Unmasking the Data Quality Demon: Details Matter

Legacy data quality controls fall short for artificial intelligence (AI) systems.

In the age of artificial intelligence (AI), data has become the lifeblood of businesses, driving decision-making and automation. However, legacy data quality controls are no longer sufficient for AI systems. Hidden dangers lie in the granular details, and businesses must adapt to ensure the success of their AI initiatives.

Traditional decision-making dashboards have relied on the law of large numbers to average out individual errors, concealing potential data quality issues. For example, a few customers with a birth year of zero might not impact dashboard tracking of age group market segment growth rates. Similarly, excluding data rows with missing values for some fields might seem safe for dashboard metrics. However, these approaches fall short when it comes to AI systems.

AI systems operate at a granular level, making them highly sensitive to small data errors or missing information. They cannot rely on broad summary metrics to compensate for data quality issues like traditional dashboards. If underage customers have a birth year of zero, your AI might mistakenly sell alcohol to them. Moreover, missing data fields can lead to AI errors or, even worse, unpredictable and hazardous decisions.

Automated AI systems lack manual risk management processes, making them vulnerable to harmful decisions affecting customers and business operations. Without human intervention to apply common sense and domain knowledge, AI systems may struggle with seemingly minor issues. For instance, a healthcare organization found that doctors used two different spellings for the same drug, which the proposed AI system couldn’t process from doctors’ notes.

To address these challenges, businesses must incorporate comprehensive data quality protections into their modern data stack. AI pipelines demand greater care and nuance than BI pipelines, requiring more detailed error detection and correction. Early warning systems should trigger alerts when data quality standards aren’t met, allowing businesses to proactively address potential issues.

In a world increasingly driven by AI, attention to data quality is no longer a luxury – it’s a necessity.