Custom AI solutions depend heavily on the data you feed them when training. Incorrect labeling, outdated information, duplicate samples, hidden biases, irrelevancy, and other flaws directly affect whether users can rely on the output. Poorly trained systems deliver misleading results and may require costly retraining after being deployed. Otherwise, these systems will remain inefficient and lose to more refined and transparent systems.

Data quality checks minimize the risks of model inaccuracy or bias. They help ensure you feed the model with clean and well-labeled data that teaches it how to behave correctly. Besides such evident benefits as accuracy, data quality testing methods facilitate long-term software maintenance and regulatory compliance.

Read more about how to check data quality and why it matters in our review to launch a custom AI model, minimizing risks.

What is Data Quality Checks

Data Quality Checks is the process of cleaning and preparing data for training an AI model. The goal is to ensure that data is complete, accurate, consistent, and valid. Quality assurance also includes removing duplicate records, identifying unusual values, and checking for over- or underrepresentation of data classes to eliminate bias.

Why Data Quality Check is Essential for Training Models

Based on Geartnr’s prediction, organizations will abandon 60% of AI projects due to the lack of AI-ready data. When you improve data quality for AI before training models, you make an AI system more likely to operate accurately and meet the set business goals. Careful preparation before using data for training helps engineering teams prevent many long-term problems like low performance, limited capabilities, and bias.

Garbage In, Garbage Out Principle Still Works

The GIGO concept means you get out of the system what you put into it. In other words, if you use low-quality data to train the system, the output will basically have the same quality. That is the main reason for data teams to run data quality testing and eliminate flaws.

AI Software Performance and Accuracy

Recent research reveals that data quality is particularly important for small language model performance, with excessive duplication reducing accuracy by 40%. Data quality checks help ensure the standard format and structure of data. Such consistency facilitates data processing, helps models learn accurate patterns, and prevents unexpected failures. 

Higher Complexity of Machine Learning Tasks

For systems that require complex operations, such as unstructured data processing or multimodal inputs, data quality in AI is even more critical than for basic AI solutions. Pre-processed data enables advanced models to capture sophisticated relationships for demanding use cases such as autonomous driving, healthcare diagnostics, or financial fraud detection. Quality checks also make the use of such systems more transparent, which is crucial for achieving regulatory compliance.

Optimized Software Development Process

When engineering teams check data quality and eliminate issues from early development stages, it enables them to avoid costly retraining cycles. It also reduces debugging time and results in a more predictable product development timeline and launch. Data engineers and software developers can collaborate more efficiently thanks to a shared understanding of data, standardized processes, and documentation. For businesses, it means faster iteration cycles and optimized development costs.

Real Impacts of Ignored Data Quality Testing 

You may have heard about the recent Washington Post investigation into law enforcement agencies' misuse of facial recognition software. This case resulted in at least eight people being wrongfully arrested. The police skipped other evidence, but the AI systems were also misleading due to the low quality of surveillance images.

Another case about the sexist and ableist biases in OpenAI's Sora was recently published by WIRED. It was found that Sora's model perpetuates stereotypes in its results, showing men as CEOs and pilots and women in more service-oriented roles.

These are real data quality check examples showing the consequences of insufficient data preprocessing and the use of unreliable data for AI systems. Not all consequences are as drastic as in the listed cases; however, corrupted data and the lack of data quality testing always harm the reliability of AI results. When using high-quality data is not possible, software providers must ensure responsible product use and insist on keeping humans in the loop for critical decisions.