  Nowadays, social networks of ever increasing size are studied by researchers
from a range of disciplines. The data underlying these networks is often
automatically gathered from API's, websites or existing databases. As a result,
the quality of this data is typically not manually validated, and the resulting
networks may be based on false, biased or incomplete data. In this paper, we
investigate the effect of data quality issues on the analysis of large
networks. We focus on the global board interlock network, in which nodes
represent firms across the globe, and edges model social ties between firms --
shared board members holding a position at both firms. First, we demonstrate
how we can automatically assess the completeness of a large dataset of 160
million firms, in which data is missing not at random. Second, we present a
novel method to increase the accuracy of the entries in our data. By comparing
the expected and empirical characteristics of the resulting network topology,
we develop a technique that automatically prunes and merges duplicate nodes and
edges. Third, we use a case study of the board interlock network of Sweden to
show how poor quality data results in incorrect network topologies, biased
centrality values and abnormal influence spread under a well-known diffusion
model. Finally, we demonstrate how our data quality assessment methods help
restore the correct network structure, ultimately allowing us to derive
meaningful and correct results from analyzing the network.
