Disaggregated data: an opportunity to increase the visibility of different realities

July, 2021

Margarita Vaca
Researcher, Data for Development Unit 
m.vaca@cepei.org

The 2030 Agenda and the Sustainable Development Goals (SDGs) have established the premise of 'leaving no one behind'. This requires making visible the particularities of each population group, community and territory, identifying their needs, interests and priorities. Disaggregated data therefore becomes a tool for collecting information that reflects local realities and allows decision-makers to translate plans, programs and projects which make up public policies, into activities that respond to the deprivations faced by societies.

What are disaggregated data and why are they important?

Although there is no single definition that conceptualizes disaggregated data, it is possible to understand it as the representation of the different components of a phenomenon or situation that allows us to determine trends and associated patterns, as well as to characterize the phenomenon or the actors involved, by breaking down the aggregate data into smaller units.

Data can be disaggregated by dimensions that in turn contain categories such as gender (male or female), areas (rural/urban), income (high, medium, low), educational level (high school, undergraduate, graduate), language (Spanish, English...), age groups (age ranges), ethnicity (Afro-descendant, indigenous...), etc. depending on the nature of the analysis (IAEG-SDGs, 2019)

For example, the identification of the target population for the implementation of a program aimed at strengthening learning processes at basic levels in a specific municipality requires a disaggregated data analysis, both of educational institutions and students, to focus resources and efforts.

Regarding the first case, the location by neighborhood and stratum, number of students, educational levels, among other data, should be identified in order to profile the beneficiary educational institutions. Similarly, disaggregated data allow us to gain detailed knowledge of the students and their families or caregivers, by segmenting them by gender, age group, geographic area, social stratum, socioeconomic conditions of their families, diverse abilities, academic performance, among other aspects.

The disaggregation of data by different categories and characteristics is essential to highlight differences in the development of territories and communities, in order to provide evidence for decision-makers and planners to establish actions aimed at closing any type of current or future gaps that restrict the capacities of a population.

What are the principles of disaggregated data?


1. All populations should be included in the data.

2. All data should, whenever possible, be disaggregated to accurately describe all populations.

3. Data should be drawn from all available sources.

4. Data collectors and statisticians should be held accountable.

5. Human and technical capacity to collect, analyze and use disaggregated data must be improved, particularly through adequate and sustainable funding. We know that collecting and analyzing disaggregated data requires specific skills that need to be developed.

These principles highlight the need to make people visible through data to understand their lives and include them in decision making for sustainable development, bearing in mind the disaggregation of data whenever possible and the use of both official and non-official sources, in order to produce timely and quality data always under the framework of transparency, confidentiality and privacy.

What is the relationship of disaggregated data and the Sustainable Development Goals?

The 2030 Agenda and the Sustainable Development Goals (SDGs) demand the collection of disaggregated data to better understand the real needs of all population groups, especially the most vulnerable and 'leave no one behind'.

For example, when the UK Office for National Statistics (ONS) disaggregated maternal mortality data by ethnicity, it found that colored women suffered a higher level of maternal mortality (32 per 100,000 live births) than any other ethnicity (Asian 12.7 per 100,000 live births) (SDG UK Platform).

Along these lines, it has been established that SDGs indicators should be disaggregated by income, sex, age, race, ethnicity, migratory status, disability, geographic location or other characteristics, in accordance with the Fundamental Principles of Official Statistics. It is estimated that at least 94 indicators should be disaggregated by one of these dimensions (ECLAC, 2018).

Aware of the challenges involved in disaggregating data within the framework of the 241 SDG indicators defined, in 2016, the United Nations Statistical Commission created the Inter-Agency and Expert Group on Sustainable Development Goal Indicators (IAEG-SDGs) that seeks to strengthen national capacities and develop the necessary statistical standards and tools for this purpose (IAEG-SDGs, 2019).

The expert group has identified the availability of data disaggregated by dimension for SDGs indicators according to the global SDG indicator database, evidencing the efforts made to strengthen disaggregated measurement and the significant challenges that still need to be met to ensure that all SDG indicators are at the level of disaggregation required, particularly in dimensions such as ethnicity and race that are not covered by any indicator.

What's next?

The strengthening of capacities and development of skills among stakeholders producing official or non-official information, both at the national and subnational levels, allows the capture, reporting, processing and dissemination of disaggregated data to become easily applicable processes. In addition, they build installed capacities to venture into new collection methods that promote the generation of this type of data, such as the analysis of social networks, citizen-generated data, among others, in line with the bridging of gaps for the measurement of SDGs indicators.