Скачать или смотреть UNSC53 side event: The use of non-traditional data and quality assurance in SDG monitoring

Concept Note

We are in “The Decade of Action” which demands quality and timely data to monitor the progress towards the SDGs to help decision makers understand where policy interventions should take place and where investments can have the greatest impact. This also implies the need to improve and accelerate the production of SDG indicators. Even more, according to the SDGs Report 2021 “The (COVID-19) pandemic has brought to the forefront the critical importance of such data. ... As policy- and decision makers were pressuring data providers for more up-to-date and accurate information, national statistical offices (NSOs) and their partners stepped up to the challenge. They forged new collaborations and leveraged alternative data solutions while increasing efforts to protect data privacy and confidentiality”.

To respond to this unprecedent demand for data, many NSOs have gone to great efforts to use non-traditional data sources to collect data for indicators, which couldn’t be measured using traditional sources. However, adopting these non-traditional sources and methods has posed enormous challenges to many NSOs as they cannot count on existing capacities to implement these innovative solutions in the required speed.

The discussion of the “what” (what technique, what model, what source, etc.) must be complemented with discussion also on the “how” (how such techniques were developed, how to train for such model, and so on) and, especially for the NSO, the “how” refers to “quality assurance”. The United Nations Statistical Commission has developed the UN National Quality Assurance Framework (UNNQAF), including guidelines to work with non-traditional data sources.

Considering that NSOs are the stewards of the quality assessment in the National Statistical System, it forces practitioners to see the broader landscape in this regard. How to connect the traditional quality framework assessment with new sources and methods? How to connect this with statistical standards like GSBPM, GSIM, NNQAF and the like? What role does the IAEG-SDGs play in this changing context? What actions and initiatives have been (or will be) developed, supported by the IAEG-SDGs and other groups (like the UN Big Data Global Working Group), that could act as a basis for ensuring quality of data from non-traditional data sources?

This side event will discuss aspects related to representativeness, definition and validation of the data from non-traditional data sources for SDG indicators, confidentiality issues, comparability with other data, capacity building, and the possibility to bridge data gaps. The event also seeks to broaden the perspective of the use of quality assurance frameworks to ensure the quality of the statistics produced by non-traditional data sources and methods.

Objectives
• Sharing experiences with innovative ways to assure the quality of statistical production using non-traditional data sources and new techniques, identifying best practices and main challenges ahead.
• Discussion about feasible standardized procedures that can be implemented by NSOs to ensure the quality of data, and at the same time widen the range of sources and methods used to increase data availability.
• Exploring and sharing strategies for capacity building in different regions.