The widespread availability of geospatial data through spatial data infrastructures, cloud platforms, and web services has made data evaluation a central component of GIS practice. Effective use of geospatial data depends on understanding not only spatial extent and structure, but also data quality, semantics, provenance, and usage constraints. Standardized metadata has emerged as the primary mechanism for documenting these characteristics and enabling dataset discovery, interoperability, and long-term reuse. Key dimensions of geospatial data quality include positional and attribute accuracy, logical consistency, completeness, and currency, each of which contributes to assessing fitness for use. Provenance and lineage information further support transparency, trust, and reproducibility by documenting sources, methods, and processing decisions. Emerging applications of artificial intelligence offer new opportunities to assist with metadata creation and analysis, but do not eliminate the need for human judgment in evaluating geospatial data.