Investigating an Architectural Framework for Small Data Platforms

The potential of data to support solutions to some of the global grand challenges is uncontested. This potential is recognized and confirmed through the articulation of technology as an explicit Means of Implementation for the Sustainable Development Goals. In particular the advent of big data has introduced not only new data sources and data providers, but also new data analytics and processing algorithms that are having an impact across national and global data ecosystems. The major investments to harness this potential for data are being made in the private sector, to provide insights to inform better decision making for business; and also in the public sector where governments are exploring the use of data for better governance and service delivery. The role of data to make an impact on societal challenges, especially in the context of challenges related to social wellbeing and the Sustainable Development Goals, is typically considered from the macro and meso levels where the trends about national or state/district level phenomenon are observed. This macro level (also called ecological level) perspective, with its associated instruments of analysis, techniques of visualization, is in contrast to another growing perspective which is encapsulated in the small data approach. The small data approach seeks to connect individuals with ‘timely, meaningful insights, organized to be accessible, understandable, and actionable for everyday tasks’. Thus within this approach the unit of sampling (which is usually an individual or a household) is maintained as the same unit at which data analysis is undertaken. Consequently the target of consumption of the derived insights and knowledge is the individual, which implies the use of reporting and visualization techniques that are similarly geared at the individuals. This paper revisits an architectural framework for knowledge-oriented, context-sensitive platforms, and evaluates this architecture for the realization of systems and platforms that embody the small data approach. Through a layered and modular separation of data, access, social networking, interaction and presentation components, this architecture seeks to achieve the interaction and presentation personalization for individuals while ensuring not only improved data provenance preservation but also the security of the underlying data.