Last Mile Data Enablement and Collaboration, and Building Trust in Indicators Data
The ensuing data revolution not only holds 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 technology-supported participation of community-level actors, and building of trust, in indicators monitoring.
Building trust in data, especially in this era of fake news and alternative truths, is typically linked with notions of ensuring transparency in visibility, data quality assurance, and data provenance visibility. 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 “not leaving anyone 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.
This project is undertaken through the following set of activities in pursuit of the project goals.
- Data enablement for SDG 3 “Health and wellbeing” – Individuals are increasingly engaged in indicators data monitoring, through personal informatics, life-logging, quantified self, and from IoT sensor data. This research focuses on SDG 3 “health and wellbeing” and investigates individuals’ current data collection and monitoring practice; explores motivations and incentives for data use; investigates data utility, sharing and social sense-making; and explores data enablement through technology.
- CBO intermediated data collaboration – Community based organizations (CBOs) have the potential to play a unique and crucial role in the process of implementing and monitoring progress towards meeting the UN SDGs. The research explores the modalities of engagement for CBOs, unpacks their data intermediation and collaboration role within the indicators data ecosystem, and explores the role of technology to amplify their engagement.
- Participatory indicators for Tamil Nadu MSME sector – This research activity aims to support the localization of SDGs for the MSME sector in Tamil Nadu – India, to facilitate the active participation of the MSME firms in indicators monitoring, and to explore the modalities of engagement and participation for the MSME firms.
- Capability Maturing Models for NSOs – Capability Maturity Models (CMMs) for National Statistics Offices are an instrument for ascertaining the maturity of the organizations’ processes and systems towards the production of high-quality, trusted indicators data. This research aims to develop prescriptive, multi-dimensional CMMs to assess maturity of NSOs and to prescribe pathways towards increased maturity.
Small Data, Sustainable Development Goals, Social Indicators Monitoring, Data Ecosystem Maturity, Data for Development, Participation
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