Digital Skill and Workforce Capacity
A Blog post by David Castle (WDS Scientific Committee Member)
In July of this year, the Organization for Economic Cooperation and Development (OECD) released its report, Building Digital Workforce Capacity and Skills for Data Intensive Science. Commissioned by the OECD’s Global Science Forum (GSF) in 2019, this is the ninetieth report in the OECD’s series of Science, Technology and Innovation Policy Papers.
The main focus of the report is to understand the training needs of public sector research that is becoming digitized as scientific disciplines evolve, data management becomes more prevalent and rigorous, and open science continues to be a call to action and emerging practice. Digitization of research across all disciplines has also attracted digital infrastructure and cybersecurity investments. At the same time, however, digitization both drives research competitiveness in new directions for scientists and demands greater expertise in new competencies for research support personnel. Is everyone keeping up with the pace of change?
Figure 1. Venn Diagram of Roles and Responsibilities
(Figure 3 from Building Digital Workforce Capacity and Skills for Data Intensive Science)
The answer to this question is mixed in three main ways. First, as the Venn diagram from the report visualizes it (Fig. 1), there are roles and responsibilities for researchers and support personnel working in data-intensive sciences that have functional titles, but where their competencies overlap. Using illustrative examples from several case studies shows that roles and competencies have been changing for some time. Second, because the mix of capacity and competencies is a moving target, the present challenge is to identify the skills needed as the composition of the research workforce changes. The third point is that training has been lagging behind the front wave of digitizing research, leaving skills gaps that may be ignored or go unnoticed.
Figure 2. Digital Workforce Capacity Maturity Model
(Figure 5 from Building Digital Workforce Capacity and Skills for Data Intensive Science)
The Expert Group convened by the OECD GSF, on which I served as a member, realizes that not every OECD member state, or non-members for that matter, has recognized the challenge of building workforce capacity and digital skills at the same pace, or with the same level of resource commitment. A ‘digital workforce capacity maturity model’ was developed to capture this diversity (Fig. 2). It serves as a rough indicator of what training is needed most urgently, according to where one lies on a spectrum of training depth.
Figure 3. Opportunities for Actors to Effect Change Across the Five Main Action Areas
(Table 2 from Building Digital Workforce Capacity and Skills for Data Intensive Science)
The report also offers insights, organized initially as a matrix (Fig. 3), into who might do what to provide training. The ‘who’ are the main actors: national and regional governments; research agencies and professional science associations; research institutes and infrastructures; and universities. The ‘what’ includes a wide array of initiatives: defining needs; provisioning of training and community building; career path rewards; and broader enablers. This is more fully fleshed out in many examples from around the world, describing some of the initiatives that have been undertaken to develop training.
Recommendations are made for the various actors, and the report takes special note of what can and should be done at research universities, and their associated libraries. The overall recommendation to OECD members is that policies recognizing and enabling both the need for workforce capacity growth and access to digital skills training must be embraced to maintain the competitiveness of national and internationally collaborative research, and thus achieving its highest goals.
The report was in its final stages of review and approval when the COVID-19 pandemic struck. As we observed in the conclusion of our Foreword, ’The COVID-19 pandemic highlights the importance and potential of data intensive science. All countries need to make digital skills and capacity for science a priority and they need to work together internationally to achieve this. To this end, the recommendations in this report are even more pertinent now than they were when they were first drafted in late 2019’. As we get nearer to the end of 2020, all indications are that the need to build workforce capacity and digital skills for data-intensive sciences has not only escalated, but now must address new realities, research priorities, urgent timelines for training, and challenge socioeconomic circumstances.