Why AI data quality is key to AI success

In AI systems, data quality also encompasses factors that are less emphasized in traditional data quality dimensions—such as representativeness, bias, label accuracy and irrelevant variations (noise)—which can affect model behavior.

The importance of data quality in AI cannot be overstated: poor data quality is one of the most common reasons AI initiatives fail. AI models trained on flawed, biased or incomplete data will produce unreliable outputs regardless of how sophisticated architectures might be. As the saying goes: garbage in, garbage out.

High-quality data, on the other hand, forms the foundation of trusted and effective AI. As AI systems become more complex and scalable, continuous and robust data quality management will determine whether those systems can perform reliably, adapt to changing environments and enable informed decisions.

Advanced data quality tools can help streamline AI data quality management by embedding continuous monitoring and validation directly into data and model pipelines. In addition to rule-based automation, AI can be used to improve AI data quality by detecting subtle anomalies, prioritizing issues based on downstream model impact and much more. By automating checks for accuracy, consistency, completeness and other data quality dimensions, these tools help teams detect issues early and keep data quality aligned as AI systems evolve.

Organizations worldwide continue to invest heavily in AI. Global AI spending is forecast to surpass USD 2 trillion in 2026, representing 37% year‑over‑year growth, according to Gartner.¹ Yet this rapid expansion masks the fact that many AI initiatives struggle to deliver lasting value.

The IBM Institute for Business Value’s 2025 CEO Study found that only 16% of AI initiatives have successfully scaled across the enterprise,² while MIT’s NANDA study³ reports that up to 95% of generative AI pilots fail to progress beyond experimentation.

Research suggests that AI data quality and data governance are key differentiators within the AI ecosystem. A separate IBV study found that 68% of AI-first organizations report mature, well-established data and governance frameworks, compared with just 32% of other organizations.⁴

As the study’s authors note, “While less flashy than cutting-edge algorithms or ambitious use cases, this foundation of structured, accessible, high-quality data represents the essential precondition for sustained AI success.”

That foundation matters because machine learning models—a core part of many AI systems—“learn” directly from the datasets they are given. When that data misrepresents reality due to errors, gaps, outdated information, silos or systematic bias, models not only inherit those weaknesses but can also amplify data issues at scale.

For example, in generative AI systems, such as large language models (LLMs) used for natural language processing, data quality issues may surface as text with factual inaccuracies or biased image outputs. Poor data quality can also lead to uneven performance, particularly in edge cases such as uncommon inputs and underrepresented scenarios.

Even small percentages of low‑quality data can have outsized effects. Just a few poor results could undermine decision-making and trust in the technology overall, leading executives to conclude that an AI tool is defective when the root cause lies in the quality of the data informing it.

Beyond technical outcomes, low AI data quality carries legal and ethical implications, including risks related to data privacy and responsible data use. Models trained on poorly governed data can perpetuate discrimination in areas such as hiring, lending, healthcare and public services. At the same time, regulations including the EU Artificial Intelligence Act and a growing body of US state‑level AI laws increasingly hold organizations accountable for data privacy, as well as the quality, representativeness and provenance of training data.

Measuring AI data quality relies on many of the same data quality dimensions that are tracked through traditional data quality metrics. The difference lies in how data quality dimensions are reframed in AI scenarios: They are evaluated for their impact on model training, model generalization, fairness, operational risk—particularly as models are developed and deployed in different data environments.

When applied to AI systems, data quality is evaluated using adapted versions of the following data quality dimensions:

• Data accuracy
• Completeness
• Data integrity
• Consistency
• Timeliness
• Relevance

In traditional settings, accuracy focuses on whether data values correctly represent real-world entities or events, often verified through basic checks and predefined thresholds. In AI systems, accuracy also depends on robust data validation processes that assess how label noise (incorrectly or ambiguously labeled training examples), measurement error and proxy variables affect model training.

In addition to checking whether required fields or records are missing under completeness, for AI data quality, it extends to whether the data sufficiently covers the full range of cases the model is expected to encounter, such as edge cases, rare events and minority populations. Gaps in coverage can result in brittle models that perform well on average but fail in underrepresented scenarios, increasing fairness and operational risks.

Traditionally, data integrity is about making sure data follows basic rules such as adhering to the right schema and connecting correctly across systems. For AI, data integrity also means knowing exactly where the data came from and being able to recreate how it was prepared and used throughout the entire data pipeline.

Teams should be able to track data back to its original source and keep a clear record of every change made to it. Important data assets, including training data and model inputs, should be protected so issues such as accidental damage, duplication or unauthorized changes can be detected and investigated.

Beyond consistent formats and definitions, measuring AI data quality means examining whether data is collected, processed and augmented in consistent ways across historical and new data. This check helps ensure that changes in pipelines or sources do not inadvertently introduce distortions, bias or downstream model risk.

Classical timeliness focuses on how current data is at the point of collection. In AI systems, timeliness also requires monitoring how new or real-time data differs from training data, as data or concept drift can degrade model performance.

Instead of asking whether data is broadly useful or related to the problem domain, assessing data relevance in AI use cases means determining whether each feature and example provides information that supports the system’s intended function. This metric includes examining whether data improves predictive performance, supports robustness across different conditions, reduces sensitivity to noise or spurious correlations and facilitates downstream interpretability or diagnostics.

Measuring AI data quality establishes an initial baseline, but maintaining it requires continuous data quality monitoring as data, usage patterns and operating conditions evolve. Four foundational practices for improving and sustaining AI data quality include:

• Data profiling and exploration early in the lifecycle
• Data observability as the foundation
• Data quality checks using AI
• Closing the loop with remediation and feedback