Supply chain networks are complex and often proprietary, which implies that on the most part, the structure of a company’s supply chain is not well known nor accessible. This research investigates supply chain network topology, properties and supply network evolution using a data-driven approach. The key idea is to construct a sample set of data from a financial source and examine it in the context of supply network topology. This represents a new direction, since, while financial data has been applied by researchers to explore financial relationships in a supply chain, the application of this data source to determine the underlying topological characteristics is still in its infancy. As a starting point, we create a sample of supply networks from the retail industry sector (two from home improvement industry and one from the sporting goods industry). We expect that the retail industry will provide a rich and dynamic source of representative data for a typical supply chain network. We use the sample data sets to identify specific topological characteristics (for example, average degree, network diameter, average path length, and degree exponent) which help explain the evolution and dynamics of a modern supply network. Using these identified characteristics, our plan is to expand the selection to cover additional networks over a wider time-span in order to generalize the findings.