Sarcouncil Journal of Engineering and Computer Sciences

Abstract: The needs of data-driven processes in healthcare, finance, energy, and even artificial intelligence have prompted the creation of custom mentoring strategies introduced to big data teams. Interdisciplinary work, rapid technology change, and staff turnover are some of the special issues that are linked to such teams. A big data environment is project-based, volatile, and hybrid, which does not augur well with the classic models of mentoring. The review is directed towards a critical evaluation of theoretical models, empirical research and organisational practices, which contain information about effective mentoring structures in this regard. The propositional model is designed with the dimensions of key elements being of mentor competency, contextual alignment, communication structure, and feedback. Experimental data confirms these results on the effectiveness of structured mentoring in improving the implementation of an undertaking, developing the technical competencies, and the satisfaction of the staff. The review concludes by offering a recommendation to conduct longitudinal and cross-sector research that can expand the scalability, equity as well as sustainability of mentoring systems of data-centric teams.