Dr Caitlin McDonald, University of Edinburgh
Creative Informatics – part of the Creative Industries Clusters programme – is an R&D programme based in Edinburgh, which aims to explore and develop data-driven innovation in the creative industries. We are partnering with CRAIC at Loughborough London and The Data City to explore important questions about economic data collected, stored, processed and analysed on the creative industries. Our core questions are: who does this data belong to? And how can that data be collected, utilised and presented in a way which assists the businesses (who have provided the data)?
The way we collect, store, process and analyse data on the creative industries (and arguably beyond) does not facilitate creative research and development. To achieve this goal, data needs to be collected, analysed and shared differently. Though many prior research programmes have attempted to address these problems, innovation in evaluation methods has been relatively slow to take hold. Experimenting with novel methods for measuring innovation and industrial impact, moving beyond traditional economic classifications like SIC and SOC codes, this project will provide new recommendations for the ethical management and sharing of data for the creative industries to achieve better, more impactful creative industries outcomes. Creating robust, trusted, novel data standards for collecting, processing, analysing and evaluating innovation data would facilitate better regional and cross-sector comparisons for innovation both within the creative industries and beyond.
Throughout the Creative Informatics programme, we have sought to understand not only how the creative industries can derive benefit from data-driven technologies, but also the challenges of accessing and using data within the industry. These challenges face creative industry participants of all scales, but are especially acute for small businesses, freelancers and individual practitioners. These practitioners may have many overlapping economic identities that sit outside the tidy framings of current data classification systems pertaining to the industry. These complexities cause challenges for collecting, assessing, and reporting effectively on these creative practitioners’ economic realities. Consequently, these practitioners experience lost attribution of their economic value, lost opportunities for collaboration and funding, and lost economic safeguards in the form of furlough and other mitigation initiatives in times of economic upheaval. Further, the benefits of data collection and sharing are often at the aggregate level, often for the benefit of external decision makers like policy officials and investors. While some individuals and organisations within the creative industries are swift to reap the rewards of the same data analysis used by these bodies, many are unaware of or unable to unlock the benefits of these data sets – or sometimes even actively harmed by the data collected about them, or the lack of it, with little recourse to understand what these data sets are, how they are being used and to rectify their shortcomings.
Challenges in fully representing the economic and public value impact of the creative industries have long been recognized, with an extensive body of literature on different methods for defining and assessing value. Without reproducing the whole field here, a few key theoretical groundings are especially relevant for the research we are starting now. Susan Galloway and Stewart Dunlop critique the very epistemological foundations of the “creative industries” in the first place, arguing that such a concept erodes the value of culture for the public good. Their 2007 critique did not stop the UK’s Department for Culture, Media and Sport from defining the creative industries as an analytical unit in a 1998 template subsequently adopted by several other nations wanting to assess the impact of this cluster of related industries on the economy as a whole. Jason Potts and Stuart Cunningham propose four conceptual models for the creative industries, one of which is particularly relevant to our work in Horizon 5. Rather than treating the creative industries as a public good, a competitive industry the same as any other, or a special driver of growth which percolates out into other sectors, Potts and Cunningham propose that the creative industries act as a higher-order system which coordinates innovation, novelty and change across multiple sectors: “…this is the same model as proposed for the effect of science, education and technology in the national systems of innovation approach. The creative industries, in this view, originate and coordinate change in the knowledge base of the economy. In consequence they have crucial, not marginal, policy significance.” Each of these four models has clear implications for setting public policy strategy, with vastly different choices to be made by prioritising the creative industries as a radical catalyst for change across the whole economy compared to a funded public good that cannot self-fund through competitive means.
Recent research has begun exploring the value of experimenting with automated forms of data capture and assessment for economic activity in the creative industries: in 2018 the Creative Research and Innovation Centre at Loughborough commissioned BOP Consulting and SQW to analyze data on cultural investment, employment, and R&D activity for deeper insights about the place-based effects of cultural networks in the UK. The insights from these projects suggest directions for future policy, investment strategies, and economic analytical methods for the creative industries, a thread further developed by the Creative Industries Policy and Evidence Centre reporting on geographically mapping R&D in the creative industries across the UK. Picking up on Potts and Cunningham’s theoretical framework, Hasan Bakhshi’s research proposing alternative frameworks to Standard Industrial Classification (SIC) and Standard Occupational Classification (SOC) codes suggests a route towards dynamic industrial classifications which more accurately reflect the dynamic nature of a complex system acting as a catalyst for change throughout the economy, enabling different types of policy decision-making than more traditional measures.
While Bakhshi’s work above and related experiments by Nesta and the Creative Industries Policy and Evidence Centre have largely been one-off experiments, at least one organization is putting these principles for novel methods for measuring innovation and industrial impact, moving beyond SIC and SOC codes, into practice in a commercial setting. The Data City has built on prior work with ODI Leeds that scraped business information data from the web. It has combined that data with other sources to develop a tech innovation indexin an attempt to replace the need for SIC and SOC codes across all industry sectors, not just the tech industry. Challenges remain, such as accuracy over time in a changing business environment, data representation across sectors that may rely more on local community knowledge than web presence, and disproportionate benefits or harms for some subsets of the population arising from how their data is captured, analysed and used. These are some of the questions we hope to address in contributing to this body of literature on economic analysis of the creative industries.
Building on the core questions above, this project will work with stakeholder groups, including creative practitioners currently working in the creative industries, policymakers who collect econometric data to drive policy decisions about the sector, trade bodies who collect data on practitioners in order to represent the interests of their constituents to policymakers and funders, researchers like ourselves who collect data while trying to make sense of the big picture, and more. Working towards an understanding of our two core questions above, we’ll dive into areas like:
- What challenges exist for capturing economic data to support delivery of public value impact data through research and innovation in the creative sector? This could include but is not limited to questions about data sparsity, data standardisation, duplication in different but overlapping data sets, etc.
- How might creative practitioners, trade bodies and policy makers move beyond data for monitoring and instead use data for continuous learning: using data in real-time or short cycles of assessment and response to make responsive and adaptive funding changes that impact business behaviour?
- What ethical issues (eg. replication or exacerbation of structural inequalities for marginalized groups, privacy concerns, data ownership, onerousness of data capture, accessibility and usefulness of analyses) exist with respect to current methods of representing economic and public value impact data in the creative sector? What new ethical challenges may arise from changing existing methods?
- What recourse do creative industry practitioners have for challenge and redress wrong, missing, or unhelpful data in data sets that are widely used for decision-making about the creative industries by policymakers and funders?
- What additional or different data would creative practitioners want to be used about them for assessing sector impact? What data, or different methods of collecting data, could support innovation while minimizing potential harms such as underrepresentation, disproportionate administrative burden, replication of existing societal harms, etc?
By exploring these questions, we intend to create a richer understanding of the challenges practitioners and businesses face with regard to current economic data collection and analysis methods for the creative industries. In practical terms, we hope to produce:
- An analysis of the ethical limitations of current economic data collection methods for the creative industries, and how any proposed changes to those methods may change ethical considerations for sector data analysis.
- New tools, or prototype plans, which enhance the data-informed decisions of policymakers and investors to better represent creative practitioners’ own interpretations of what data matters about them for making economic decisions about the sector.
- New tools, or prototype plans, which empower businesses and individual practitioners within the creative industries to understand what datasets policymakers use to make strategic decisions and how their work contributes to the broader economic picture for the creative industries. (This and the previous outcome could be two features of the same tool.)
- Recommendations for further research, future policy and investment strategies, and future changes to analytical methods for assessing impact of the creative industries.
We intend to present outcomes of this research in formats that are useful for the academic community, policymakers and trade bodies, and of course individuals and businesses working within the creative industries in early 2023.
For further information about the project, please contact lead researchers Caitlin McDonald (Caitlin.firstname.lastname@example.org.) or Jennie Jordan (email@example.com).