Big data is an evolving term used to describe the variety, volume and velocity of large amounts of structured and unstructured data. It can offer useful insights at both operational and strategic levels, thereby helping organisations to move forward in times of rapid change and uncertainty. However, there are challenges in terms of how best to capture, store and make sense of data. Many cultural arts organisations generate value through the relationships they create and the networks they sustain, but far too often this data is not clearly articulated or evidenced to leverage insight, support and business opportunities. The ArtsAPI project aimed to understand the connections that underpin the 'relational value' within the arts sector. The R&D project resulted in the development of a proof of concept business modelling and analytic tool to enable arts organisations to generate new insights through data capture, visualisation and analysis. The numerical/analytical technique of Social Network Analysis (SNA) was used to visually map and analyse network structures and relationships found within and across the extended boundaries of five cultural arts organisations located in the UK. Based on the 'blue print' from the SNA research, seven scenario-based insights were generated that offered impact measures for debates around evidencing forms of cultural value. These scenarios were later mapped onto a semantic ontology to create a 'SNA lite' web-based tool. In the paper to be reported here, we will set the context and background of the project, briefly describe the research methodology and the outcomes that influenced the development of the ArtsAPI tool.