I can't reliably join datasets because one source uses country names while another uses ISO codes.

Add a country identifier normalization step that maps country names and common variants to standardized ISO country codes (and back) before you join datasets. This makes joins consistent across sources and reduces mismatches caused by formatting differences or alternative spellings.

This feature helps you reliably join datasets when one source represents countries as names (e.g., "United States") and another uses ISO codes (e.g., "US" or "USA"). It provides a normalization step that can be applied to one or both datasets to create a consistent join key, such as ISO 3166-1 alpha-2 or alpha-3. During setup, you select the input country column, choose the target standard, and generate a new normalized field to use in joins. The mapping handles common aliases, punctuation/casing differences, and known alternative names so that "Côte d’Ivoire" and "Ivory Coast" resolve to the same standardized code. Any values that cannot be confidently mapped are surfaced for review so you can correct them or add an override. Once normalized, you use the standardized field as the join key across tables, pipelines, or reports. This reduces join failures, prevents silent data loss from unmatched rows, and improves downstream aggregation accuracy. The feature is useful for combining metrics from multiple vendors, harmonizing historical datasets with changing country naming conventions, and preparing data for consistent filtering and reporting by geography. It also supports repeatable workflows so the same normalization logic can be reused across projects and updated without rebuilding joins.