Organizations can reduce carbon emissions by collecting data on what their emissions are and replace activities that are emission dense. To aid exploration, emission data can be visualized using a data visualization tool. A basic principle of designing a data visualization tool is the Visual Information Seeking Mantra (VISM): Overview first, zoom and filter, then details-on-demand. The company Measure & Change, which created a tool automating the retrieval and calculation of an organization’s emission data, has also designed a visualization tool for exploring such data. However, this tool was not created with the VISM in mind. This thesis discusses and evaluate (1) How can the data visualization tool be optimized using the design principle of Overview first, zoom and filter, details-on-demand? and (2) What is the impact of re-designing the data visualization tool in such a way? Through analysis of the dataset, user tasks, and the Visual Information Seeking Mantra, a new prototype was created. Both prototypes were then evaluated in A/B tests, with thinking aloud, a semi-structured interview, and the standardized ICE-T survey. The results suggest that the changes improved the tool, and that the user value increased. The ICE-T scores put the old implementation at 4.7, and the new prototype at 6. Visualizations scoring 5 or higher are generally accepted as valuable.