One of the data integration method is data consolidation. This method caputers data from multiple source system data and integrates it into a single persistent data. We examined the performance of data consolidation using k-means and gaussian mixture clustering. Meanwhile, we use Silhouette index as cluster validation and measure how well of a clustering relative to others. The experiments analyses the data in various data duplication rate and actual number of data cluster. Based on the experimental result, there are two factors affecting the performance of data consolidation. These factors are the rate/ percentage of duplicate data and the number of actual cluter contained in a data. The higher percentages of duplicate data and less number of clusters containde in the data would be increasing the performance of clustering algorithm.