The many thousands of innovators working around the world to achieve the Sustainable Development Goals (SDGs) badly need finance and political support. But all share another set of common challenges that have not had enough attention:
There is a simple reason for these overlapping problems: it’s no-one’s job to orchestrate and curate knowledge, evidence and data. As a result the world lacks the basic knowledge and data infrastructures needed to achieve the SDGs.
There are very strong capacities within companies to do similar things – particularly ones like Amazon, Google, IBM, Unilever or McKinsey, which are brilliant at organising consumer or financial data, or sectoral information. But there is nothing remotely comparable for the SDGs.
There are also very strong global institutions organised around money with a public purpose – from the World Bank, IMF and ADB. But there are no comparable institutions for data, information and knowledge.
Numerous projects provide sketches of what might be possible, some within specific fields like sanitation or malaria. There are dozens of repositories of evidence, peer networks and promising projects using data and bodies, like the Sustainable Development Solutions Network (SDSN) doing important work in fields like food and energy. There are also many exciting initiatives focused on better measuring the SDGs, ambitious ideas for data in the UN Global Platform project, and attempts to make philanthropy better at handling systemic issues, like Co-Impact.
But from the viewpoint of the innovators themselves the current platforms and tools remain hard to use, patchy and unreliable, and most are run on a shoe-string. And, as a study commissioned by Bertelsmann on the various impact platforms showed, there is ‘duplication of effort, fragile business models, and similarity of value propositions’.
Yet this is a soluble problem. It requires some commitment of resources, but very little as a proportion of aid or capital flows. It requires a genuine willingness to collaborate - with humility rather than over-obsession with brands and credit.
The task, in essence, is the conscious and deliberate orchestration of collective intelligence around each of the SDGs, bringing together evidence, useful knowledge, peer networks and data sets, tracking of experiments and innovations. It’s a task best done through fairly tightly focused initiatives - e.g. on issues like air quality or children’s health - since over generic approaches tend not to work. And it’s a task best done in close partnership with the people who will use the data and knowledge to ensure it really is used and useful, and can be adapted to local circumstances.
This last point is crucial. Most initiatives start with the providers of knowledge or with funders. But all of these projects are likely to work best if they start with the users of knowledge: how, in practice, will they make use of data, knowledge and insights, and how can a range of sources be curated to be as easy to use as possible?
The promise is to harness global collective intelligence and so multiply the effectiveness with which money and people are deployed. I think of this work as similar to fixing the plumbing - it’s less visible and glamorous than the vast gatherings like UNGA or COP 26, or big commitments around money. But it’s the vital missing infrastructure without which actions are bound to be less effective.
Here I set out in a bit more detail what this means – drawing in part on our work with the UNDP’s Accelerator Labs and the comprehensive Collective Intelligence Design Playbook which Nesta published last month, and on work over the last few years I’ve been involved in creating experimental versions of what is now needed on a much larger scale.
The starting point is the need to accelerate progress on the SDGs. All over the world countries are putting in place more ambitious strategies to achieve the SDGs. In many cases there is still a big gap between targets and current trajectories.
This diagram summarises some of the complementary elements needed to bridge these gaps, including top-down action by governments, innovation by business and bottom-up grassroots action and innovation, using SDG 6.2 as an example:
For some of the SDGs, there are detailed accounts of what each player needs to do, how these can complement each other, and how to fit distinct local environments. But for the great majority there is not. The key point is that we need to focus on versions of the blue line for each SDG, and in each region or nation: accelerating both innovation and adoption, and mobilising the unique contribution of each sector.
Bridging these gaps will require many things, including finance and political will. But a crucial necessary, if not sufficient condition, will be better orchestration of data, information, knowledge and evidence. Over the last few years a network of practitioners has been working on how to do this better. This thinking is feeding into the UNDP’s new accelerator labs, now forming in 60 countries (Nesta has been closely involved in developing their methods and training).
But the work of these labs is revealing major infrastructural gaps. Teams working on local or national strategies for achieving the SDGs, either through implementing existing approaches or innovating new ones, find it very hard to access crucial data, knowledge and insights. There are many excellent initiatives – but they are fragmented, often hard to access and usually ad hoc. As indicated earlier it’s (almost) no-one’s job to do this seriously, and so it remains a marginal activity for various bodies (like the UN, OECD, WEF, WB ...) but not central to any.
We believe that this gap can be filled over the next ten years, and that better coordination and orchestration could be achieved at relatively low cost. We propose focusing on four main areas: accessible science; useful evidence; real time data; and insights from innovation, and acting more systematically to connect existing initiatives and take them to the next level.
The first challenge is better organising the relevant scientific knowledge that underpins the SDGs and help their achievement, building on this kind of mapping.
Thematic Map of Research on Sustainable Development Goals
View the full-size image on the London School of Economics website
This is a fairly straightforward task which various groups are working on, though still in a fairly ad hoc way. It’s much easier for some SDGs - like the environmental ones, which are more precise - than others. For example, a lot of good work is underway on specific topics like oceans, helped by the OECD, UNESCO and umbrella bodies like POGO.
The next task is orchestrating what is known about what works, and also more specifically what works where, for who and how (drawing on the many organisations going beyond repositories of evidence to much sharper attention on how to get evidence used in the everyday work of teachers, policy-makers, health workers).
The Alliance for Useful Evidence and What Works Centres have made strong progress in creating an infrastructure of evidence in the UK, with a dozen centres and high usage rates - with important lessons about how to ensure evidence is really made use of.
At a global level there are examples like the Africa Evidence Network, the now-stalled Results for All, and the Cochrane and Campbell Collaborations and J-PAL. But most are small and certainly struggle to be used. This example from SUMMA in Latin America shows how complex evidence can be made digestible, though it too has struggled to get widespread useage by teachers and policy-makers:
The third task is orchestrating the data that’s relevant to individual SDGs. Some of this work is global – eg creating standardised approaches to environmental indicators; some is local – mobilising mobile phone companies to share data to support economic policies, building on programmes such as UN Global Pulse.
A lot of work is underway to gather data tracking the level of SDGs in different countries - but rather less on the more contextual data needed to support practical actions.
The new Collective Intelligence Playbook from Nesta, developed with UNDP, contains lots of examples. The VAMPIRE platform for environmental challenges in south-east Asia is a good one. Again, however, far less brainpower and money is being directed to projects like this than comparable ones in finance and business, or the military and surveillance.
Finally, we need better ways of linking up the many experiments and pilots underway around the world to ensure faster real time learning and mutual support. There are some technological tools that could assist this. Nesta has just launched arXlive which provides a real-time tool for monitoring innovation. Another Nesta project supported by the Robert Wood Johnson Foundation Nesta has created search tools (under the name Health Mosaic) that can scan not just for scientific research and patents, but also for research proposals, startups and meet-ups to create a living map of innovations underway globally (this will be formally launched open source in early 2020). Oddly, however, it’s not clear who would be the clients for more comprehensive version of this.
These tools can provide an overview of activity. It should also be possible for anyone running a pilot or experiment to link up with counterparts and share both data and experience. Yet this too remains remarkably hard in practice, although a few platforms like Nesta’s IGL now try to provide living maps of experiments in particular fields, like this one of current trials underway on business support:
Innovation Growth Lab's living map of experiments
View the full map on the IGL website
Bringing these together, the long-term goal should be an architecture of data, knowledge and insight for all of the SDGs, organised both at a global level and at national and local levels, in nested ways. In this way very local knowledge and perspectives can connect to more generalised, global knowledge.
These ‘intelligence assemblies’ would gather data, insight, memory and creativity, tied into practical action and learning, for example on graduate employment in a city, public health in a region or gender equity in a nation, or carbon reduction strategies.
Nesta’s Centre for Collective Intelligence Design links many researchers and practitioners around the world working in this space – and often combining data, AI and human collective intelligence to better address the SDGs. But as far as I am aware no one has yet come close to an intelligence assembly for any of the SDGs (though work on oceans may be closest). So as teams work through problem-solving, using the framework set out in the Playbook - from understanding and seeking solutions, to action and learning - they often have to orchestrate knowledge and data from scratch.
If the UN was being invented from scratch in the 2020s these functions might well lie at its core, and might be seen as just as essential as financial flows. We might have World Data Banks and International Knowledge Funds rather just organisations designed around money.
Unfortunately, at present there is no obvious locus of responsibility for orchestrating these things. While this is unlikely to be solved in the near term, progress can be achieved on three fronts, which can complement the many initiatives underway on better measurement of the SDGs:
Individual projects in this space have some momentum. But the whole is less than the sum of its parts.
Serious backing from global bodies which sometimes appear to be active in this space, like the WEF and OECD, and more formal commissioning from the highest levels of the UN, would give it significantly more energy, and would also begin the move to a longer-term vision of better orchestration of global know-how.
In this way we can accelerate 21st century solutions to some of our very acute 21st century problems.