Many biological disciplines use data visualization alongside computational methods to explore large-scale biomedical data. Visualization often provides insight into patterns in the data that are not available in the numerical data and statistics. The development of new visualization tools requires the use of sophisticated software and programming skills. Commercial standalone software like Tableau create multiple types of common visualizations and have the ability to customize certain features. There are also freely available software libraries like D3.js that can be used to make interactive web applications based on static or dynamic data. Nevertheless, modern data visualization is highly sophisticated, and creating customized visualizations to interact with a specific dataset can be challenging for a variety of reasons. Specifically with D3.js, which builds a scalable vector graphic (SVG) programmatically, generating the visualization is a process of trial and error. The programmer generates SVG markup manually and then views it with a browser and does this iteratively until the final graphic is realized. We present an iterative workflow shown in Figure 1 that simplifies the creation of SVG images using freely available software. We demonstrate this workflow in constructing an interactive dashboard to track clinical trial enrollment.