Data Literacy in Accounting Education: Pedagogical Strategies, Analytics Tools and Global Standards Alignment
by Liyana Nadhirah Kamal, Nur Syuhada Jasni
Published: January 13, 2026 • DOI: 10.47772/IJRISS.2026.1026EDU0021
Abstract
Data literacy (DL) is a fundamental competency required in the accounting profession and a key pillar of datadriven professional decision-making. Professional accountants have the dual duty to understand complex datasets, utilise visual analytics effectively, and apply sound ethics and ethical reasoning to the data. International accrediting organisations, such as the Association to Advance Collegiate Schools of Business (AACSB) and the International Federation of Accountants (IFAC), have acknowledged the importance of DL via IES 2 (Technical Competence), IES 3 (Professional Skills), and IES 4 (Ethics and Values). This literature review presents a comprehensive review of the current evidence base, compiled from nineteen peer-reviewed studies published between 2015 and 2025, focusing on the relationship between DL and worldwide standards in accounting education. Informed by three key research questions, there is a focus on (i) the competencies and institutional support of teachers; (ii) the use of digital tools and curricula; and (iii) ethical, equity, and global strategies for the use of DL. Four themes emerge: educator empowerment, technology-based curriculum development, ethical and equity considerations, and global policy strategies. Findings indicate the need for comprehensive institutional support, instructor training, and tiered curricula that incorporate a range of instructional strategies, from basic tools (e.g., Excel) to more advanced techniques (e.g., analytics and scenario-based simulations). Ethical reasoning and transparency are integral to open-data projects, and global strategies should prioritise lowcost and scalable solutions to achieve equity in accessibility. Reform of the curriculum builds a strong foundation upon which to support AACSB and IFAC alignment. Future research should investigate the effects of DL on audit quality, fraud detection, and ethical reasoning in a quantitative manner. In combination, these insights provide guidance for training graduates with technical, ethical, and globally relevant skills in a data-driven profession.