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New data for Innovation Policy
Hasan Bakhshi and Juan Mateos-GarciaSPRU 50th Anniversary Conference,
8th September 2016
Data is critical for innovation research and policy
But what data?
Select & design Implement Evaluate
Policy cycle
Feature 1: Novelty and change
Innovation = new combinations, disequilibriumOfficial data = a snapshot of a stable economyNo IOT, cleantech, 3d printing in official stats.
Rearview of the economy
Feature 2: Pervasiveness
Happening in many sectorsMany measures (patents, publications) mainly
relevant for S&T industries: Hidden innovation
Feature 3: Complexity
Interdependency, context, historyEconometric modelling : Generalising & averaging
Reductionism
Feature 4: Multiple audiences
Innovation system: Many stakeholdersReports have limited space, biased authors:
Unanswered questions.
Evidence gaps for research and policy. Can innovation analytics help?
• New data sources (web, open) & combinations; • New analytical methods• New ways to present data
Emerging space: Illustrate opportunities with Nesta experience
Novelty Unstructured data
Use web and text data to generate new classifications & track new activities
Gotcha: Quality and robustness
Pervasiveness New sources
...And measure activity and innovation in less studied sectorsGotcha: New biases, temporal inconsistency
Complexity Analytics
Use relational datasets and data combinations to characterise systems, machine learning to identify drivers
Gotcha: Information overload, interpretability, model variance
Variety Open code, data + interactivity
Release data and visualisations to widen reach & enable new analysesGotcha: Lot of work (documentation, IP, privacy), use cases?
Uncertainty is addressed through experiments
• We are exploring all these opportunities in a collaboration with Welsh Government: launch March 2017.
• New data has big opportunities but also risks: address them through reproducibility and collaboration.
• New data and methods are unlikely to automate innovation policymakers.
• Phew!
