Modern systems are becoming extraordinarily dependent on data. As a result, it’s becoming problematic for software engineers to view data quality as an abstraction or just a downstream concern. 

‍

As organizations become exponentially more data-driven, data quality is increasingly impacting software functions and how confidently software teams can make decisions. Even code that these teams write beautifully and fully test can fail quite spectacularly if it involves incomplete, inconsistent, or invalid data. 

‍

And due to the complexity of modern systems, software-related failures due to poor data quality can easily cascade, resulting in wasted engineering hours, broken business intelligence pipelines, and flawed customer experiences. This kind of bad data slows software development down and creates silos. It also acts like a thief, robbing organizations of the competitive advantages they work so hard to create.

Therefore, software engineers are increasingly realizing that they need to champion data quality just as much as they do their own code’s quality. But doing so means confronting a fundamental gap in ownership. Code quality is typically engineers’ responsibility, while data quality often isn’t—at least not in a way that organizations clearly define or consistently enforce. As a result, many teams that try to close the code quality vs. data quality gap start their efforts in the dark, without clear processes or shared expectations to guide them.

Shedding some light on the issue and, in turn, closing this quality gap begins by covering the basics: what code quality and data quality are, respectively, then comparing their similarities and differences. 

What is code quality?

From a software engineering perspective, code quality primarily refers to how well software code performs its intended function. But this concept also involves how easy it is to read, change, and maintain a given piece of code over time. High-quality code, therefore, enables software development and engineering teams to move faster and identify and reduce bugs more easily. It also ensures that the systems that developers and engineers build are more stable, scalable, and secure. 

‍

Ultimately, code quality’s defining features will vary across teams or programming languages, but as a general rule, it typically depends on correctness and clarity. As such, the best code works as intended and is easy for other developers to understand and modify, without said modifications breaking something else. 

Key dimensions of code quality

While the definition of code quality tends to vary slightly between teams or languages, most high-performing software engineers do agree on a shared set of traits. Below are a few of the most important of those dimensions to consider:

‍

• Readability: High-quality code is clearly structured and follows team conventions.
• Maintainability: Software engineers and developers can easily update or extend high-quality code without incurring unintended consequences.
• Testability: Engineers can use automated tests to easily verify the code. 
• Efficiency: The code avoids waste, be that unnecessary processing, bloated memory usage, or unnecessary complexity. 
• Robustness: High-quality code handles edge cases and fails gracefully when something goes wrong. 
• Security: This type of code also resists common vulnerabilities and protects sensitive logic or data. 

Based on this constellation of factors, it’s clear that code quality is the result of attention, skill, and experience—a cumulative result of intention and discipline across the entire software development lifecycle. Therefore, code quality enforcement requires a combination of peer reviews, automated testing, static analysis, and well-defined style guides. Together, these practices allow software engineering teams to catch problems early and standardize the way they write their organization’s code over time.

How to enforce code quality

Code quality enforcement hinges on embedding peer reviews, automated tests, and static analysis tools directly into the development lifecycle. Tools like SonarQube and CodeClimate support this kind of continuous inspection by flagging bugs, inefficiencies, and security vulnerabilities across large code bases. 

Others tools, like Checkstyle and SpotBugs, offer language-specific support to help developers write cleaner, more maintainable logic in languages like Java, Python, or C#. Software engineering and development teams also use version control systems like Git to manage changes and facilitate collaborative reviews, and they often use AI-powered code assistants like GitHub Copilot or Tabnine for further support. These assistants suggest real-time improvements based on learned programming patterns. 

Additionally, across mature organizations, teams often embed these code quality practices directly into CI/CD pipelines, where automated tests, static code analysis, and quality checks run before code reaches production. This allows engineers to catch issues early and maintain consistent engineering excellence, an approach that is now influencing how teams think about enforcing data quality at scale. 

What is data quality?

Similarly to code quality, data quality is a measure of excellence that refers to how reliable and useful data is for its intended purposes. Unlike code, however, these uses may instead involve powering an internal dashboard, training a machine learning model, or feeding a downstream product experience. As a rule, high-quality data, therefore, is data that is accurate, complete, consistent, and timely. 

Much like code, data also acts as a building block. But unlike code, data is constantly in flux—users update it, systems generate it, and integrations move it across platforms. This volatility makes data quality harder to predict and control, and it’s a leading cause of downstream failures in modern enterprise systems.

Key dimensions of data quality

Comparably, while the meaning of “good data” also shifts depending on context, enterprise, or use case, most data leaders and teams align around a consistent set of dimensions that define whether data is fit for their purpose. These are the most widely accepted of those dimensions:

‍

• Accuracy: High-quality data correctly represents the real-world entities, events, or values it describes.
• Completeness: All required data fields and records are present, so there are no gaps or missing information.
• Consistency: High-quality data remains uniform and aligned across different systems, sources, and time periods.
• Timeliness: Data is current and available when teams needs it for operational, analytical, or decision-making purposes.
• Validity: High-quality data conforms to defined rules, formats, and acceptable ranges that business and technical standards establish.
• Uniqueness: Each record in a high-quality data set is distinct, with no unintended duplicates that could distort analysis or downstream processes.

In light of these key data quality dimensions, software engineers should understand that data, unlike code, can break without warning, even after it’s already in production. For this reason, data engineering teams who deal with quality enforcement rely on continuous checks like schema validation, anomaly detection, and automated alerts. They can build some of these checks directly into data pipelines, but other quality checks run asynchronously, which enables teams to spot problems as they emerge. 

Additionally, data leaders are increasingly turning to data contracts to shift data left, which further improves data quality by making the expectations between data producers and data consumers more explicit and enforceable.

How to enforce data quality

In practice, data engineering teams can operationalize quality checks through an ecosystem of tools that they tailor to their organization’s stack and workflows. These tools typically fall into the following categories:

• Validation frameworks like dbt, Great Expectations, Deequ, and Soda Core enforce quality rules during data transformation. In doing so, they help teams test assumptions early and prevent issues like null values, duplicate rows, or invalid references before data moves downstream.
• Orchestration and observability platforms like Airflow and Datafold embed quality enforcement directly into data pipelines. In particular, data engineers often use SQL-based assertions and Airflow sensors to monitor quality as data moves and surface anomalies throughout integrated CI/CD pipelines. These tools provide shared visibility and real-time alerts when assumptions break.
• Metadata and lineage tools, such as data catalogs, improve visibility into data sources, data models, and pipeline dependencies. When teams pair them with metrics dashboards, these tools allow them track long-term quality trends using indicators like null percentages, duplicate rates, and transformation errors.
• Downstream impact tools like customer relationship management systems, business intelligence platforms, and other analytics dashboards rely directly on high-quality data. When upstream data integrity fails, these systems become the first to expose broken reporting, misleading analytics, or flawed user experiences, all of which erode trust and slow down decision-making.

In many ways, data quality efforts mirror the same discipline and intentionality that define code quality. For instance, both rely on rigorous testing, monitoring, and clearly defined standards to prevent failures before they spread. But when you look closer, it becomes clear that while these two forms of quality are conceptually aligned, they operate under very different conditions. For this reason, understanding how and why they diverge will help you understand why enforcing data quality demands its own specialized tools, methodologies, and mindset.

Code quality vs. data quality: Shared goals, but different roles

Be it code or data, when it comes to quality, both software and data engineers share the same end goal: to build systems that are reliable, trustworthy, and resilient over time. They also must monitor, review, and test both code and data to maintain quality over time. 

‍

Like code, high-quality data is critical to the health of downstream data consumers, whether they’re APIs, dashboards, or machine learning models. Because of this, focusing only on conceptual similarities can obscure some definitive differences between code and data. That’s why understanding the similarities between code quality vs. data quality is key for appreciating the different tools, techniques, and mental models you’ll need to enforce high quality. 

‍

Here are three of the most important similarities to be aware of: 

1. Volatility vs. version control

Code is deterministic. This means that once teams craft and deploy it, code tends to behave predictably. As a result, you can version it, lock it down, and know exactly what change introduced a bug.

By contrast, data is inherently dynamic. It flows through changing ETL pipelines, receives updates from users and third-party systems, and can silently shift in ways that violate business rules. This volatility is what makes data quality monitoring and data validation essential—it’s not always possible to catch bad data at the point of ingestion.

2. Pre-deployment testing vs. in-flight observability

High-quality code relies heavily on pre-deployment testing, which often includes static analysis, unit tests, and code reviews that catch most issues before a component reaches production. Data quality, however, can become compromised after it lands in production. This is especially true as it flows through the increasingly complex pipelines of modern enterprise organizations, where bad inputs may not break a system but can easily compromise its output.

Due to this operational reality, data observability—the ongoing process of data teams tracking and monitoring data’s health throughout its lifecycle—is a cornerstone of modern data quality enforcement. To make this observability actionable, teams should leverage data profiling, metadata management, and data pipeline monitoring to detect anomalies in real time, trace them back to their source, and resolve them without damaging downstream dependencies.

3. Clarity of ownership vs. shared responsibility

A final point of difference involves ownership. High-quality code typically has clear owners: the software engineers, developers, or teams who wrote it and go on to maintain it. Comparatively, the chain of custody for data, regardless of quality, is often much less definitive. In the data world, ownership often spans multiple data producers, platform teams, data analysts, consumers, stakeholders, and even legal or compliance departments.

Therefore, robust data governance (the formal process of ensuring that an organization’s data is well-managed, trustworthy, and compliant) becomes essential for maintaining optimal data quality. Poor or inconsistent data governance practices, especially in data-driven enterprise organizations, actively undermine data quality and lead to inconsistent, duplicated, or incomplete data, which may escape notice until a key report or product feature breaks. 

Together, these differences reveal a deeper truth: code and data may share the same goal of reliability, but they operate on entirely different terrains. Code quality is a function of precision and predictability, and its behavior is knowable and controlled. Data quality, on the other hand, is a function of context and change, so it’s constantly shifting. As a result, teams must validate it while it’s in motion. 

Solving for this quality conundrum requires more than just shared awareness. Truth be told, doing so also demands a shared framework. That’s where, as more software engineers are starting to appreciate, data contracts are proving to be the best way to close the gap.

Bridging the gap: Engineering common ground between code and data quality

Code and data quality have always mattered individually. But the interdependent demands of modern data-driven systems are making it more critical than ever to treat them as connected concerns. 

‍

To the data-conscious software engineer, yes, code quality ensures that systems run correctly. But they also know that data quality makes sure that those same systems produce the right results. 

‍

Therefore, logic dictates that the interface between the two—the space where misunderstandings, misalignments, and silent breakages occur—is now where the most costly failures tend to originate. This is exactly why a growing number of software engineering teams are shifting left and embedding data contracts directly into their workflows. 

‍

By codifying expectations between producers and consumers, data contracts bring the same rigor, visibility, and early warning systems that CI/CD pipelines brought to software. By making data quality actionable for software engineers in this way, contracts can shift the burden from cleanup to prevention.

Ready to catch data quality issues earlier, reduce breakages, and build systems that your teams can trust? Visiting Gable.ai to explore how data contracts can do exactly that is an exceptional way to get started. 

‍