Data Quality in AI: Challenges, Implementation, Audits, & Best Practices

As organizations adopt predictive and generative artificial intelligence (GenAI) to enhance their operations, the role of data quality has never been more critical. High-quality, reliable data is the foundation of effective AI, powering everything from training and testing to inference and decision-making. 

To support this, many organizations are upgrading their data infrastructure. They’re moving to scalable cloud platforms, real-time analytics, and easier-to-use developer tools. PwC found that 72% of top-performing companies have prioritized cloud data modernization — more than twice the rate of their peers (33%). 

Why data quality matters in AI

AI models are only as good as the data they’re trained on. Inaccurate or incomplete datasets can introduce bias, reduce model accuracy, and result in flawed insights that hurt the business.

A Fivetran study found that poor data quality often leads to misinformed decisions, costing organizations up to 6% of their global annual revenue.  

Data quality dimensions to improve for AI 

In addition to cleaning and ensuring quality, preparation is about making sure data supports AI's technical goals and related business outcomes. Data engineers play a critical role here, understanding how data attributes impact model performance and making sure training sets are aligned with broader objectives. 

They typically focus on a few core dimensions of data quality:

• Accuracy: Are the data values correct? Mislabeled or incorrect data can quickly degrade a model's performance. 

• Completeness: Are all the needed fields filled in? Missing values can confuse models and lead to less reliable results. 

• Consistency: Are formats and records standardized across all sources? Inconsistencies (e.g., different date formats or duplicate customer records) can skew training and create problems later on. 

• Integrity: Do the data elements make logical sense together? Strong data integrity helps models draw more coherent and trustworthy conclusions. 

Avoiding common AI data quality challenges 

While data quality is strategically important to AI, quality and accuracy have dropped 9% since 2021, according to VentureBeat. The reason? Modern AI models are more complex and data-hungry. They demand more nuanced, annotated, and frequently refreshed inputs. Common challenges include: 

• Data errors and inconsistencies: Manual mistakes, format mismatches, and conflicting records can weaken how a model reasons and learns.

• Labeling inconsistencies: When annotations vary across datasets, it can confuse models, lower accuracy, and make it harder to track performance.

• Bias in data: Skewed or non-representative data — often introduced unintentionally — can lead to unfair or unreliable outcomes. 

Using metrics to improve AI data quality

Tracking the right metrics helps catch issues early and measure progress over time. By setting baselines and monitoring changes, teams can create a more reliable foundation for AI. Some of the most popular metrics for tracking AI quality include: 

• Error rate: Measures how often incorrect data points appear in the dataset. A high error rate can mislead models and reduce their effectiveness.

• Completeness score: Evaluates the percentage of fields that are fully populated. Missing data can make models less accurate and less able to generalize.

• Data timeliness: Assesses how fresh the data is. This is especially important for models that need up-to-date information, like fraud detection, forecasting, and personalization.

• Uniqueness: Flags duplicate records that could overweight certain patterns and introduce bias.

• Validity: Tracks whether data follows expected formats and rules. Invalid or out-of-range values can cause training errors or even model crashes. 

For Large Language Models (LLMs), a crucial aspect of improving and maintaining data quality involves robust evaluation techniques, often referred to as "Evals." These Evals help assess the model's quality and accuracy by examining various aspects like factual correctness, coherence, toxicity, and relevance, providing critical feedback for continuous improvement.

Building strong data quality programs

Strong data quality doesn’t happen by accident; it’s the result of a carefully planned strategy and ongoing management. Data teams can build better foundations for AI by setting clear standards and continuously monitoring for risks. Here are some suggested best practices: 

• Defining policies: Set expectations for creating high-integrity data that is relevant, explainable, and easy to interpret. The Data Foundation recommends aligning with governance principles that support responsible AI. 

• Monitoring models: Use security and monitoring tools to catch anomalies, such as unusual data patterns or potential poisoning attacks that could manipulate model training.

• Remediating issues: Prioritize problems based on their severity (critical, high, medium, low) and address them early to reduce the impact on model performance. 

Techniques to improve and maintain AI data quality 

Even with a strong strategy in place, maintaining high data quality requires day-to-day tactics that spot and fix issues before they grow. Teams often combine several techniques to keep datasets clean, reliable, and fit for AI: 

• Profiling: Analyzing data to spot patterns, outliers, and potential quality problems.

• Cleansing: Correcting or removing inaccurate or misleading data points.

• Enriching: Adding external or third-party information to make datasets stronger and more complete.

• Standardizing: Making sure formats and structures are consistent across the entire dataset.

• Validating: Checking that data meets pre-set rules (e.g., teams often split datasets into a training set and a testing set to catch issues early).

• Applying governance: Defining ownership, setting policies, and increasing accountability, all while meeting compliance needs. 

Auditing and monitoring for long-term success

Data quality is a 24/7, 365 ongoing process. Continuous monitoring and regular audits help teams catch new issues early and keep AI systems performing reliably over time. Best practices for long-term success include: 

• Evaluating data sources and formats: Regularly check data pipelines for reliability and ensure consistent formatting across systems like CSV, JSON, or SQL.

• Measuring data against quality metrics: Use established metrics to catch problems like missing fields, out-of-range values, or unexpected anomalies.

• Identifying gaps in governance and tooling: Audit workflows to find weak spots in areas like data lineage, access controls, and security.

• Implementing automated alerts for anomalies: Set up systems that automatically flag unusual patterns, shifts, or missing data that could hurt model performance. 

Tools supporting AI-driven data quality

Improving data quality at scale requires the right tools to spot and prevent problems. Leaders in data, analytics, and AI often drive these efforts by investing in platforms that make data validation and monitoring more systematic, such as: 

• Great Expectations: An open source framework for building and validating data quality checks.

• Soda: Offers real-time monitoring and automated testing to catch data issues early.

• Monte Carlo: Focuses on data observability and detecting anomalies before they affect downstream systems.

• Snyk: Helps developers spot security issues tied to data, code, and compliance, a valuable piece of every strong data governance strategy.

A practical plan for improving AI data quality 

As AI becomes more tightly integrated with software systems, data quality and application security can’t be treated as separate concerns. Errors in one area can easily ripple into others. That’s why leaders are prioritizing continuous improvement programs built around these pillars:

• Data preparation and cleansing: Use preprocessing pipelines to standardize, enrich, and deduplicate data before training begins.

• Data quality enhancement and transformation: Leverage ETL/ELT tools that include quality checks, and keep refining those checks based on how your models perform.

• Governance and culture: Build a data culture where employees take ownership of quality, set clear expectations, and treat data like a product that needs ongoing care.

• Security tools that support development: Equip teams with tools like Snyk that help detect and fix vulnerabilities in code, open source packages, containers, and cloud infrastructure, which are all critical components in secure AI systems. 

Make data quality a strategic priority

AI has widened the aperture for attacks. Now, attackers don’t just target applications, they go after the underlying data and models themselves. And with the fast pace of software delivery and the growing use of AI-assisted coding tools, there’s more room for mistakes to slip through.

With 40% of auto-generated code checked in without modification and over a third containing known vulnerabilities, attackers increasingly exploit AI-assisted development pipelines using tactics such as cross-site scripting and SQL injections. 

To stay ahead, security, compliance, and development teams need to work together. Investing in secure development practices, like proactive code scanning and remediation with tools such as Snyk, helps reduce the risk of vulnerabilities making their way into AI-powered systems and supports the integrity of the applications that handle sensitive data. This collaborative approach is further championed by initiatives like the Linux Foundation's Data & AI efforts, where Snyk is a proud sponsor, fostering a community-driven environment to address AI data-related concerns and promote open source innovation in AI and data.

Ensure AI data quality with Snyk 

Reliable AI starts with strong data and building systems to catch small issues before they become a bigger problem. As organizations rely on AI for analytics, automation, and customer tools, maintaining data quality through every stage of the lifecycle is mission-critical.

Snyk helps developers do their part by securing the code that processes and interacts with that data. With Snyk Code, teams can scan both handwritten and AI-generated code for vulnerabilities. Snyk Code is powered by Snyk Agent Fix, which delivers safe, immediately actionable fix suggestions directly in the IDE. 

Get more best practices for AI data security.