Page 122 - AC/E's Digital Culture Annual Report 2015

Cultural business models on the Internet122As previous Nesta research59 on the impact of analytics on organisational performance has shown, collecting data on its own is not enough to produce a benefit. It is also necessary to build up the right skills and capabilities, transform processes and develop a culture of data-driven decision-making. Accomplishing this in the Arts and Cultural sector presents some challenges that I shall overview in turn, identifying what the obstacles are, and their implications for practitioners and funders.The first challenge is how to access skills. As I mentioned in section 2, people with the skills to create value from data – data scientists – are hard to find. Moreover, the Arts and Cultural sector is not perceived as a destination for quantitative data analysts: young people studying advanced statistics, social network analysis or Artificial Intelligence in universities do not realise that they could have a careerin the Arts and Cultural sector, while those passionate about the Arts and Culture arenot aware that analytics skills would be very beneficial for their future career in the sector, and for the organisations that employ them. Addressing these misperceptions requires creating more spaces for disciplinary crossover at schools, and improving the visibility of the Arts and Cultural sector among graduatesfrom data disciplines.60 In doing this, Arts and Cultural organisations can follow the example of other organisations in the creative industries – including TV broadcasters and advertising agencies – who are collaborating with universi- ties61 to spot young analytical talent, and using the unique characteristics of their organisations (creative environments, interesting datasetsand the opportunity to have an impact in a“green-fields” site) as “magnets” for data scien- tists who are, in many ways, creative workers themselves.62Another potential strategy that Arts and Cultural organisations can adopt to access analytical talent is using broker organisations like DataKind,63 a global charity that connects data scientists with NGOs. DataKind assembles data science teams who work pro bono withthe NGO, exploring how the data it alreadyhas (or that it could collect) can help achieveits goals. An example of how this has beendone in the Arts and Cultural sector is the Cultural Data Project,64 where DataKind’sdata scientists explored a large dataset of the activities and financial performance of 11,000 US Arts and Cultural organisations in orderto understand what behaviours were linked to financial sustainability in the sector. The analysis revealed distinct “clusters” of performancethat the Cultural Data Project is now using to design better support services and peer-support activities for its members.A potential strategy that cultural organisations can adopt to access analytical talent is using broker organisations.Other options that the sector can use to connect its datasets with the data science community include Open data challenges65 where Artsand Cultural organisations open up interesting datasets for external analyst to explore (offering a prize for those who do the most interesting work), and “data scientist in residence” schemesUsing data to create value in the arts and cultural sector