This document discusses how new data sources and analytical methods can help address evidence gaps in innovation research and policy. It notes four key features of innovation - novelty, pervasiveness, complexity, and multiple audiences - that are difficult to capture with traditional data sources. The document then outlines how new data sources like web and text data, as well as new analytical methods like machine learning, can help measure innovation in more novel areas, sectors, and complex systems. It acknowledges challenges like ensuring quality, consistency, interpretability and addressing information overload, but emphasizes exploring opportunities through experiments and collaboration.

1. New data for Innovation Policy
Hasan Bakhshi and Juan Mateos-Garcia
SPRU 50th Anniversary Conference,
8th September 2016

2. Data is critical for innovation research
and policy
But what data?
Select &
design
Implement Evaluate
Policy cycle

3. Feature 1: Novelty and change
Innovation = new combinations, disequilibrium
Official data = a snapshot of a stable economy
No IOT, cleantech, 3d printing in official stats.
Rearview of the economy

4. Feature 2: Pervasiveness
Happening in many sectors
Many measures (patents, publications) mainly
relevant for S&T industries: Hidden innovation

5. Feature 3: Complexity
Interdependency, context, history
Econometric modelling : Generalising & averaging
Reductionism

6. Feature 4: Multiple audiences
Innovation system: Many stakeholders
Reports have limited space, biased authors:
Unanswered questions.

7. 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

8. Novelty  Unstructured data
Use web and text data to generate new classifications & track
new activities
Gotcha: Quality and robustness

9. Pervasiveness  New sources
...And measure activity and innovation in less studied sectors
Gotcha: New biases, temporal inconsistency

10. Complexity  Analytics
Use relational datasets and data combinations to characterise systems,
machine learning to identify drivers
Gotcha: Information overload, interpretability, model variance

11. Variety  Open code, data + interactivity
Release data and visualisations to widen reach & enable new analyses
Gotcha: Lot of work (documentation, IP, privacy), use cases?

12. 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! 

13. Thank you
Juan.mateos-garcia@nesta.org.uk
@JMateosGarcia
https://uk.linkedin.com/in/juanmateosgarcia