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Highlights • Poor data quality impairs the ability to obtain actionable insights from networks. • A method to measure completeness based on metadata is detailed. • A method to merge duplicate nodes based on network attributes is detailed. • The effectiveness of the method is assessed using the Swedish corporate network. Abstract Nowadays, social network data of ever increasing size is gathered, stored and analyzed by researchers from a range of disciplines. This data 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 social 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 distorted network topologies, incorrect community division, biased centrality values and abnormal influence spread under a well-known diffusion model. Finally, we demonstrate how the proposed data quality assessment methods help restore the network structure, ultimately allowing us to derive meaningful and correct results from the analysis of the network. [ABSTRACT FROM AUTHOR]