model workers, juan mateos-garcia-nesta
DESCRIPTION
DBi Conversion Thursday July Juan Mateos-Garcia is an Economics Research Fellow at the Creative and Digital Economy Team at Nesta, the UK's Innovation Foundation. Other than boasting numerous academic letters after his name, he has been working on a programme of research measuring how UK companies are using data for innovation, and what this means for managers, education and policy. He will be spilling the beans on his findings with 45 companies to outline what kinds of skills are needed in a modern data-drive organisation, what practices and strategies companies use to find such talent and the role of education in helping to keep this supply going.TRANSCRIPT
@JMateosGarcia, Nesta, 17 July 2014
2 of 26
The UK’s innovation foundation. An
independent charity with a mission to
help people and organisations bring
great ideas to life.
Prologue: Understanding the Datavores
1. Rise of the Datavores 2. Inside the Datavores …
• A three-year programme of research aiming to
generate robust, independent evidence to inform
policy and practice enabling UK businesses to create
value from their data
3
Only 18% of
UK companies
commercially
active online =
data-driven
Data-driven
company 8%
more
productive
than the
average
The human face of the data revolution
0
200
400
600
800
1000
1200
1400
1600
1800
2005 2012
Inte
rne
t D
ata
(20
05
=10
0)
“Big data will produce
progress, eventually.
How quickly it does, and
whether we regress in
the meantime, will
depend on us”
4
Mythical creatures
5
Model Workers
Audience Questions
Everyone What are the skills of productive data
analysts?
Educators Is the education system producing
enough of them?
Managers How can managers organise their data
talent to create value?
We interviewed managers of
data analysis teams, HR
managers, data scientists and
CTOs. We targeted companies
where data plays an important
role in production and/or
operation.
6
Data landscape: Four Data modes
Variety
Vo
lum
e
Business
Intelligence
(Analytics)
Data intensive science
(Com bio, epidemiology)
Web Analytics
(digital marketing)
Big data (data
scientists)
7
Data landscape: Four Data modes
8
One mode to rule them all?
Variety
Vo
lum
e
Business
Intelligence
(Analytics)
Data intensive science
(Com bio, epidemiology)
Web Analytics
(digital marketing)
Big data (data
scientists)
9
Supply (better tech
and more data) &
demand (competition)
driving firms into the
‘big data corner’
The perfect analyst
Analysis +
computing
Domain
knowledge +
Business savvy
Storytelling +
team-working
Creativity +
curiosity
Th
e p
rofile
mo
st o
f o
ur
resp
on
de
nts
lo
ok fo
r
4 in 5
bizreport
difficulties
recruiting
Talent lacks
skills +
experience
Not enough
talent
Talent without
the right mix of
skills
Internal capacity
issues
10
Future trends…
L
w
SupplyDemand
Better toolsEducation
adapts
More sectors
become data-
driven
Better tools lower
barriers to entry
for SMEs
Education
adapts too
slowly…
? In the short-term, data
talent crunch + some
instances of offshoring
11
How are the companies we interviewed
managing this situation?
Good p
ractie
sfo
r the
managem
ent o
f cre
ativ
e ta
lent +
innovativ
e w
ork
12
Policy implicationst
Develop
workforce skills
Build up the data
scientist
profession
Ensure access to
overseas talent
Better university-
industry
communication
Promote inter-
disciplinarity
Improve teaching
of math + stats in
school
Change
perceptions of
data jobs as
uncreative and
boring!
13
Next stepst
Model workers: Final report
Autumn
Analysis of new data
including firm survey
(N=400) and data about
destinations of graduates
from quant subjects.
Policy development with
government
Business dissemination
Something more practical?
?
14
