  Finance is heavily influenced by data-driven decision-making. Meanwhile, our
ability to comprehend the full informational content of data sets remains
impeded by the tools we apply in analysis, especially where the data is
high-dimensional. Presenting the Topological Data Analysis Ball Mapper
algorithm this paper illuminates a new means of seeing the detail in data from
data shape. With comparisons to existing approaches and illustrative examples,
the value of the new tool is shown. Directions for employing Ball Mapper in
practice are given and the benefits are reviewed.
