Hasan Bakshi, NESTA , discussed the questions in relation to his experience as an economist attempting to develop robust measures for innovation, value creation and, recently, cultural or ‘intrinsic value’ through methods such as contingent valuation. With the proliferation of big data, from diverse sources, his concerns include the question of data standards and how we might understand these in relation to bigger data. One recommendation for the arts might be to involve more data scientists, who arts organisations can work with to ensure the quality of the data and its analysis. He gave an example from recent research of ‘machine learning’ through exploring massive datasets, which have helped to identify video games as a growing form of cultural participation – in contrast to other forms of participation research that miss changes in behaviour, particularly associated with new or emergent cultural forms. One particular method which needs updating is the survey model: instead, Hasan argues, big data use cases can reveal new patterns of behaviour and experience, and even redefine what we understand as culture.
Cimeon Ellerton, The Audience Agency, talked of his experience in developing audience data through services such as Audience Finder. For him, the priorities remain the standardisation of data and also the need to aggregate data whilst encouraging arts organisations to be open. This needs to be a collective effort, involving all organisations, so that the smaller arts companies with less capacity for audience evaluation and research could benefit from scalable economies. Leadership by the sector in using data and evidence is also key: “whoever owns the story gets the funding”.
Alison Clark, Arts Council England North, said she feels arts organisations continue to have a fear of social media and of sharing. There isa sense that the more organisations value data, the less they are likely to share data. Arts Council England has embraced these issues, by developing a new data strategy and encouraging the use of data scientists to bring new skills and practices to enrich big data analysis and evaluation. Although this may bring more opportunities for ‘data-driven decision making’, at the same time it brings other anxieties. For example, evidence-based funding schemes, such as Creative People and Places, which is only for places with identified low levels of engagement established by Active People survey data, mean places and programmes, which other types of knowledge and analysis would reward, miss out. There is a concern that policy for arts and culture becomes somehow un-human – that there is an automation of creativity, and policy formation can be algorithmic. A solution to this lies to some extent in a collective embrace of big data, where everyone joins in: for Alison, this means leadership within the sector to create peer pressure and encourage sharing of resources and practice.
Nick Merriman, Manchester Museum, speaking from the ‘user’ perspective, discussed how initiatives like Culture Counts can provide better accountability for public funding, since by aggregating stakeholder perspectives they provide a credible means of understanding quality, not just of arts and cultural experiences, but also other activities which involve exchange between publicly funded services and the public, for example, science engagement. He spoke of the added value of the process of developing shared metrics through collaboration, although he also cautioned it is early days still for the embedding of this approach in the sector, particularly in terms of bringing together the data from Culture Counts with social media data.
Discussion focused on the barriers to data sharing: unlike the commercial sector, for the publicly funded arts there is more opportunity – and need – for policy to intervene in this space and to encourage organisations to work collaboratively to increase their analytical capacity. The discussion suggested that concerns for technologically-determined funding decisions are balanced by the opportunities to create better transparency and accountability. This, it was felt, is facilitated viaa richer conversation based on a range of knowledge which includes the generative potential of big data rather than relying on single source or set of measures; to achieve thisfurther scientific endeavour is needed to develop the data standards, metrics and methodologies to measure and demonstrate public value.
The arts may feel they are playing catch-up with other sectors in this agenda, and whilst one wonders if the rhetoric of evidence-based policy is simply replaced by the rhetoric of big data within data-driven decision-making, with the added ‘wow factor’ of algorithmic policy-making, there is clearly an appetite to work together to understand and harness the potential of big data, to adapt and use its social properties, especially when led by the sector rather than imposed top down by policy.
Bailey, J and Richardson, L (2010) Meaningful measurement: a literature review and Australian and British case studies of arts organizations conducting “artistic self-assessment” in Cultural Trends Vol. 19, No. 4, December 2010, 291–306.