AI is only as good as the data that feeds it
For AI to make reliable, fair decisions, it needs to consume a data diet that’s balanced and trustworthy. Poor data is like AI junk food: full of empty calories that lead to poor outcomes. But give it the good stuff—accurate, complete, and timely data—and you’re on the path to model success.
High-quality data keeps your AI on track, meeting the standards and compliance checkpoints that matter.
Here’s the basic approach to data quality testing (access the full guide at the bottom of the article):
Define quality benchmarks: start by clearly establishing what quality means for your AI project. This involves setting benchmarks based on the specific use case, such as required accuracy levels, completeness thresholds, and consistency needs.
Categorise data quality attributes: break down data into core attributes like accuracy, completeness, and timeliness, then assess each. For example, ensure values are not just correct (accuracy) but fully present (completeness) and up-to-date (currentness).
Use automated tools for consistency checks: automated data quality tools can quickly catch inconsistencies across large datasets, such as duplicate entries or contradictory records, which are common pitfalls in AI applications.
Validate against real-world contexts: test datasets against known real-world scenarios to ensure they align well with intended model applications. Representative data that mirrors the diversity of real-world conditions will help models generalize and avoid bias.
Perform iterative testing: data quality is dynamic, so testing should be ongoing. Regular quality checks allow for timely adjustments, ensuring the data continuously meets the evolving requirements of AI applications.
In short, data quality testing for AI goes beyond single checkpoints. It’s a structured process that evaluates data at every stage—keeping models accurate, fair and equipped for real-world decision-making.
These are just some of the key points to be aware of, which is why we’ve written a more in-depth guide about testing data quality for AI-powered solutions.
You can access the full guide here, provided by our partner RISE Research Institutes of Sweden.