In the era of big data, there is a growing need to explore large, high-dimensional datasets in an unsupervised manner to identify clusters, detect outliers, and generate labels. Many existing tools and methods facilitate these tasks through visual exploration, but there remains a significant gap between the ‘zoomed-in’ view, which focuses on individual objects using tables and bar plots, and the ‘zoomed-out’ view, where the entire dataset is visualized in two dimensions using dimensionality reduction techniques like PCA or t-SNE. To bridge this gap, we propose VISAnt, a visual data analytics tool that employs Chernoff faces visualization. Alongside the standard two-dimensional dataset projection followed by a scatterplot, VISAnt represents groups of adjacent objects using Chernoff face glyphs, while allowing users to zoom in on individual instances within these groups. This approach not only aids in revealing local patterns along with the view of the whole structure, but also facilitates comparing the efficiency of different dimensionality reduction methods in a given dataset. We demonstrate the utility of VISAnt in a real-world case study involving patients with multiple sclerosis.