Model workers 9th july2014

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  • Mention Survey here?
  • This still has the McDonalds figure on it
  • This still has the McDonalds figure on it
  • Bullet point numbering has gone wrong.
    Need to be clear in presentation who the policy implications are for…
  • Bullet numbering
  • 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

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