model workers 9th july2014
TRANSCRIPT
Hasan Bakhshi, Juan Mateos-Garcia and Andrew Whitby, Nesta P&R9 July 2014
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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
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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
…
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Datavores in the minority; organised differently
Datavores Dataphobes0%
10%
20%
30%
40%
50%
Decisions based on experience +
intuitionDecisions based on data
and analysis
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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)
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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?
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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.
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Data landscape: Four Data modes
Variety
Vol
um
e
Only 1 in 4 of the companies in our
sample in this data mode Business
Intelligence(Analytics)
Data intensive science(Com bio, epidemiology)
Web Analytics(digital marketing)
Big data (data scientists)
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One mode to rule them all?
Supply (better tech and more data) & demand (competition) driving
firms into the ‘big data corner’
Variety
Vol
um
e
Big data (data scientists)
Business Intelligence(Analytics)
Web Analytics(digital marketing)
Data intensive science(Com bio, epidemiology)
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The perfect analyst
Analysis + computing
Domain knowledge + Business savvy
Storytelling + team-working
Creativity + curiosity
The
pro
file
mos
t of
our
resp
onde
nts
look
for
4 in 5firms report
difficulties recruiting
Talent lacks skills + experience
Not enough talent
Talent without the right mix of skills
Internal capacity issues
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Future trends…
L
w
SupplyDemand
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
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Policy implications: skills
1. Develop workforce skills• Upskill existing professions• Make this part of cluster development
programmes?
2. Build up the data analyst profession• Develop training and certification
standards?• Raise awareness and share good
practice
3. Ensure access to overseas talent• Including students & entrepreneurs
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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
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Policy implications: perceptions
Change perceptions of data jobs as uncreative and boring!
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Implications for managers
Data talent is often innovative and creative. This is a source of opportunities (innovation) and management challenges
(motivation, organisation, predictability).
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The companies we interviewed are… going out to where the talent is
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…bypassing the absence of ‘unicorns’ by building strong teams
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…being careful where they place their talent
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…harnessing the creativity of data analysts, but also managing them carefully
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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.