Industrial policymakers and researchers have become increasingly frustrated with traditional data sources when it comes to measuring, analysing and informing policies to support new and innovative sectors. Such datasets, which include business surveys, R&D spending and volumes of scientific and technological outputs, such as academic publications and patents:
- Are ill-suited for the analysis of businesses and jobs in new industries.
- Fail to capture networks of collaboration which we know are critical for innovative success.
- May involve substantial time lags between data collection and publication, making it hard to capture real-time development of emerging clusters and industries.
- Anonymise business information in a way that makes it hard to target interventions and match data across sources (e.g. in order to analyse the impact of a policy intervention on a given business).
This makes it harder to determine industrial policy priorities and design, implement and evaluate policy interventions, and has contributed to a situation where there are high levels of uncertainty about what works and what doesn’t.
The data revolution is creating new opportunities to address some of these challenges. We can use social media data to map innovation networks, and public and open datasets to obtain a more comprehensive view of local innovation systems and the businesses that operate in them.
Boosting and combining datasets from traditional sources with novel sources gives policymakers intelligence which is more detailed and timely, and information about phenomena that couldn’t be measured before (such as informal innovation networks in industries that don’t patent or publish).
Nesta’s work with creative clusters illustrates the expansion in online data sources and analytical toolkits that are available to policymakers. As the potential value of data has increased it has become more important to make the data available to users, using tools like interactive data visualisation and dashboards.