Abstract:

Organisations worldwide are accelerating digital transformation in response to rapid technological advancement, adoption of AI, and the expanding role of data in decision-making. While significant attention has been given to adult learning curricula, organisations increasingly face a parallel challenge: how to systematically and continuously develop, measure, and recognise data capabilities within their professional workforces.

In this work, we describe how the National University of Singapore (NUS) addresses this challenge through the introduction of a Data Literacy Skills Recognition Scheme (SRS), a university-wide framework designed to institutionalise and formally recognise advanced data competencies, while developing an in-house talent pool within its Executive and Administrative workforce.

The initiative builds upon the University’s suite of stackable micro-credentials leading to a Master of Science (MSc) in Applied Data Science for working professionals published in the EDULEARN 2025 proceedings (doi: 10.21125/edulearn.2025.2078). While earlier work focused on inclusive adult-centric curriculum design (e.g., open-funnel admissions, modular, bite-sized blended learning, and workplace-based projects), a key question remained: how can the attainment of stackable micro-credentials be translated into sustained workplace practice, measurable organisational value, and recognised professional identity? The SRS represents the next phase of that transformation journey.

Anchored within NUS’ Continuing Education and Training (CET) strategy, the SRS positions the University not only as a provider of data science education, but also as a “living lab” for internal capability development and lifelong learning. The Scheme formally recognises staff who demonstrate continued application of advanced data analytics beyond their substantive roles in the workplace. It integrates three components: completion of approved academic qualifications - Graduate Diploma and/or MSc in Applied Data Science; conferment of Secondary Job Titles - Associate Data Scientist / Data Scientist with skills allowances; and a minimum of 200 practice hours annually comprising impact-oriented, cross-departmental data-related projects, facilitation of workshops, and leading Communities of Practice.

The Secondary Job Titles are conferred on renewable three-year tenures and supported by a structured governance framework. Staff contributions are documented through an e-Journal and reviewed by a multi-disciplinary panel, with institutional alignment and standards overseen by relevant offices. In recognition of their specialised competencies and sustained contributions, staff also receive a competitively benchmarked monthly skills allowance, reinforcing the value placed on advanced data expertise within the University. This conferment preserves the University’s existing employment structure while concurrently building an in-house talent pool of data analytics expertise.

This case study offers an implementable model that extends beyond curriculum reform towards the institutionalisation of organisational capability. It illustrates how institutions of higher education can integrate competency-based, stackable adult learning pathways with strengthened workplace application through a robust Human Resource recognition framework aimed at building sustainable, institution-wide data-driven capabilities in an AI-enabled landscape.

Keywords:

Data science, skills recognition, stackable micro-credentials, adult-learning, workforce development, lifelong learning, workplace learning, learning application.