Model workers 9th july2014

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?




…

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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




…

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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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.

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THANK YOU

[email protected]@hasanbakhshi

[email protected]@JMateosGarcia

mailto:[email protected]




Model workers 9th july2014

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Transcript of Model workers 9th july2014