Using new types of data to measure the digital economy

Juan Mateos-GarciaJuan.mateos-garcia@nesta.org.uk

READIE Research Summit, 21 March 2016

1: What’s the big digital data opportunity?

Some important questions:

-Where it is happening

-How is it happening?

-What are the barriers?

-Do our interventions to support it make a difference?

We want to measure & understand digital

innovation & entrepreneurship to support them better

Why is it hard to do this with official

data?

What is the policy problem?

What can we do about it?

Rear-view of the economy based on existing industrial codes

Data in silos, lacking a relational dimension,

Anonymised data: we care about outliers but learn about averages

Hard to identify new industries, new digital technologies, new clusters

Fragmented, incomplete view of ecosystems

Imprecise targeting across the policy cycle

Use unstructured data to create our own categories

Combine datasets, use social network data

Use public data

The (big, open) data revolution is multiplying the opportunities to generate, communicate and use data insights about digital industries

Example 1: Measuring digital industries poorly covered by industry (SIC) codes

We used web data to identify and map UK games

companies, evidencing the limitations of existing SIC codes and revealing new

clusters.

Example 2: Finding communities

We used data from Meetup.com (a website used to organise networking events to) to identify communities interested in different tech specialisms across the UK.

Example 3: Mapping networks

Bringing it all together: Tech Nation 2016

In Tech Nation we combine web, open and official data to measure

the state of the UK digital economy, its clusters and

its networks.

This allowed us to look at digital tech subsectors, measure digitisation in non-digital industries,

analyse changes in digital salaries and map regional

tech networks...

New data isn’t the silver bullet for digital econ policy

● It has weird biases

● Hard to use it for time series analysis

● Black box problems

● Lacks important variables (e.g. financials)

● Need new skills to collect, analyse and use.

● Policymakers aren’t purely data-driven automatons

But used judiciously, and in combination with other datasets, it can create new information about the digital economy that is relevant for policymakers.

What are we doing next?

-Arloesiadur: An innovation data analytics platform for Welsh Government.

Going from…STATIC INTERACTIVE

DESCRIPTIVE PREDICTIVE

RESEARCH APPLICATION

Project running January 2016-March 2017. We’ll keep you posted about what we find!