Genome-wide association studies (GWAS) have proliferated rapidly, producing summary statistics on thousands of phenotypes. However, integrating and analyzing summary statistics across studies remains challenging due to inconsistencies in variant encoding, trait categories, file formats, and metadata reporting. For instance, allele codes like A/T versus 0/1 may differ, while same traits may be labeled differently across studies. These inconsistencies can lead to errors when combining datasets for meta-analysis or looking up variants across different studies. Manual curation and harmonization of variants and traits requires extensive effort and is error-prone. Automated harmonization pipelines are needed to enable rapid, accurate integration of GWAS summary statistics from diverse sources. By aligning summary statistics to a common data schema, data harmonization facilitates powerful cross-study analyses and enhances the utility of GWAS repositories. Implementing robust harmonization workflows will increase efficiency, reproducibility, and scientific insights as GWAS analyses continue to scale up in the era of big data.