1. OpenDataMonitor
Horizon 2020
Coordination and Support Action
GARRI-3-2014 Scientific Information in the Digital Age: Text and Data Mining (TDM)
Project number: 665940
Skills and Education: The Need for Strategic Thinking
FutureTDM
Reducing Barriers and Increasing Uptake of Text and Data Mining for Research Environments
using a Collaborative Knowledge and Open Information Approach
Kiera McNeice
FutureTDM Symposium, Salzburg
June 13th, 2017

2. Barriers to TDM
Split into four categories:
▪ Economics and incentives
▪ Legal and policy
▪ Technical and infrastructure
▪ Education and skills
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3. TDM Skills and Education
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4. Skills in use of TDM tools
Barriers to entry still high
▪ Use of TDM tools requires programming knowledge
Skills gaps between TDM experts and domain experts
▪ 64% of Chief Data Officers come from IT/data backgrounds5
Skills gaps between academia and industry
▪ Graduates’ skillsets “do not match TDM needs”
Hiring experts can be prohibitively expensive
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5. Dawn of the CDO

5. FutureTDM recommendations
▪ Ensure everyone working with data has access to education,
information, and experts to consult about TDM
▪ Universities and libraries establish information hubs where
researchers and citizen scientists can access TDM training,
resources, and advice
▪ Ensure education prepares TDM practitioners for the diversity of
TDM tools and applications
▪ Promote exchanges of skills and knowledge between companies
and universities
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6. Broader Skills and Awareness
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7. Education, skills, awareness
Broader skills and awareness needed
▪ Awareness of benefits of TDM
▪ Basic data literacy across all sectors
▪ Data analytical skills
▪ TDM research and development
▪ Turning TDM results into business insights
“More than ten million adults in England lack the basic
digital skills they need.”1
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1. Department for Culture, Media & Sport, The Rt Hon Karen Bradley MP

8. Why support TDM skills?
“Data science is the new IT.”
▪ Data analytics skills will be crucial to the next generation of
researchers
▪ TDM can make research more effective and efficient
▪ Potential value to all research fields and sectors of the economy
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9. TDM is relevant to all sectors
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10. In practice: Varies across sectors, limited spread of awareness
“I had never heard of TDM
until I moved to the publishing industry.”
-- Former social science researcher
“Fewer than one in 20 researchers
are carrying out any kind of TDM in this domain.”
-- Research statistician
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11. Lack of data analysis skills
In a survey3 of 461 data management professionals:
▪ 38% of organisations do not handle any big data
▪ 40% cite inadequate staffing or skills to do so
▪ 61% say better big data management is needed to improve their
data analytics
In a survey4 of 125 ICT decision-makers:
▪ 66% lack skills to implement big data technologies
▪ 69% lack skills to capitalise on collected data
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3. TDWI Best Practices Report: Managing Big Data
4. Trends vs Technologies: A research report from Capita in partnership with Cisco

12. Poor understanding of what TDM can do
“Customers don’t know what they want –
it’s like trying to sell cool bicycles
when there are no roads.”
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13. FutureTDM recommendations
▪ Establish training and courses in data literacy and “computational
thinking” early in the educational system and in lifelong learning
▪ Integrate education about core principles of data management and
analysis across all subject areas, not just traditionally quantitative
fields
▪ Encourage cross-discipline and cross-sector sharing of skills,
knowledge and best practices
▪ Provide evidence of the benefits of TDM to encourage investment
in and development of TDM skills
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14. The Role of Universities
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15. The role of universities
Involved in all steps of the TDM value chain
▪ Content creation (research data, papers, etc.)
▪ Content dissemination (via repositories, licensing)
▪ TDM activities (dedicated departments, broader programmes)
▪ Value creation (deriving new knowledge and insights from TDM)
Need better awareness and education across all steps
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16. The role of research libraries
Natural candidates to coordinate support for TDM
▪ Already undertake a variety of research-supporting activities
▪ Several of these are already relevant to TDM
(e.g. data management, open access)
▪ Already coordinate support across different university departments
(e.g. legal, financial)
▪ Can mediate and facilitate sharing of knowledge across different
domains, bridging gaps
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17. Need for strategic, coordinated approaches
Relying on ad hoc interpersonal networks limits sharing
of skills, knowledge, best practices
▪ Universities are large networks with complex hierarchies
▪ Wide variety of stakeholders with different needs
▪ Need for dedicated staff time/resources, supported by policy
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18. What Can Universities Do?
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19. What can universities do?
Demonstrate need
▪ Ghent University: Surveys and interviews identified data management and
data science as key skills to develop and support
Involve all stakeholders
▪ Ensure buy-in from researchers, research faculty, education faculty, IT,
other specialized departments…
Understand internal processes
▪ Know how educational courses are structured, and how departments
communicate, to work with existing processes
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20. What can universities do?
Consolidate information and resources
▪ Bringing together fragmented knowledge from different departments and
sectors helps everyone share best practices
Identify early adopters and promoters
▪ Particularly domain experts with personal interest in supporting TDM and
other data-related practices
Introduce incentives
▪ Talk to stakeholders to understand what would benefit them – incentives
may not need to be financial!
Coordinate with industry
▪ Ensure people are learning relevant skills and techniques
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21. What can universities everyone do?
Share success stories!
▪ …of integrating TDM awareness into curricula
▪ …of implementing support for TDM and other data-related activities
▪ …of solving TDM-related skills or infrastructure problems
▪ …of exciting applications of TDM tools and technologies
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