The adoption of SDGs and the associated targets by the United Nations General Assembly has set the global development agenda for 2030. The strategies and action towards SDGs are informed and to a large extent depend on the capacity of data ecosystems to provide relevant indicators data and metrics for progress monitoring. The modus operandi of the traditional statistics entities (e.g. National Statistics Offices) is being disrupted and “forced” to engage not only with new data sources but also with new technologies and data analysis tools (e.g. Machine Learning tools and algorithms). This is not only the inevitable evolution and modernization of the statistics production systems, it is also necessitated by the large number of SDG indicators for which novel and innovative data sources and methodologies are needed.
The focus of human development monitoring has traditionally been for informing strategy and action for governments, with minimal focus on enabling and supporting local community-level action. This invariably means that the lenses of data analysis and reporting are typically skewed towards identifying macro phenomenon as opposed to the individual “interesting” points in the data. This bias towards observing macro-patterns continues in current Big Data innovations, some of which are being utilized and incorporated into SGDs indicators monitoring interventions. The current trajectory, wherein significantly more effort, partnerships, and interventions are being initiated towards the utilization of Big Data for SDGs, while missing the opportunities around Small Data, represents an overall risk to the SDGs indicators monitoring effort. This is a risk that directly affects the very principle of “leave no one behind” which is core and foundational in the articulation of the SDGs.
“Small Data connects people with timely, meaningful insights (derived from Big Data and/or “local” sources), organized and packaged – often visually – to be accessible, understandable, and actionable for everyday tasks” (Digital Clarity Group). These are the kinds of insights that not only enable and support local community level action, but also allow for an exploration of a phenomenon at a detailed level to allow for targeted and focused interventions. Only through a synergistic interaction between Small and Big Data approaches within the data ecosystems, is a more complete picture of the wellbeing of individuals presented.
This research adopts an ecosystem perspective on SDGs indicator monitoring, and considers the dependencies between the different actors involved in collection, processing, and communication of SDGs indicators data. The research seeks to undertake targeted (i.e. on specific countries and specific SDG indicators) interventions to investigate the use of verified and validated Small Data to support SDGs indicators monitoring. The research seeks to make contributions to improving the effectiveness of country data ecosystems through implementation of Small Data systems and platforms. This will be linked to specific challenges and SDG indicators, to support official statistics production and community level action.