Data Debt: What Is it and How to Avoid It

Accruing data debt might be the cost of doing business in today's digital economy. But, just like its financial cousin, data debt can cost a business dearly if left unmanaged.  

And, while the monetary analogy makes for a helpful onramp, it's crucial to appreciate the unique and specific impacts data debt has on organizations. While gaining this appreciation, it's also essential to differentiate data debt from technical debt—an analog that occurs in data engineering and software departments.

Data debt, to begin with, refers to shortcuts and compromises made within an organization related to data quality, data architecture, and data management in an effort to accelerate access to data. As opposed to creating immediate issues, data debt accumulates over time and can create significant, long-term challenges that, ironically, "seem to come out of nowhere." And, while a majority of modern business models are susceptible to these effects, data debt is particularly troublesome within organizations for whom accessible, reliable, high-quality data is mission-critical. 

Alternatively, for technical debt, the causal decisions often involve hardware and software-related factors. While the "costs" associated with correcting technical debt can impact an entire organization, the damage inherent in data debt can be far more insidious. 

Data is increasingly—if not overwhelmingly—fueling the engines of modern business. Poor data quality, compounded by an accumulation of data debt, compromises data-driven decision-making.  

What's more, left unchecked, data issues can also lead to operational inefficiencies, hamper scalability, harm customer trust, increase security and compliance risks, and more. 

For these reasons, proactively handling data debt should be considered mission-critical. And the ability to do so hinges on helping stakeholders appreciate the multitude of ways data debt can damage an organization.

13 symptoms that may mean data debt is sinking the ship

1. Integration issues: When a company moves to adopt new tools or systems, data debt begins to complicate the integration process, leading to delays, complexity, and errors. This same situation can occur when two companies merge and are forced to reconcile two drastically different data systems. 
2. Project delays: Projects in highly data-dependent departments (e.g., IT and product development) may be the first to slow down, be put on hold, or begin going over budget as teams increasingly deal with inconsistencies or proper data hygiene.
3. System degradation: Data debt can begin to compound data integrity, creating malfunctions, service errors and outages, or even complete system failures.
4. Poor decision-making: It’s admirable when an organization’s leadership tries to use data to make better decisions. But data debt can begin empowering sub-optimal strategies and actions that cause more harm than good. In this sense, no data truly is better than bad data. 
5. Missed opportunities: As data grows untrustworthy or begins to lag behind real time, leadership will also struggle to capitalize on market trends, competitive intel, or the evolving needs of their own customers.
6. Reduced productivity: Employees who end up (re)cleaning, transforming, and validating data are spending less time on their own tasks. As data debt snowballs, inefficiencies and lessening throughput do as well.
7. Increasing operational costs: The constant rework needed to counteract an organization’s growing debt begins increasing direct costs like bringing in more resources to help, and indirect costs related to lost time and effort.
8. Security vulnerabilities: Data debt can begin to erode overall security, leading to lax user access management, inadequate encryption, or outdated data storage protocols that begin exposing the organization to unacceptable amounts of risk.
9. Regulatory and legal issues: Common debt contributors make it exceptionally difficult to handle user data with the same level of integrity and transparency once limited to a few specific industries, like finance and health care. But, while the scope of who needs to clear regulatory bars has changed, the consequences of failing to do so haven’t, often resulting in severe penalties and/or fines.
10. Decreasing employee morale: We’ve touched on impacts ranging from projects and processes to workflows and costs. Unfortunately, data debt can also announce itself through the toll it takes on employee wellbeing. Frustration builds as day-to-day employees continually deal with data-related fire drills, leading to low morale and updated LinkedIn accounts.
11. Dissatisfied customers: Ongoing data issues eventually leech out through the walls of the organization as billing mishaps, ineffective customer support, misleading product recommendations, and poor customer experience begin frustrating customers.
12. Brand reputation damage: The same data quality issues that begin to eat away at customer satisfaction can also harm an organization’s reputation within its industry. This, in turn, puts the screws to customer acquisition and retention.
13. Dulling competitive edge: Companies that manage data effectively generate insights faster, utilize them better, respond to market changes quicker, and not only innovate but do so more rapidly. For organizations drowning in data debt, the exact opposite holds true. And it’s these organizations that, sooner or later, find themselves losing step with competitors.

Best practices for managing data debt

Fortunately, despite all the factors that can contribute to data debt within an organization, there is an array of best practices that business leaders can champion to counteract debt’s ability to accrue. Broadly, these best practices can be lumped into three categories: procedural, technological, and cultural. Or, as it’s commonly known in reference to digital transformation, “people, processes, and technologies.”

Procedural best practices

These best practices begin and end with instituting and supporting a robust data governance framework. To counteract data debt, this framework needs to clearly outline all methodologies, use cases, procedures, and policies related to the data lifecycle. 

The governance framework should be employee-accessible, ensuring individuals at all levels of the organization understand data standards and how they can avoid contributing to poor-quality data. These efforts should also include training and workshops, so the entire organization gets on (and stays on) the same page regarding data management best practices.

As you develop your governance framework, put a system in place for documenting how data is collected, processed, and stored. Plan to use this system as a foundation for clear data lineage procedures in order to benefit from the ability to trace and map data’s unique journey throughout your organization.

Procedures also need to account for data retention policies, outlining where data should be retained, for how long, and whether it should eventually be archived or deleted entirely. Audit processes should then be put in place so that data is regularly reviewed for consistency, accuracy, and relevance.

Technological best practices

Next, turn your attention to tools and technology that actively work against data debt. Ideally, this begins by modernizing your data infrastructure. Data should be migrated carefully from any legacy systems to more agile, efficient, and scalable systems. And cloud-based solutions continue to make this increasingly easy to implement. 

Consider introducing data integration platforms to keep the merging, cleaning, and integration of data from different sources streamlined over time. Metadata management systems make it easier to manage, store, and access valuable context about data as it flows into, and through, your organization. Use data quality tools and automated data validation so that new data is validated against a specific set of standards, and corrected automatically when those standards aren’t met.

And while access to data documentation and context should be widely available, access to the data itself shouldn’t be. By implementing access control tools you can dial in user permissions, ensuring only specified individuals can access and modify data.

Cultural best practices

With proper processes and technologies in place, you must absolutely address the human element inherent in an organization’s data debt.

Start with buy-in from leadership because data quality is everyone’s responsibility. Without executives and stakeholders making this clear, data quality within an organization will rot from the roots up—diligent, day-to-day processes ultimately keep organizational data aligned with the truth. It’s important, then, to ensure top management commits and is ready to take accountability for their organization’s data debt. Leadership will also need to ensure best practices are funded and adopted. 

With leadership behind these efforts, a culture of data ownership can actually be fostered. As employees begin to benefit from data debt mitigation, it will be easier for peers to hold each other accountable for good data management and contributing to data quality.

As this sense of ownership takes hold, encourage a collaborative environment where departments work together on data projects, as this requires unified standards, promotes data literacy, and breaks down silos.

Keep communication channels open so that your increasingly empowered employees can easily suggest improvements and report data issues as they arise. Comparing and contrasting feedback from different departments can uncover valuable insights. And, finally, actively reward data hygiene.

A natural by-product of these best practices is that the idea of data quality and data debt will become much more tangible. Reinforce this tangibility by recognizing and rewarding teams and individuals who maintain the highest standards of data quality and management.

Data contracts: 10 ways better data agreements and specifications are paving a data debt-free future

Data contracts can play a pivotal role in minimizing or mitigating data debt. At its core, a data contract is a formal agreement or specification that outlines the format, structure, and semantics of data. They are particularly important when different systems or services interact with one another, ensuring a standardized and expected format of data exchange. Here's how they can help:

1. Standardization: Data contracts ensure a consistent structure, which means that data from different sources or teams adheres to a unified format. This reduces discrepancies and inconsistencies, a common form of data debt.
2. Validation: Data contracts can be used as a validation mechanism, ensuring that incoming or outgoing data meets specific criteria or standards before processing. This can prevent inaccurate or corrupt data from entering the system.
3. Documentation: A well-defined data contract serves as a form of documentation that’s version controlled, clarifying how data should be structured, what each attribute means, and any constraints on the data. This reduces the chances of misunderstandings or misinterpretations.
4. Interoperability: As organizations grow, they may integrate multiple systems, tools, or external services. Data contracts ensure seamless integration by setting clear expectations about how data should be sent and received between systems.
5. Reduction in ambiguity: With clear data contracts, there's less guesswork or ambiguity involved in how data should be managed or understood, reducing the risk of errors.
6. Easier troubleshooting: When issues arise, having a data contract in place makes it easier to pinpoint deviations from the expected data format, speeding up troubleshooting and resolution processes. Contracts also ensure that the correct data owners are contacted based on the specific issues at hand. 
7. Support for evolution: As data needs change, data contracts can be versioned, allowing for organized transitions and updates. This ensures that even as changes occur, there's a clear record of different data formats and structures over time.
8. Efficiency in collaboration: When different teams or departments collaborate, having clear data contracts reduces friction and misunderstandings. Everyone knows what to expect, ensuring smoother collaboration.
9. Transparency and trust: For consumers of the data (which might be internal teams or external partners), a clear contract gives confidence in the data's consistency and quality, fostering trust.
10. Enhanced compliance: For industries with stringent regulatory requirements around data, having clear data contracts can assist in ensuring compliance by setting and following clear data standards.

To harness these benefits, it's essential to ensure that data contracts are kept up-to-date, widely accessible to relevant stakeholders, and strictly enforced.

And that’s the core principle Gable was designed to address. Join our beta waiting list to be one of the first in your field to learn how data contracts are foundational aspects of a strong data strategy, effectively curbing the accrual of data debt.

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