dataset-quality-and-linkability-diagnostic

Diagnosing phone network dataset quality involves verifying standard graph structure, required file availability, and data health for analysis. This Skill checks ID naming spaces and cross-province linkability to ensure your data is ready for unified analysis.

Cross-province linkability checks whether ID naming spaces align across multi-province data sources. It matters because consistent entity linking across provinces is required to build a unified dataset capable of supporting accurate multi-source analysis.

Yes, you can use DuckDB with Python to diagnose data quality and linkability for multi-province datasets. This Skill uses DuckDB to process data health checks, capability matrices, and cross-province entity linking within unified datasets.

The best way to verify standard graph structure is to run a diagnostic script that checks for required files, ID naming spaces, and cross-province linkability. This validates that the phone network dataset meets structural standards before analysis.

Yes, you need Python to check ID naming spaces and cross-province linkability, as this Skill requires Python and DuckDB for processing. You execute the diagnostic wrapper script via the command line by passing the dataset root, dataset name, and province parameters.

Cross-province entity linking fails during a data health check when ID naming spaces are inconsistent or the phone network dataset lacks a standard graph structure. Running a linkability diagnostic identifies these specific structural and naming issues.