The ensuing data revolution not only holds the potential to support and inform action towards the realization of the UN Sustainable Development Goals, it also holds great potential to marginalize, exclude and misinform. This research investigates the technology-supported participation of community-level actors, and building of trust, in social indicators monitoring.
Building trust in data, especially in this era of fake news and alternative truths, is typically linked with notions of ensuring transparency and visibility, data quality assurance, and maintaining data provenance. Beyond these data-centric aspects, trust is also associated with socio-political notions of democratic participation, civic engagement, citizen ownership and buy-in. Thus building trust within the data ecosystem and “leaving no one behind”, which is a core principle of the UN Sustainable Development Goals, are not two disparate concerns in indicators monitoring but are rather aspects of the same goal of progression towards the maturity of the social indicators ecosystem.
This project has the goals of exploring and expounding on the role of data (esp. social indicators) towards individuals development and wellbeing; supporting and catalyzing community-level action towards the Sustainable Development Goals (SDGs); democratizing social indicators monitoring by highlighting and demonstrating the role of the bottom-up, micro-level, citizen-generated data to complement the official social indicators; and enhancing trust in social indicators data.
SMALL DATA APPROACH
Overall, this research adopts the small data for development approach, which is “an approach to data processing that focuses on the individual (or the source of data) as the locus of data collection, analysis, and utilization towards increasing their capabilities and freedom to achieve their desired functioning”.
Small data is about empowering people, who are in most cases the sources of data, with relevant and actionable insights from data through adopting an approach of analyzing data at the same unit at which it is sampled. Small data not only enables and supports individual and community level development action, but also allows for a nuanced understanding of the complex human development phenomenon. Thus, the bottom-up, micro-level, citizen-generated, locally-relevant data stands to augment and complement the largely top-down, macro-level human development data. Only through a synergistic interaction between the small data approaches and the traditional social indicators approaches within mature data ecosystems, can the full value and utility of data for development be achieved and delivered.