In 2014, Ryerson University launched Canada’s Big Data Consortium to bring industry, government, and academia together to collaborate on key issues related to big data and analytics. For its inaugural project, the consortium led the first national, multi-sector study on Canada’s Big Data and Analytics Talent Gap – to better understand the extent of the gap, and to develop strategies to help close the gap.
The paper, Closing Canada’s Big Data Talent Gap, presents the outcomes of the study; below, we present some highlights. While the research was conducted with a Canadian focus, the lessons learned and strategies developed can be applied by organizations, industries, sectors, and regions globally.
- A multi-sector challenge requires a multi-sector solution
Through six months of research and consultation, we found that Canadian employers of all sizes and across all regions, sectors, and industries are struggling to recruit and retain big data and analytics talent. Meanwhile, academia and government are trying to keep up with employer demand by developing more of the right types of talent, deployed in the right places and at the right times. Despite the clear interdependencies, collaboration had been limited to date.
The big data talent gap is a multi-sector challenge that requires multi-sector solutions. Industry and government (as employers) need to provide critical input on talent needs – who, what, when, where, how – to help academia optimize curriculum, and to help government better facilitate talent deployment. To close the talent gap, collaboration between the demand and supply sides of the talent equation is critical.
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- Improve labour market clarity: establish common professional definitions, career pathways
What is a Chief Data Officer, a Data Scientist, or a Data Solution Architect? And what do they do? In the absence of common professional definitions and career pathways, we found that employers struggle to clearly express their big data and analytics talent needs, and that prospective talent find it difficult to assess their suitability, interest, and candidacy in the field.
To close the big data talent gap, we believe that labour market clarity between prospective employers and prospective talent is essential. As such, the consortium will develop, propose, and share definitions and pathways which can, in turn, be customized to meet global, regional, sector, and industry nuances.
- Better meet employer demands: build more of the right types of talent
In our study, we found that Canadian employers are certainly demanding technical skills, such as data mining and data analysis. But, cited with equal frequency were complementary skills, such as project management, and consulting-related skills. Yet, a sampling of the big data and analytics offerings currently available through universities, colleges, and training companies shows that existing curriculum is highly focused on technical skills – thus not currently meeting the full scope of employer demand.
To close the big data talent gap, we believe that academia not only needs to increase its supply of educational and training offerings at all levels (i.e. undergraduate, graduate, professional, and executive), it also needs to expand existing curriculum to incorporate in-demand complementary skills. And, as data literacy becomes an imperative for all professionals, we believe that academia should review its curriculum at all to ensure data skills are embedded at the right level at the right time, starting at K-12.
- Leverage existing talent: source and grow talent internally
In our study, we found that Canadian employers are primarily relying on external recruitment for big data and analytics talent. However, this overlooks a readily available talent pool: existing, in-house talent. The right resource can be found in unexpected places – an employee’s role and responsibilities today does not always reflect his or her past experience, or future capacities and capabilities.
To close the big data talent gap, we believe that employers should also consider looking internally to identify talent for development, as a complementary strategy to external recruitment. While this strategy is associated with learning and development costs, there are distinct advantages to developing in-house talent: existing talent possess the deep industry and functional expertise, as well as knowledge of their organization’s people, processes, and technologies that is so essential for extracting value out of data, and which could potentially take years to acquire.
- Optimize talent utilization: use shared services models
In our study, we found that, in some Canadian organizations, big data and analytics talent already exists, but are often “hidden” within functional groups. As a result, these in-demand resources difficult to leverage strategically, and enterprise-wide.
To close the big data talent gap, we believe that employers can use shared services models, such as a Centre of Excellence (CoE), to optimize and amplify existing in-house talent. In the CoE model, resources can be fully dedicated to projects that are selected and prioritized based on factors such as strategic fit, business need, and business impact; best practices and lessons learned can be codified and disseminated enterprise wide. Moreover, because resources are staffed on a steady flow of strategic, impactful, and interesting projects, CoEs are also often a beacon for top talent attraction and retention.
- Government as key enablers
All levels of government have at their disposal significant strategic levers (e.g. legislation, policies, programs, funding, grants, etc.) to help close the big data talent gap. Yet, in our study, we found that few employers understood what resources could be engaged and how.
In its role as an advocate of economic growth, competitiveness, and innovation, we believe that government should work with employers so that they can better understand the resources available, and how best to use them. Government should also consider developing national, regional, and local talent strategies, including strategies that aim to better leverage existing but underutilized talent pools (e.g. women, older professionals, and immigrants) and continue to explore ways to facilitate talent movement within Canada and also internationally through strategic immigration policies.
The global workforce – a challenge and an opportunity
The rise of the global workforce presents both a challenge and an opportunity for the big data talent gap. On the one hand, unprecedented talent mobility means an intensification of demand among employers; competition is global. Governments that make it easier for employers to bring in global talent can help position its industries and sectors for a competitive advantage. On the other hand, a mobile workforce increases the talent pool accessible. Interdependencies clearly exist between industry, government, and academia. Addressing the challenges and opportunities associated with the global workforce calls for unprecedented levels of targeted collaboration, and likely requires government leadership.
A call to action: help us close the big data talent gap
On behalf of Canada’s Big Data Consortium, Ryerson University welcomes your feedback and insights, and invites any interested academic, industry and government contributors to collaborate with us to close the talent gap. For more information, please visit us online at www.ryerson.ca/talentgap.
Author: Peggy Steele is the Special Project Liaison for the Office of the Provost and Vice-President Academic at Ryerson University, and the Project Lead of Canada’s Big Data Consortium and Canada’s Big Data Talent Gap Study. She is a management consultant specializing in public sector, and a researcher focused on public sector innovation and open government.
Image: People stand in front of a big data analytics logo at the booth of IBM during preparations for the CeBIT trade fair in Hanover, March 9, 2014. REUTERS/Fabrizio Bensch