What each country brought to Make Inclusive Data the Norm

What each country brought to Make Inclusive Data the Norm 

Make Inclusive Data the Norm (MIDN) is a South-South learning project between Colombia, Ghana, and Kenya to advance inclusive data systems. 

Launched during the UN Statistical Commission in New York on February 28, 2024, at a meeting of the Heads of the National Statistical Offices (NSOs) from Colombia, Ghana, and Kenya, MIDN is a partnership between APC-Colombia and the Global Partnership for Sustainable Development Data. The initiative aims to enable the three countries to learn from each other’s experiences and challenges in advancing inclusive data.

By leveraging local experiences and citizen data, the initiative supports governments and civil society to co-create more participatory and accountable data ecosystems and practices. 

Colombia

Colombia joined MIDN to strengthen the capacity of civil society organizations (CSOs) and communities to produce and use reliable data, incorporate citizen-generated data into the national statistical system, and gain insights from the experiences of Ghana (in technical innovation and deployment), and Kenya (in quality standards and partnerships for better citizen data). 

Colombia has a strong track record for data innovation, encouraging citizen participation in data, and intersectional data approaches. Through MIDN, the country developed new participatory approaches for collaborating with CSOs and co-designed new apps and tools for documenting issues that are tough to measure through traditional statistics, such as discrimination faced by marginalized groups and access to public services. The country is also developing a new Citizen Data Framework, and an innovative Maturity Model that lets organizations see how ready they are to integrate citizen data.

Ghana

Ghana joined MIDN to leverage its legal mandate to explore non-traditional data sources for national development and address critical data gaps affecting vulnerable populations. The Ghana Statistical Service (GSS) made citizen data a top priority as a way to understand the lived realities of underrepresented communities, make public resource allocation more accountable, and use technology to make decisions based on data. 

Ghana's focus through MIDN was on stopping female genital mutilation (FGM) in the northern parts of the country, where multi-stakeholder consultations found big policy gaps, outdated information, and data that was not disaggregated by factors like age and socioeconomic status, making targeted interventions difficult. 

GSS has impressive machine learning capabilities, and was already working on developing mobile apps for citizen data collection on sensitive issues, including gender-based violence. GSS brought skills and experience in technical deployment, accessible technology, and cross-sector collaboration to MIDN (for example, working with telecoms networks to roll out mobile technology). GSS wanted to build on existing citizen data projects by creating systematic frameworks for quality assurance, and building the capacity of institutions. Ghana aimed to learn from Colombia's intersectional data approaches and Kenya's quality assurance frameworks through South-South collaboration.

Kenya

Kenya joined MIDN to institutionalize citizen data within its national statistical system and strengthen the already-progressing collaboration on inclusive data practices between the Kenya National Bureau of Statistics (KNBS) and civil society organizations.

Through MIDN, Kenya aimed to learn from Colombia's Maturity Model approaches and Ghana's technology innovations, while sharing its own experiences in NSO-CSO partnership models, exemplified by the Memorandum of Understanding signed between KNBS and SDG Kenya Forum, and civil society leadership on disability data through organizations like United Disabled Persons of Kenya (UDPK). The MIDN initiative aligned with Kenya's national commitments to SDG monitoring and the Leave No One Behind agenda, particularly for people with disabilities, women, refugees, and other marginalized communities.

Read more about how Colombia, Ghana, and Kenya are advancing inclusive data practices through South-South collaboration.

Why South-South learning on inclusive data?

The Leave No One Behind agenda

The 2030 Agenda for Sustainable Development pledges to "Leave No One Behind" (LNOB) by making sure that development progress benefits everyone. To achieve this, we need data systems that accurately represent the lives of marginalized and vulnerable groups, such as women and girls, people with disabilities, indigenous communities, refugees and migrants, LGBTIQ+ people, and people who live in poverty or remote areas. 

However, traditional statistical systems don't always have the granularity, disaggregation, and community involvement needed to represent these groups in data, which can lead to policies that do not meet everyone’s needs and deepening inequalities. Through MIDN, NSOs, civil society organizations, and communities are working together to include citizen-generated data and intersectional approaches in official statistics and policymaking.

Data gaps and marginalized communities

While data collection has improved around the world, there are still key data gaps around the experiences of marginalized groups in the Global South. In all three MIDN countries, landscape assessments showed that there were common problems due to insufficiency of: disaggregation of different identity markers (gender, ethnicity, disability, age, location); territorial granularity of local inequalities; involvement of communities in deciding how their data is collected; coordination between NSOs and CSOs that collect community data; and resources for both government institutions and grassroots organizations to effectively produce, validate, and use inclusive data. These gaps can lead to policies that don't include or consider vulnerable groups in development plans.

Discrimination against LGBTIQ+ and Afro-descendant communities in Colombia; female genital mutilation in Ghana; and disability data in Kenya, are all examples of areas that traditional surveys have trouble measuring but that community-led projects can help shine a light on, leading to more effective and targeted policies and practices.