Seven key takeaways

If we hope to get closer to achieving the global goals over the next decade, it will be necessary to mobilize intelligence of all kinds to better understand problems, broaden the range of effective solutions, and implement new ideas more effectively. The 13 stories from the Labs offer many lessons for challenges to watch out for, as well as how to overcome them.


Collective intelligence design offers a varied toolbox to help the development sector make progress on the SDGs. The 13 case studies described in this report offer many lessons for challenges to watch out for, as well as how to overcome them. Below, we summarize the main takeaways that cut across the case studies, as well as the lessons learnt across the UNDP Accelerator Lab Network.

Working with people to create change


1. Move beyond the usual suspects to build new coalitions for the SDGs

The Accelerator Labs have demonstrated the benefit of engaging with existing networks to expand the potential reach, quality and impact of their collective intelligence efforts. For example in Zimbabwe, by partnering with trade associations, the Lab was able to make visible the contributions of 20,000 informal workers in the food production value chain from all across the country. Prior to this, the team had struggled to connect with informal vendors in local markets in Harare. Many other Labs have also worked with intermediaries who already had established relationships with local communities. This may be particularly important for collective intelligence design efforts that involve vulnerable communities and where the engagement is short term, remote, or there is little time to build trust. 

The Labs have also worked effectively with CSOs to leverage specific technical skills. In Ukraine for example, the Lab worked with an NGO who had experience of data visualization, while in Tanzania, the Lab partnered with an organization who had the expertise to support an online mapathon with local students. 

The Labs have also gone beyond the usual suspects to tap into new sources of innovation skills. For example, when working on the Life Helmets Challenge in Colombia, the Lab engaged with local maker communities to help them attract individuals with the necessary skills. And in Argentina, the Lab connected with an existing open source hardware community to support students building DIY sensors. Mobilizing new networks and partnerships is critical for tapping into diverse sources of distributed know-how to drive collective action for the SDGs.

2. Combine ‘big’ data and ‘thick’ data for contextualized insights

Official data can lack granularity of insight and can often be out of date. Novel sources of data, such as satellite data, can help address this. As the Labs found, however, big data might help you see patterns in what is happening, but it might not help you understand why. Collective intelligence methods can bridge these gaps by generating both quantitative measurements and qualitative insights that offer complementary perspectives on an issue. 

In Lao PDR, for example, the Lab analyzed satellite data to help map open burning hotspots. However, this data missed the reasons behind the persistence of open burning in these locations, many of which were served by municipal waste collection services. To fill these gaps, the team supplemented the data with ethnographic methods, including observations and interviews, as well as discussions during community town halls. In Vietnam, the Lab used on-the-ground GPS sensing to map routes taken by informal waste workers in combination with interviews about the barriers they faced and patterns of working. This helped the team to better understand their contributions to the waste collection system.

Embracing new ways of working with data


3. Use novel data and technology to help build relationships and trust with communities 

Grassroots communities can often question the motivations of institutional actors and the credibility of official data. While this challenge applies to development initiatives more broadly, it’s especially critical when it comes to the collective intelligence methods, which by definition rely on mobilizing insights and action with, and by, communities.

As the Labs discovered, involving communities in generating and analyzing data can help establish a shared and objective source of facts about an issue. This can help stakeholder groups to build trust and then propose solutions, with communities taking ownership of the results. An example of this is the collective intelligence work carried out by the team in Ukraine. The digital dashboard visualizing the locations of open burning from satellite images was considered a more trustworthy data source by community members than official reports. The Lab worked with rural communities to interpret this data, leading to new insights about how open burning patterns varied regionally. By learning how to work with the data themselves, these communities had more faith in its accuracy and were motivated to take action as a result. 

This experience is also echoed in Cambodia, where the Lab found that the accelerated adoption of digital tools like Miro, due to COVID-19 restrictions, enabled more diverse people to take part in meetings. In addition, anonymous participation and autonomous contributions led to a higher quality of contributions.

4. Establish responsible data stewardship and remain flexible during data partnerships

Negotiating access to private sector and other closed data requires high levels of technical proficiency and can take many months. Involving communities or volunteers in data collection complicates considerations around privacy and ethics. These common challenges can be off-putting for organizations lacking in-house expertise, and steer them away from experimenting with some of the most novel data-driven collective intelligence methods.

Some of the Labs have demonstrated the viability of creative solutions for overcoming these data partnership challenges. For example, the team in Mexico started working with publicly available datasets to develop a proof of concept while they negotiated a memorandum of understanding with the government. In Serbia, the Lab reprioritized research questions to avoid making requests for sensitive or personal information, allowing them to work with publicly available LinkedIn data.

When collective intelligence initiatives work with underrepresented or vulnerable groups, they need to take extra care that data collection isn’t extractive and is designed to benefit the communities involved. In Lao PDR and Vietnam, the Labs worked with underrepresented communities and invested significant resources in outreach and engagement. This is an important aspect of creating a collective intelligence design and shouldn’t be skipped. Experience also shows that partnering with trusted intermediaries (such as in Zimbabwe where the Lab worked with trader associations) can also be an effective route. Developing a central or regional support function for data stewardship that can provide guidance about different models for data partnerships and ethical data practices, may encourage local teams to work with new data sources. 

Building pathways to impact


5. Emphasize the potential of collective intelligence for agile policy-making and program design

A number of the Labs have demonstrated the value of collective intelligence approaches to inform more agile, localized and responsible governance. In Tanzania, for example, the team set out to understand the waste management practices in a semi-rural ward of Mwanza city, which had seen a rise of informal settlements as a result of urbanization. By using crowdmapping they created an up-to-date map of the infrastructure of this fast-changing neighborhood and identified potential sites of waste accumulation. These maps are already being used by the city’s Urban Water Supply and Sanitation Authority to understand how to adapt their waste collection services. Ukraine’s Ministry of Environmental Protection and Natural Resources is now aiming to scale the Lab’s data dashboard for open burning as a monitoring tool for estimating air quality and disaster risk. In Argentina, officials from the city administration and Ministry of Environment and Sustainable Development are planning to use the temperature and humidity measurements obtained by the sensors to design urban ‘climate corridors’. Although these examples are still at early stages, they provide a glimpse of how the Labs are helping governments make complex systems visible and understand problems closer to real time – enabling them to respond more effectively to localized issues.

6. Develop clear ‘hand-off’ mechanisms and routes to impact for prototypes

Many of the Labs have focused on creating prototypes using new methods. Some of these have generated interest from their national governments who have taken them on or are helping to scale what was developed. A good example of this is in Argentina, where the Lab has worked with both national and city level governments to adopt citizen sensing for environmental monitoring. Clearly identifying a ‘sponsor’ within the UNDP or government from the outset, and ensuring those stakeholders’ needs are factored into the design from the get-go, may help increase the likelihood that insights or prototypes find traction.

In Argentina, the Lab adapted the design of citizen science activities to collect environmental measurements that were more relevant to local urban planning decisions. In other cases, the prototypes have been taken up by the UNDP country office. For example in Zimbabwe, the analysis and visualizations produced by the Lab are being used to adapt the ways the country’s long-running Resilience Building Fund works with data for annual reporting. While in Vietnam, the Lab chose the locations for their waste management project in collaboration with UNDP environmental experts. This ensured that the resulting insights could be used to complement the ongoing work of the Climate Change Unit in these districts. 

However, many of the case studies weren’t developed with a clear path to adoption or use. This is a common challenge for innovators tasked with socializing new methodological approaches. The Labs could overcome this by spending more time upfront articulating the change they want to create and involving key stakeholders early in the design phase.

7. Invest in building capacity for the wider ecosystem

The most successful collective intelligence initiatives within the UNDP Accelerator Lab Network have been achieved by partnering with local NGOs who already have some methodological expertise in working with non-traditional data sources or participatory methods. For example, in the Ukraine and Tanzania, both waste management projects depended on GIS expertise provided by their local civil society collaborators. Other Labs turned to experienced researchers based at universities, working with them on a consultancy basis. This was the route taken by the team in Vietnam to tap into GIS expertise and by the team in Mexico to identify an NLP researcher. These cases highlight the challenge that Labs face when it comes to finding appropriate partners. The Labs have also found it necessary to invest time in helping to build understanding of collective intelligence amongst the organizations and stakeholders they work with.

For collective intelligence methods to thrive, the entire development sector will need to invest in developing these 21st-century innovation skills locally, as well as across borders. One potential source of future talent is students. In Tanzania, Lao PDR and Argentina, the Labs all incorporated training and workshops with local students into their project designs, so students could then undertake crowdmapping and ethnography, and build DIY air pollution sensors. Involving groups of students helped the teams to complete vital project tasks, including data collection and hardware assembly, whilst helping to build future capacity in the local ecosystem. Country teams and Accelerator Labs should continue to cultivate skills in-house, as well as providing external training and workshops on collective intelligence methods. This will expand the pool of opportunities for collective intelligence methodologies to be applied at scale.