Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Hasan Bakhshi, Juan Mateos-Garcia and Andrew Whitby, Nesta P&R
9 July 2014
1: Understanding the Datavores
1. Rise of the
Datavores
2. Inside the
Datavores
3. Skills of the
Datavores
…
• A three-yea...
Rise of the Datavores
Published November 2012: Survey of 500
UK companies commercially active online
Data
Insight
Action
I...
Datavores in the minority; organised differently
0%
10%
20%
30%
40%
50%
Datavores Dataphobes
Decisions based
on experience...
Inside the Datavores
1. Rise of the
Datavores
2. Inside the
Datavores
3. Skills of the
Datavores
…
Looking at the
link bet...
2. Skills of the Datavores
The US will have a shortfall of
‘deep data talent’ of up to
190,000 by 2018.
McKinsey, 2011
The...
Model Workers
Audience Questions
Everyone What are the skills of productive data
analysts?
Educators Is the education syst...
Data landscape: Four Data modes
Variety
Volume
Only 1 in 4 of the
companies in our
sample in this data
modeBusiness
Intell...
One mode to rule them all?
Supply (better tech and
more data) & demand
(competition) driving
firms into the ‘big data
corn...
The perfect analyst
Analysis +
computing
Domain
knowledge +
Business savvy
Storytelling +
team-working
Creativity +
curios...
Future trends…
L
w
Supply
Demand
Better tools Education adapts
More sectors
become data-driven
Better tools lower
barriers...
Policy implications: skills
1. Develop workforce skills
• Upskill existing professions
• Make this part of cluster develop...
Policy implications: education
1. Better university-industry
communication
• Sector skills councils
communicate, universit...
Policy implications: perceptions
Change perceptions of data
jobs as uncreative and boring!
14
Implications for managers
Data talent is often innovative and creative. This is a source
of opportunities (innovation) and...
The companies we interviewed are… going out
to where the talent is
16
…bypassing the absence of ‘unicorns’ by
building strong teams
17
…being careful where they place their talent
18
…harnessing the creativity of data analysts, but
also managing them carefully
19
3. Conclusions
1. Big data companies are in minority, but everyone looking
for talent with data scientist profile
2. Data ...
THANK YOU
Hasan.Bakhshi@nesta.org.uk
@hasanbakhshi
Juan.Mateos-Garcia@nesta.org.uk
@JMateosGarcia
21
Upcoming SlideShare
Loading in …5
×

Model workers 9th july2014

1,716 views

Published on

Published in: Technology, Business
  • Be the first to comment

Model workers 9th july2014

  1. 1. Hasan Bakhshi, Juan Mateos-Garcia and Andrew Whitby, Nesta P&R 9 July 2014
  2. 2. 1: Understanding the Datavores 1. Rise of the Datavores 2. Inside the Datavores 3. Skills of the Datavores … • A three-year programme of research • Aim: to generate robust, independent evidence to inform policy and practice enabling UK businesses to create value from their data • Research examines business data practices, effect on performance, and skills implications 2
  3. 3. Rise of the Datavores Published November 2012: Survey of 500 UK companies commercially active online Data Insight Action Impact Collection? Analysis? Use? 1. Rise of the Datavores 2. Inside the Datavores 3. Skills of the Datavores … 3
  4. 4. Datavores in the minority; organised differently 0% 10% 20% 30% 40% 50% Datavores Dataphobes Decisions based on experience + intuition Decisions based on data and analysis 4
  5. 5. Inside the Datavores 1. Rise of the Datavores 2. Inside the Datavores 3. Skills of the Datavores … Looking at the link between data activity and productivity and profitability 16% more data-active = 8% more productive Analysis has the highest impact on productivity (+11%) and EBITDA (+3,180 per employee) Positive synergy between employee empowerment and data activity (4x boost) 5
  6. 6. 2. Skills of the Datavores The US will have a shortfall of ‘deep data talent’ of up to 190,000 by 2018. McKinsey, 2011 The sexy job in the next ten years will be statisticians. Hal Varian Going from technology and data requires the right skills… but what are those skills? Data scientists: a new occupation? a new capability? A rebranding? What does this mean for educators, policymakers and managers? 6
  7. 7. 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. 7
  8. 8. Data landscape: Four Data modes Variety Volume Only 1 in 4 of the companies in our sample in this data modeBusiness Intelligence (Analytics) Data intensive science (Com bio, epidemiology) Web Analytics (digital marketing) Big data (data scientists) 8
  9. 9. One mode to rule them all? Supply (better tech and more data) & demand (competition) driving firms into the ‘big data corner’ Variety Volume Big data (data scientists) 9 Business Intelligence (Analytics) Web Analytics (digital marketing) Data intensive science (Com bio, epidemiology)
  10. 10. The perfect analyst Analysis + computing Domain knowledge + Business savvy Storytelling + team-working Creativity + curiosity Theprofilemostofourrespondentslookfor 4 in 5 firms report difficulties recruiting Talent lacks skills + experience Not enough talent Talent without the right mix of skills Internal capacity issues 10
  11. 11. Future trends… L w Supply Demand Better tools Education 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
  12. 12. Policy implications: skills 1. Develop workforce skills • Upskill existing professions • Make this part of cluster development programmes? 1. Build up the data analyst profession • Develop training and certification standards? • Raise awareness and share good practice 1. Ensure access to overseas talent • Including students & entrepreneurs 12
  13. 13. Policy implications: education 1. Better university-industry communication • Sector skills councils communicate, universities innovate, NCUB broker links? • CDEC, Imperial College data institute 2. Promote inter-disciplinarity 3. Improve teaching of math + stats in schools…and after schools 13
  14. 14. Policy implications: perceptions Change perceptions of data jobs as uncreative and boring! 14
  15. 15. Implications for managers Data talent is often innovative and creative. This is a source of opportunities (innovation) and management challenges (motivation, organisation, predictability). 15
  16. 16. The companies we interviewed are… going out to where the talent is 16
  17. 17. …bypassing the absence of ‘unicorns’ by building strong teams 17
  18. 18. …being careful where they place their talent 18
  19. 19. …harnessing the creativity of data analysts, but also managing them carefully 19
  20. 20. 3. Conclusions 1. Big data companies are in minority, but everyone looking for talent with data scientist profile 2. Data analysis is creative work -> good for innovation, but management (and education) challenges 3. Blockages in data talent pipeline echo situation with coding. What can we learn from Next Gen campaign? 4. Autumn 2014: Next report based on new skills survey + HESA data. 20
  21. 21. THANK YOU Hasan.Bakhshi@nesta.org.uk @hasanbakhshi Juan.Mateos-Garcia@nesta.org.uk @JMateosGarcia 21

×