Innovation scaling: It’s not replication. It’s seeing in 3D
BY Gina Lucarelli | September 12, 2018|Comments 0
My brother is a mathematician and on family vacations, he talks about data in multi-dimensions. (Commence eyes-glazing over). But as the family genius, he’s probably on to something.
Lately, in my own world where I try to scale innovation in the UN to advance sustainable development, I am also thinking in 3D, or, if properly caffeinated, multi-dimensionally. As new methods, instruments, actors, mutants and data are starting to transform how the UN advances sustainable development, the engaged manager asks: when and how will this scale? To scale, we need to know what we are aiming for. This blog explores the idea that innovation scaling is more about connecting experiments than the pursuit of homogeneous replications.
Moving on from industrial models of scaling innovation
In the social sector, the scaling question makes us nervous because the image of scaling is often a one dimensional, industrial one: let’s replicate the use of this technology, tool or method in a different place and that means we’ve scaled. This gives us social development people pause not only because we can’t ever fully replicate [anything] across multiple moving elements across economic, social and culture. Even if we could replicate, it would dooms us to measuring scaling by counting the repeated application of one innovation in many places.
Thankfully, people like Gord Tulloch have given us a thoughtful scaling series that questions the idea that scaling social innovation is about replicating single big ideas many times over. [Hint: he says scaling innovation in the public sector is less about copy-pasting big ideas and more about legitimizing and cultivating many “small” solutions and focusing on transforming cultures.] Apolitical’s spotlight series on scaling social impact includes a related insightful conclusion: when looking at Bangladesh’s Graduation Approach as one of the few proven ways out of poverty, they suggest that while the personalized solutions work best, they might be replicable, but too bespoke to scale.
So if scaling ≠ only replication, how do we strategize for scale? I’ve got a proposal: what if we frame the innovation scaling question more about doing deep than broad? The scaling question becomes: How will we move from distinct prototypes managed by different teams at the frontier of our work to a coherent, connected use of emergent experiments in programme operations?
Scaling also means moving from fringe to core
Scaling innovation in a large organization like the UN has a glorious serendipity to it. Did you hear that we are looking into impact bonds in Armenia? What about the food security predictor in Indonesia? Nice collective intelligence approach in Lesotho. Blockchain is being used for cash transfers in Pakistan and Jordan. Check out the foresight in Mauritius. UNICEF is using Machine learning to track rights enshrined in constitutions. UNHCR is using it to predict migration in Somalia. UNDP is testing out social impact bonds for road safety in Montenegro. These organic innovations are beautiful and varied and keep us learning, but we as a UN system are not yet scaling in 3D.
These days, I’ve been talking to people (my brother’s eyes glaze over at this point) about how to see various methods of innovations not as distinct categories of experiments, but rather as connected elements of an emergent way of doing development. Towards a connected kind of 3D. Yes innovation is more of an evolving set of disruptions than a fixed taxonomy of new methods, but if we narrow our scope for a moment to the subset of innovations which have passed the proof of concept stage, can we start thinking seriously about how they connect? [As an important side note, thinking in terms of taxonomies of innovations is not a panacea. Check out @gquagiotto’s slides for a more thorough story on how classification is trouble for public sector innovation because it means we limit our vision and don’t see unexpected futures where they are already among us.]
Projectizing innovation without keeping an eye on the links among the new stuff won’t get us far, and might even be counter-productive. Instead, what would it be like if innovations were deployed in an integrated way? A bit like Armenia’s SDG innovation lab where behavioral insights, innovative finance, crowd-sourced solutions and predictive analytics [among others] are seen as a package deal. I am looking for collaborators to learn more about how are all these methods and tools related. Do they help or hinder each other? Are there lessons that can be learned from one area and applied to others? Should some new tech and methods not be combined with others?
9 elements of next practices in development work
A few of us UN experimenters came together in Beirut in July to pool what we know on this. We had a pretty awesome team of mentors and UN innovators from 22 countries. We framed our reflections around the 9 elements of innovation which I see as approaching critical mass in the field. This is by no means exhaustive, but it’s a start to moving these methods from fringe tests led by various teams to core, connected operations.
Here are the “nine elements of next practice UN” we are working with:
- Tapping into ethnography, citizen science and amped up participation for collective intelligence to increase the accuracy, creativity, responsiveness and accountability of investments for sustainable development.
- Using art, data, technology, science fiction and participatory foresight methods to overcome short-termism and make sustainable futures tangible.
- Complementing household survey methods with real time data and predictive analytics to see emerging risks and opportunities and design programmes and policies based on preparedness and prevention.
- Building on the utility of “superman dashboards” for decision makers to helping real people use their own data for empowerment, entrepreneurship and accountability.
- Leveraging finance beyond ODA and public budgets by finding ways to attract private capital to sustainable development.
- Evolving the way we do things and even what services we offer by managing operations through new technologies
- Applying psychology and neuroscience for behavioral insights to question assumptions, design better campaigns and programmes and to generate evidence of impact when it comes to people’s behavior.
- Carving out space for science and technology partnerships within the UN’s sustainable development work
- Improving how we support our national partners in managing privacy and ethical risks
Moving from “that’s cool” to “aha it’s all connected”
We need to start thinking of these 9 elements as connected. It might be that they reinforce each other – whereby focusing on data empowerment gives meaning, context and legitimacy to the use of big data to understand behaviors and online activity. Or that they undermine each other – in the way that citizen science can undermine innovative finance pay-outs, or behavioral insights are helping companies get around privacy regulations.
Looking for the practical connections, here’s what we’ve got so far:
- Collective intelligence methods that listen to people organically can help determine whether your behavioral campaigns are resonating. Because people’s intell is often more granular than statistics, they could also be used to test whether new forms of finance are making an impact on health, education and other development issues.
- Small scale and/or internal experiments in the UN to manage operations with new technology help us know what the next generation privacy and ethics risks are. Experiments in gray zones can then inform future-oriented regulatory frameworks.
- Keeping a focus on helping people use data for empowerment is a good northstar when using new data and predictive analytics to ensure that cultivating realtime sources of data isn’t deepening the digital or data privacy divide.
- Using foresight methods or predictive analytics can point to signals of where to invest with innovative finance instruments [Follow Ramya from IFRC innovations for more on this.
Hence some early connections form a budding conspiracy theory!
If you are thinking multi-dimensionally too, or using a few of these methods and see where this line of thinking can be improved, help me draw more lines on the innovation conspiracy board! [Or tell me why this is the wrong tree to be barking towards… That’s always helpful too.]
We’re working on a playbook to codify what we know so far in terms of principles and methods for each of these 9 elements. Stay tuned for that… and please do get in touch to throw your own knowledge in!
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- Innovation scaling: It’s not replication. It’s seeing in 3D
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