The term “technical debt” first arose in the domain of software development. It was coined by Ward Cunningham, one of the pioneers of software development and a co-creator of the Agile Manifesto, in the early 1990s. Cunningham used the metaphor of technical debt to describe the long-term costs of taking shortcuts in software design and development. These shortcuts might lead to faster delivery in the short-term but accumulate “debt” that must be “paid back” through refactoring, debugging, or rewriting code in the future. Just like financial debt, technical debt incurs “interest” over time in the form of increased maintenance costs, complexity and potential issues that can hinder future development.

The hidden costs of technical debt in the world of software development can be significant and may not always be immediately apparent. This problem manifests itself in a number of ways: increasing maintenance effort, increasing costs, slowing down long-term agile development cycles, reducing product quality, impacting team morale and productivity, putting projects at risk or increasing project failure rates, increased or hidden infrastructure costs and, lastly, risks around compliance or standards that aren’t being met.

So, Can Technical Debt Apply To The Data Domain Too?

Yes, yes and yes! Technical debt is highly relevant to the data domain, particularly in the context of data management, data architecture and data engineering. In the realm of data, technical debt can manifest in several ways and its consequences can be just as significant as in software development.

The biggest challenge to the business, aside from actually solving the symptomatic issues you would expect to see when technical debt starts manifesting around data practices, is how to get rid of the problem. As with any kind of debt, it tends to create a big, cavernous black hole that sucks up resources, time, efficiency, quality, money and so much more.

There is an extensive list of areas that are affected if this problem is not addressed. Some of the obvious ones are any of the following:

• data quality issues
• complex or fragile data pipelines
• scalability issues
• performance issues
• data governance challenges
• non-standardised practice
• increased maintenance and operational costs
• manual interventions where automation could be used
• continued use of legacy systems
• delayed delivery of projects
• blocked innovation
• compliance risks

Unravelling Technical Debt

The trouble with technical debt is trying to unravel it. It costs money. It consumes time. It means putting other important initiatives on the back burner. It is risky to tackle this challenge because determining root causes can deliver ambiguous or contentious insights on feasibility and there are no guarantees that the expected outcomes will be satisfied. But technical debt in data management can have far-reaching consequences and it is essential to address the challenge by investing in the necessary solutions properly. By doing so the organisation ensures that data becomes a valuable asset rather than a liability.

Unravelling technical debt in data management without making the investment excessively onerous requires a strategic, incremental and disciplined approach. Here’s a guide to managing and reducing technical debt efficiently:

1. Prioritise And Assess Technical Debt

• Identify High-Impact Areas
• Assess the Risks
• Estimate Costs and Benefits

Start by identifying the areas of technical debt that have the most significant impact on your data operations. Focus on issues that are causing the most pain such as frequent pipeline failures or poor data quality. Evaluate the risks associated with each piece of technical debt, including potential compliance issues, security vulnerabilities or scalability challenges. Prioritise debt that poses the greatest risk to business operations. Calculate the costs of addressing each piece of technical debt versus the benefits. This will help in deciding which debts are worth tackling first and which can be deferred.

2. Adopt An Incremental Approach

• Small, Manageable Changes
• Integrate Debt Reduction into Ongoing Work
• Focus on Quick Wins

Break down the work into small, manageable tasks that can be accomplished within regular development cycles. Avoid attempting a large-scale overhaul which can be costly and disruptive. Incorporate technical debt reduction efforts into your regular workflow planning. Look for low-hanging fruit—small changes that can significantly improve system performance or data quality with minimal effort. These quick wins can build momentum and demonstrate the value of addressing technical debt.

3. Automate Where Possible

• Testing
• Data Cleaning and Validation
• Monitoring and Alerts

Implement automated testing for data pipelines, transformations and integrations to catch issues early and reduce the manual effort required to maintain data quality. Use automated tools for data cleaning, validation and deduplication to improve data quality without relying on manual processes. Set up automated monitoring and alerting systems to detect and address issues in real-time reducing the operational overhead associated with managing technical debt.

4. Restructure Legacy Systems Gradually

• Modular Restructuring
• Legacy System Wrapping
• Phased Migration

Instead of trying to refactor entire systems at once, focus on individual components or modules. This reduces the risk and cost while allowing for incremental improvements. If fully replacing legacy systems isn’t feasible, consider wrapping them with modern interfaces or APIs that improve integration and interaction without needing a complete overhaul. Plan phased migrations from legacy systems to newer technologies gradually transferring functionality while minimising disruptions.

5. Enhance Documentation And Knowledge Sharing

• Document As You Go
• Encourage Collaboration
• Onboard New Tools

Ensure that every change made to reduce technical debt is well-documented. This includes updating data lineage, data dictionaries and process documentation. Foster a culture of collaboration and knowledge sharing among teams. Regularly hold meetings or workshops where team members can share insights and lessons learned from addressing technical debt. Invest in tools that help with documentation such as automated documentation generators to reduce the manual effort required to keep documentation up to date.

6. Balance New Development With Debt Reduction

• Allocate Time for Debt Reduction
• Align with Business Goals
• Measure Progress

Ensure that some portion of each development cycle is dedicated to addressing technical debt. This might be 10-20% of capacity around projects depending on the severity of the debt. Tie debt reduction efforts to business goals, showing how reducing debt will improve agility, data quality and overall performance. This alignment helps justify the investment and keeps the focus on high-value tasks. Track the impact of debt reduction efforts on key metrics such as system performance, data quality and time-to-market. Use these metrics to demonstrate the benefits of ongoing debt reduction to stakeholders.

7. Leverage Open Source And Third-Party Tools

• Open-Source Solutions
• Third-Party Integrations

Explore open-source tools and frameworks that can help with specific aspects of data management, such as data pipeline orchestration, ETL processes or data quality monitoring. These tools can provide cost-effective solutions for reducing technical debt. Consider third-party services or platforms that can offload some of the technical debt burden such as managed data services, cloud-based ETL tools or automated data governance platforms.

8. Educate And Train The Team

• Ongoing Training
• Encourage Best Practices
• Improve Data Culture

Provide ongoing training for your team on best practices for data management, standards and tools that can help reduce technical debt. An informed team is less likely to introduce new debt. Foster a culture where best practices are followed consistently, reducing the likelihood of accruing new technical debt. This includes policy or process reviews, adherence to design principles and proper testing protocols. Ensure that this mindset cascades across the entire organisation so that different teams have context of the challenges, the remediation approaches to be taken and buy-in to support the changes required.

9. Plan For Long-Term Sustainability

• Proactive Debt Management
• Iterate and Improve
• Budget for Debt Reduction

Implement a proactive approach to managing technical debt by regularly reviewing and addressing it before it becomes a significant issue. This might involve periodic reviews and data audits. Continuously iterate on your data systems, incorporating lessons learned from previous efforts to reduce debt. This ongoing improvement cycle helps keep technical debt under control. Include technical debt reduction in your budgeting and planning processes ensuring that it is recognised as a necessary and ongoing investment.

Do Not Delay…

Unravelling technical debt in data management does not have to be excessively burdensome. By prioritising debt reduction, taking an incremental approach, automating where possible and balancing new development with debt management, organisations can effectively reduce technical debt over time without overwhelming resources. The key is to treat technical debt as a long-term, manageable concern rather than a one-time fix.

First, the needs of the business should always be well understood and met before tackling the challenge. In all these conversations around data and Big Data challenges, data governance is a continuous thread. Do not skimp in this area. Scalable architecture gives the organisation the edge to be adaptive and focused on continuous improvement as opposed to continuous maintenance. By prioritising documentation and collaboration, organisations can reduce the long-term costs associated with technical debt and maintain more agile and sustainable data systems.

And don’t forget to tackle Data Culture head-on in the process!