-scale perfection before a single feature ships.

If the team is building too much enabling architecture at the onset, it's setting itself up for technical debt resulting from a change in requirements. Failing to get features out the door, the team doesn't get feedback until a lot of code is built. If you've got it wrong, you end up with a ton of technical debt in the form of an architecture that will never result in value to the customer.

There are, of course, feature sets that require a large amount of enabling technology. Features that have significant, complex components across multiple application tiers often resist iterative, MVP-style implementation. There are times when the MVP requires a lot of backend capability just to get the most basic version of the feature out the door. These are great cases for a buy/partner/open-source approach. Yes, you may accept some technical debt in the form of integration or "someone else's code," but if there is any risk around feature requirements, technical debt will pay dividends in the short term as you validate the feature. Place finite resources, including talent, toward solutions that could be resolved more efficiently by third-party options instead is another way technical debt mounts.

In the simplest term, reasonable technical debt is a trade-off. It's the result of identifying what's acceptable now that's worth addressing later. That's wholly manageable. What's unforeseen or overlooked that demands attention later is technical debt that every product manager wants to avoid.

To solve this and other varieties of technical debt, choose off-the-shelf options, either at the project's beginning or when they're needed. As noted above, embedded analytics allows managers to place solutions right into the development pipeline and move on. Time and talent spent focusing on other areas of the project offset the costs of buying a solution.

Debt Equilibrium

Technical debt is acceptable and even desired in some instances. When creating a genuinely trendsetting product, getting it to market as soon as feasible is the best way to obtain crucial user feedback. Addressing every possible way the new product will be used may be impossible to predict. So, creating an operational framework with simple, adaptable features that can be reliably built out into a compelling business solution for the client is a terrific way to identify and accept technical debt and leverage it for a project's benefit.

Identify technical debt and prepare for it to eliminate unpleasant surprises. Avoid it where possible and accept it where the benefits outweigh its drawbacks.

Manage and pay down debt by planning for it, choosing where and when it serves project purposes. This will ensure team momentum, the efficient delivery of products with state-of-the-art functionality, and expand the number of viable solutions to consider for addressing technical debt on projects in the future.

Charles Caldwell is VP of Product Management at Logi Analytics

Hot Topics

The Latest

From smart factories and autonomous vehicles to real-time analytics and intelligent building systems, the demand for instant, local data processing is exploding. To meet these needs, organizations are leaning into edge computing. The promise? Faster performance, reduced latency and less strain on centralized infrastructure. But there's a catch: Not every network is ready to support edge deployments ...

Every digital customer interaction, every cloud deployment, and every AI model depends on the same foundation: the ability to see, understand, and act on data in real time ... Recent data from Splunk confirms that 74% of the business leaders believe observability is essential to monitoring critical business processes, and 66% feel it's key to understanding user journeys. Because while the unknown is inevitable, observability makes it manageable. Let's explore why ...

Organizations that perform regular audits and assessments of AI system performance and compliance are over three times more likely to achieve high GenAI value than organizations that do not, according to a survey by Gartner ...

Kubernetes has become the backbone of cloud infrastructure, but it's also one of its biggest cost drivers. Recent research shows that 98% of senior IT leaders say Kubernetes now drives cloud spend, yet 91% still can't optimize it effectively. After years of adoption, most organizations have moved past discovery. They know container sprawl, idle resources and reactive scaling inflate costs. What they don't know is how to fix it ...

Artificial intelligence is no longer a future investment. It's already embedded in how we work — whether through copilots in productivity apps, real-time transcription tools in meetings, or machine learning models fueling analytics and personalization. But while enterprise adoption accelerates, there's one critical area many leaders have yet to examine: Can your network actually support AI at the speed your users expect? ...

The more technology businesses invest in, the more potential attack surfaces they have that can be exploited. Without the right continuity plans in place, the disruptions caused by these attacks can bring operations to a standstill and cause irreparable damage to an organization. It's essential to take the time now to ensure your business has the right tools, processes, and recovery initiatives in place to weather any type of IT disaster that comes up. Here are some effective strategies you can follow to achieve this ...

In today's fast-paced AI landscape, CIOs, IT leaders, and engineers are constantly challenged to manage increasingly complex and interconnected systems. The sheer scale and velocity of data generated by modern infrastructure can be overwhelming, making it difficult to maintain uptime, prevent outages, and create a seamless customer experience. This complexity is magnified by the industry's shift towards agentic AI ...

In MEAN TIME TO INSIGHT Episode 19, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA explains the cause of the AWS outage in October ... 

The explosion of generative AI and machine learning capabilities has fundamentally changed the conversation around cloud migration. It's no longer just about modernization or cost savings — it's about being able to compete in a market where AI is rapidly becoming table stakes. Companies that can't quickly spin up AI workloads, feed models with data at scale, or experiment with new capabilities are falling behind faster than ever before. But here's what I'm seeing: many organizations want to capitalize on AI, but they're stuck ...

On September 16, the world celebrated the 10th annual IT Pro Day, giving companies a chance to laud the professionals who serve as the backbone to almost every successful business across the globe. Despite the growing importance of their roles, many IT pros still work in the background and often go underappreciated ...