The idea that businessmen and women were part of a network of fellow business people who shared information and resources is not a new one. Historians have discussed business relationships as networks of people, but networks might also describe patterns of distribution, credit or information. Networks of business people have the effect of increasing the collective knowledge base, conferring status or respectability and lowering transaction costs. Historians of later periods have demonstrated how merchants came together to share skills and capital, and that networks often included people they had never met but rather knew by their association with the network. Networks, across all historical periods, are considered – theoretically at least – to be beneficial to businesses. Merchants are supposed to feel safer when going into business with someone with whom they have some fraternal connection, or who has been recommended to them by a person whom they trust. Networks are valued because they foster trust and solidarity. Indeed, whilst the word ‘network’ is a modern creation, the importance of collective endeavour or fraternal association – or indeed just the importance of family and trust, which lie at the heart of network analysis – are ideas that were profoundly medieval. Gervase Rosser has eloquently described how medieval guilds fostered new business relationships and allowed horizontal and vertical networks to be created and reinforced. However, commercial networks did not always benefit their members. Many transactions were undertaken by incompetent, quarrelsome and unscrupulous people who broke agreements and failed to repay the money they owed. For medieval historians this is something of a boon, as these people generally ended up being sued in borough courts and elsewhere, with the records of their failed transactions being recorded on the court's rolls.

This chapter examines business networks using debt litigation in later medieval Nottingham. It uses, for the first time in medieval studies, a methodology that is known as ‘visual analytics’. Visual analytics represents data graphically in order to reveal hidden properties within that data. The goal of this methodology, which is a complementary tool to traditional statistics, is to discover patterns and isolate idiosyncrasies within the data, and to allow users to interact more intuitively with arid and remote statistical evidence. Visual representations therefore make it easier for users to perceive salient aspects of their data.