Why is data disaggregation so important?

Leave no one behind

In the preamble of the 2030 Agenda, countries “pledge that no one will be left behind” – no Goal or target should thus be seen as met until it is met by all. To track progress in this regard, it is necessary that data can be disaggregated by a number of strata, such as income, gender, age, race, ethnicity, migratory status, disability, geographic location and other characteristics relevant in national contexts.

Monitoring universal access

For SDG 6, the ambition to leave no one behind is particularly relevant for targets 6.1 and 6.2 on universal access to drinking water, sanitation and hygiene. Current data can be disaggregated for place of residence and subnational region as well as wealth. Over time, the ambition is to include informal urban settlements in the data collected, as well as to develop survey instruments that can capture marginalized groups and intra-household inequalities, such as sex, age, and disability.

Monitoring impact of management

For targets 6.3 to 6.6 on water-, wastewater- and ecosystem resources, with indicators of more physical character, it is more challenging to disaggregate data for social strata. However, the impacts of poor management of these resources affect different groups of people differently, and this is important to assess and analyse. Geo-referencing physical data facilitates such an assessment and analysis, e.g. by recording in which basin within a country that water scarcity prevails, it is possible to assess the social impacts of water scarcity by looking at the number of people living within the basin.

Disaggregation is essential to make data as useful as possible, and to make sure that no one is left behind (Photo credit: Asian Development Bank, Creative Commons Attribution)

Disaggregation is essential to make data as useful as possible, and to make sure that no one is left behind (Photo credit: Asian Development Bank, Creative Commons Attribution)

Making data as useful as possible

Further types of disaggregation that are central to maximise the use of SDG 6 data include geographical (which river is polluted?), temporal (when is wetland extent being recorded?) and sectoral (which sector is using water and generating wastewater?), as well as the possibility to break down indicators into its sub-components (e.g. different aspects of IWRM and transboundary cooperation, type of ecosystem). 

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