A national health insurance company is embarking on a complex AI project to assist in...

PMI-CPMAI treats data as a central asset and states thatrepresentative, high-quality training data is essential for safe and effective AI in sensitive domains such as healthcare. Before sophisticated bias metrics or advanced KPIs are useful, the guidance stresses a phase ofdata understanding and preparation, where teams analyze data sources, coverage, completeness, and consistency, and ensure that the training set reflects the relevant populations, geographies, and use cases. PMI describes this as “profiling and exploring data to understand distributions, outliers, missingness, and segment coverage, then cleaning, integrating, and transforming it into a trusted, analysis-ready dataset.” In a multi-country health insurance scenario, with diverse hospitals and different privacy regimes, this step includes mapping schemas, resolving identifiers, handling missing or noisy records, and ensuring that patients from different regions, demographics, and care pathways are adequately represented without oversampling or excluding key groups. Simply increasing the size of the dataset without ensuring diversity and representativeness may reinforce existing biases or create blind spots. Likewise, KPI enhancement comes later, once the data foundation is sound. Therefore, thecritical step to optimize representative training datain this context is toimprove data understanding and preparation, ensuring that the integrated dataset is complete, consistent, diverse, and properly structured for training.