Tackling the global challenges relating to health, poverty, business, and the environment is heavily dependent on the flow and utilisation of data. However, while enhancements in data generation, storage, modelling, dissemination, and the related integration of global economies and societies are fast transforming the way we live and interact, the resulting dynamic, globalised, information society remains digitally divided. On the African continent in particular, this division has resulted in a gap between the knowledge generation and its transformation into tangible products and services. This paper proposes some fundamental approaches for a sustainable transformation of data into knowledge for the purpose of improving the people's quality of life. Its main strategy is based on a generic data sharing model providing access to data utilising and generating entities in a multi-disciplinary environment. It highlights the great potentials in using unsupervised and supervised modelling in tackling the typically predictive-in-nature challenges we face. Using both simulated and real data, the paper demonstrates how some of the key parameters may be generated and embedded in models to enhance their predictive power and reliability. The paper's conclusions include a proposed implementation framework setting the scene for the creation of decision support systems capable of addressing the key issues in society. It is expected that a sustainable data flow will forge synergies among the private sector, academic, and research institutions within and among countries. It is also expected that the paper's findings will help in the design and development of knowledge extraction from data in the wake of cloud computing and, hence, contribute towards the improvement in the people's overall quality of life. To avoid running high implementation costs, selected open source tools are recommended for developing and sustaining the system.
How to Cite:
Mwitondi, K.S. & Bugrien, J.B., (2012). Harnessing Data Flow and Modelling Potentials for Sustainable Development. Data Science Journal. 11, pp.140–152. DOI: http://doi.org/10.2481/dsj.009-027