The Data Talents Gap Panel
Big Data and Analytics Innovation Summit
2-3 Nov 2017, London

The Data Talents Gap Panel• Introduction to the Panel– Panel Moderator - Yuri Demchenko (EDISON project coordinator,University of Amsterdam)– Data Science and Analytics competences and skills demand andmarket• Statements by Panel members– Christoph Best (Google)– Joshua Ryan-Saha (The Data Lab)– Robert Monné (The Analytics Academy, ORTEC Data Science)– Sue Daley (techUK, University of Birmingham) – Apologies• Questions and answers: Interactive discussion
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Intro to Panel on Data Talents Gap
What we are going to discuss••••

What does the right talent look like?
What does the right management look like?
Is the economy producing the requisite number & quality of people?
So what are the strategies for finding the right people and the incentives
needed to keep them engaged over time?
• How can we retain the talent we already have?
• How can we upskill those already there and lastly, ensure a pipeline ofcandidates so the area can continue to grow and develop?

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Introduction to the Talents Gap• Recent reports, studies and facts• Existing Initiatives, projects, frameworks• EDISON Data Science Framework (EDSF)• Data Science competences and skills• Essential Data Scientist professional skills: Thinking and Acting like Data
Scientist

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Industry reports on Data Science Analytics and Dataenabled skills demand•

Final Report on European Data Market Study by IDC (Feb 2017)– The EU data market in 2016 estimated EUR 60 Bln (growth 9.5% fromEUR 54.3 Bln in 2015)• Estimated EUR 106 Bln in 2020
– Number of data workers 6.1 mln (2016) - increase 2.6% from 2015
• Estimated EUR 10.4 million in 2020
– Average number of data workers per company 9.5 - increase 4.4%
– Gap between demand and supply estimated 769,000 (2020) or 9.8%

•PwC and BHEF report “Investing in America’s data science andanalytics talent: The case for action” (April 2017)–––•

http://www.bhef.com/publications/investing-americas-data-science-andanalytics-talent2.35 mln postings, 23% Data Scientist, 67% DSA enabled jobs
DSA enabled jobs growing at higher rate than main Data Science jobs

Citing EDISON and EDSF

Burning Glass Technology, IBM, and BHEF report “The Quant
Crunch: How the demand for Data Science Skills is disrupting thejob Market” (April 2017)–––https://public.dhe.ibm.com/common/ssi/ecm/im/en/iml14576usen/IML14576
USEN.PDF
DSA enabled jobs takes 45-58 days to fill: 5 days longer than average
Commonly required work experience 3-5 yrs
Influenced by EDISONBDAI Summit 2017

The Data Talents Gap5PwC&BHEF: Demand for DSA enabled jobs
Demand for business people withanalytics skills, not just datascientists• Of 2.35 million job postings inthe US– 23% Data Scientist– 67% DSA enabled jobs

• Strong demand for managers
and decision makers with Data
Science (data analytics)
skills/understanding– Challenge to deliver actionableknowledge and competences toCEO level managers

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PwC&BHEF: Skills that are tough to findFaster growing jobs require both analyticaland social skills

To be mapped to Competences, Knowledge, Skills and Personal (soft) Skills
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Digital skills gaps density by occupation andtype of digital skills, EU28 (%)
13.9
13.2
13.3

Managers
11.6

Professionals

10.4
12.8
21.8

Technicians

17.4
16.4
17.2

Clerical workers

15.8
14.7
19.5

Sales workers

17.6
22.8
8.4
8.0

Skilled agric workers

14.0
5.6

Building workers

10.9
12.9

Plant machine operators

15.9
16.5
15.3

Elementary occupations

15.3

20.6
17.6

Basic digital skills

Advanced digital skills

Specialist digital skills

ICT for work: Digital skills in the workplace, Digital Single Market, Reports and studies, May 2017https://ec.europa.eu/digital-single-market/en/news/ict-work-digital-skills-workplace

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Workplaces reporting having taken action to tackle digital skill gaps
by type of action undertaken, EU28 (% of workplaces with digitalskill gaps which undertook actions)
On the job training and development programmes
84.4

External training and development programmes
58.3

Changing working practices (e.g.task sharing)
48.8

Reallocating tasks
47.2

Recruiting new staff with needed skills

39.0

Hiring temporary staff with needed skills (e.g.temporaryagency workers)

24.6

Outsourcing of tasks involving ICT use
29.3

Secondment of employees from other workplaces within
the same organisation

13.8

Other
11.2

ICT for work: Digital skills in the workplace, Digital Single Market, Reports and studies, May 2017https://ec.europa.eu/digital-single-market/en/news/ict-work-digital-skills-workplace

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EDISON Data Science Framework (EDSF)

EDISON Framework components

Methodology–––––•

ESDF development based on job market
study, existing practices in academic,
research and industry.

•Review and feedback from the ELG, expert
community, domain experts.

•Input from the champion universities andcommunity of practice.

–CF-DS – Data Science Competence Framework
DS-BoK – Data Science Body of Knowledge
MC-DS – Data Science Model Curriculum
DSP – Data Science Professional profiles
Data Science Taxonomies and Scientific Disciplines
Classification
EOEE - EDISON Online Education Environment

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Data Scientist definition
Based on the definitions by NIST SP1500 – 2015, extended by EDISON•A Data Scientist is a practitioner who has sufficientknowledge in the overlapping regimes of expertise inbusiness needs, domain knowledge, analytical skills,and programming and systems engineering expertise
to manage the end-to-end scientific method process
through each stage in the big data lifecycle till the deliveryof an expected scientific and business value toorganisation or project.

•Core Data Science competences and skills groups–––––•DSDA - Data Science Analytics (including Statistical Analysis, Machine Learning, BusinessAnalytics)
DSENG - Data Science Engineering (including Software and Applications Engineering, Data
Warehousing, Big Data Infrastructure and Tools)
DSDM - Data Management, Data Governance, Stewardship, Curation, Preservation
DSRMP - Research Methods and Project Management
DSDK - Domain Knowledge and Expertise (Subject/Scientific domain related)

Data Science professional skills: Thinking and acting like Data Scientist–Required to successfully develop as a Data Scientist and work in Data Science teams

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Data Science Competence Groups - Research
Data Science Competences include 5groups•••••

Data Science Analytics (DSDA)
Data Science Engineering (DSENG)
Domain Knowledge and Expertise (DSDK)Data Management DSDM
Research Methods Project Management(DSRMP)

Research Methods••••••

Hypothesis Formulation
Design Experiment
Collect DataAnalyse Data
Identify Patterns
Hypothesis Evaluation

Business ProcessManagement•••••
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Operations StrategyPlan
Design & Deploy
Monitor & Control
Improve & Re-design12Identified Data Science Skills/Experience Groups
Skills Type A – Based on knowledge acquired•Group 1: Skills/experience related to competences–––––•

Data Analytics and Machine Learning
Data Management/Curation (including both general data management and scientific data management)
Data Science Engineering (hardware and software) skills
Scientific/Research Methods or Business Process Management
Application/subject domain related (research or business)

Group 2: Mathematics and statistics–Mathematics and Statistics and others

Skills Type B – Base on practical or workplace experience•Group 3: Big Data (Data Science) tools and platforms––––––•

Group 4: Data analytics programming languages and IDE–•

Big Data Analytics platforms
Mathematics & Statistics applications & tools
Databases (SQL and NoSQL)
Data Management and Curation platform
Data and applications visualisation
Cloud based platforms and tools
General and specialized development platforms for data analysis and statistics

Group 5: Soft skills and Workplace skills––

Data Science professional skills: Thinking and Acting like Data Scientist
21st Century Skills: Personal, inter-personal communication, team work, professional network

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Data Science Professional Skills:
Thinking and Acting like Data Scientist
1.
2.
3.
4.
5.
6.
7.
8.

9.
10.
11.
12.
13.
14.

15.

Recognise value of data, work with raw data, exercise good data intuition, use SN and open data
Accept (be ready for) iterative development, know when to stop, comfortable with failure, accept thesymmetry of outcome (both positive and negative results are valuable)
Good sense of metrics, understand importance of the results validation, never stop looking at
individual examples
Ask the right questions
Respect domain/subject matter knowledge in the area of data science
Data driven problem solver and impact-driven mindset
Be aware about power and limitations of the main machine learning and data analytics algorithms
and toolsUnderstand that most of data analytics algorithms are statistics and probability based, so any
answer or solution has some degree of probability and represent an optimal solution for a numbervariables and factors
Recognise what things are important and what things are not important (in data modeling)
Working in agile environment and coordinate with other roles and team members
Work in multi-disciplinary team, ability to communicate with the domain and subject matter experts
Embrace online learning, continuously improve your knowledge, use professional networks andcommunities
Story Telling: Deliver actionable result of your analysis
Attitude: Creativity, curiosity (willingness to challenge status quo), commitment in finding new
knowledge and progress to completion
Ethics and responsible use of data and insight delivered, awareness of dependability (data scientist isa feedback loop in data driven companies)

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21st Century Skills (DARE & BHEF & EDISON)
1.

Critical Thinking: Demonstrating the ability to apply critical thinking skills to solve
problems and make effective decisions
2. Communication: Understanding and communicating ideas
3. Collaboration: Working with other, appreciation of multicultural difference
4. Creativity and Attitude: Deliver high quality work and focus on final result, intitiative,
intellectual risk
5. Planning & Organizing: Planning and prioritizing work to manage time effectively andaccomplish assigned tasks
6. Business Fundamentals: Having fundamental knowledge of the organization and theindustry
7. Customer Focus: Actively look for ways to identify market demands and meet customer
or client needs
8. Working with Tools & Technology: Selecting, using, and maintaining tools andtechnology to facilitate work activity
9. Dynamic (self-) re-skilling: Continuously monitor individual knowledge and skills as
shared responsibility between employer and employee, ability to adopt to changes
10. Professional networking: Involvement and contribution to professional network activities
11. Ethics: Adhere to high ethical and professional norms, responsible use of power data
driven technologies, avoid and disregard un-ethical use of technologies and biased datacollection and presentation

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Data Science Professions Family

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CF-DS and Data Science Professional Profiles

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Individual Competences Benchmarking
Based on EDSF Competence Framework
Individual Education/Training Pathbased on Competence benchmarking• Red polygon indicates the chosen
professional profile: Data Scientist(general)• Green polygon indicates the candidate orpractitioner competences/skills profile• Insufficient competences (gaps) arehighlighted in red– DSDA01 – DSDA06 Data Science Analytics– DSRM01 – DSRM05 Data Science ResearchMethods•Can be use for team skills match marking andorganisational skills management[ref] For DSP Profiles definition and for enumerated
competences refer to EDSF documents CF-DS and DSP
Profiles.

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Building a Data Science Team

Icons used: Credit to [ref] https://www.datacamp.com/community/tutorials/data-science-industry-infographic

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Statements by Panel members• Christoph Best (Google)• Joshua Ryan-Saha (The Data Lab)• Robert Monné (The Analytics Academy, ORTEC DataScience)

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Questions to discuss and to pick up by the Panelor audience• How to tackle digital skills gaps, how to ensure effective collaboration
across stakeholders, and within organisation/company• Who are the key players and how should these cooperate? Look for winwin scenarios.
• What do we have to do together to address digital skills gaps?
• Do you see a role for central/EU level policy or standardisation tofacilitate the digital and data skills development?
• Data Analytics vs Data Management and Governance in companies – arethey connected?
• Training: in-house v. consultants v. certified outsiders?
• Data Science, Data Skills – What will Artificial Intelligence advent willchange?
• How to secure continued engagement of employer sectors with thedeveloping universities network?
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Questions and discussion
Links to EDISON Resources• EDISON project website http://edison-project.eu/•EDISON Data Science Framework Release 1 (EDSF)
http://edison-project.eu/edison-data-science-framework-edsf––––

Data Science Competence Framework
http://edison-project.eu/data-science-competence-framework-cf-ds
Data Science Body of Knowledge
http://edison-project.eu/data-science-body-knowledge-ds-bok
Data Science Model Curriculum
http://edison-project.eu/data-science-model-curriculum-mc-ds
Data Science Professional Profiles
http://edison-project.eu/data-science-professional-profiles-definition-dsp

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Other related links•

Amsterdam School of Data Science––•

Final Report on European Data Market Study by IDC (Feb 2017)–•

http://www.bhef.com/publications/investing-americas-data-science-and-analytics-talentBurning Glass Technology, IBM, and BHEF report “The Quant Crunch: How thedemand for Data Science Skills is disrupting the job Market” (April 2017)––•https://ec.europa.eu/digital-single-market/en/news/final-results-european-data-market-studymeasuring-size-and-trends-eu-data-economy

PwC and BHEF report “Investing in America’s data science and analytics talent:
The case for action” (April 2017)–•https://www.schoolofdatascience.amsterdam/
https://www.schoolofdatascience.amsterdam/education/

http://www.bhef.com/publications/quant-crunch-how-demand-data-science-skills-disrupting-jobmarket
https://public.dhe.ibm.com/common/ssi/ecm/im/en/iml14576usen/IML14576USEN.PDF

Millennials at work: Reshaping the workspace (2016)–https://www.pwc.com/m1/en/services/consulting/documents/millennials-at-work.pdf

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