The document summarizes the goals, process, impacts, and lessons from Tech Nation 2016, a project analyzing the UK's digital tech economy. It aimed to produce timely industry-relevant analysis using web and official economic data to map digital tech businesses and consider digitization across sectors. The analysis team collected a variety of data sources and conducted interviews. The results received significant media coverage and attention. Key lessons were the value of combining different data sources and prioritizing analysis based on domain knowledge.

1. Looking under the hood of Tech
Nation 2016: process, findings and
lessons
Juan Mateos-Garcia
Juan.mateos-garcia@nesta.org.uk
READIE Research Summit, 22 March 2016

2. Goals & Structure
• Goals of the session:
– Having a practical opportunity to think about how to
combine data opportunities and policy needs
– Talk to each other!
– Create the seed of an amazing project that will change
the face of the digital economy in Europe
• I’ll get you in the mood with a little talk about Tech Nation
2016, a policy-relevant project to understand the digital tech
economy in the UK:
– Origin
– Process
– Impacts
– Lessons

3. Origin
2010 Feb 2016Feb 2015~2013
GOAL: Produce a rigorous, timely map of the UK digital tech industries that speaks
to a policy and industry audience. Nesta = Analytical partner.

4. Design features
GOAL CHALLENGE SOLUTION
Timely, industry
relevant analysis of the
digital tech industries
SIC codes aren’t
sufficient to do this
Use web data to
identify digital tech
businesses
Policy relevant analysis
including economic
measurements
Public/open data
doesn’t include econ
measures
Use official micro-data and
a transparent
methodology to select
SICs
Consider digitisation of
non-digital sectors
Few datasets measure
business digitisation
Select datasets with
combinations of
sector/digital capability
Measure skills base in
local ecosystems
Skills supply is both
formal and informal
Use web data to
measure local skills and
networks

5. Data and research team
Nesta team included Hasan Bakhshi (lead), Juan Mateos-Garcia (quant analysis),
Madeleine Gabriel (qual analysis), Cath Sleeman (interactive data viz), Matthew
Williams (external, supported work with GI, GitHub, Meetup data).
Analytical work August 2015-January 2016 -> VERY TIGHT.
• This includes ONS
(BSD, ABS, APS) data
some of which was
obtained by Frontier
Economics.
• We also collected
HESA, RCUK data.
• TCUK also ran a
business survey
• 40 qual interviews.

6. Briefly, on definitions
Definition of digital tech based
on previous Nesta analysis,
dynamic mapping method:
• Based on intensity of digital
tech occupations in different
industries.
• Allows distinguishing
between digital tech
industries and jobs, and look
at digitisation in non-digital.

9. Impacts
Big media
coverage
• 39,000 visits
• 8 national pieces
(Guardian, FT
etc.)
• 31 regional
pieces, 56 trade
pieces
• 5 broadcasts.

10. Impacts (2)

11. Lessons & next steps
• It’s not about big data OR official data OR surveys. It can (and
probably should) be all.
• It’s easy to be overwhelmed by data. Use domain knowledge to
identify interesting questions to prioritise (working with TCUK was
helpful for this).
• Give yourself enough time to analyse the data.
• Segment outputs (headlines vs. technically complex info).
What’s next?
• We are now moving from static to interactive, and descriptive to
predictive.
• Considering policy implications
• …And starting to think about next year’s Tech Nation! 

12. Follow-up!
• Juan.mateos-garcia@nesta.org.uk
• @JMateosGarcia
• https://www.linkedin.com/in/juanmateosgarcia