Topological Data Analysis (TDA) provides a powerful framework for capturing the structural properties of complex data, with persistent homology, merge trees, and related descriptors playing a central role in both theoretical advances and machine learning applications. In much of current practice, however, these topological summaries are treated primarily as numerical features or inputs to downstream models, which limits their potential as tools for exploratory analysis and explanation. In this talk, we focus on the visualization and visual analytics aspects of TDA, advocating a shift from topological descriptors as static outputs to topology as an interactive analytical interface. We present visualization strategies that tightly couple persistence diagrams, merge trees, and the underlying data domain and applications, enabling users to interactively explore scale, feature relevance, and semantic meaning. Rather than evaluating topological methods solely through numerical metrics such as accuracy or stability, this perspective emphasizes interpretability, user-driven exploration, and task-oriented evaluation, demonstrating how TDA can support human-in-the-loop data analysis across a wide range of scientific and engineering domains.