scaling and data — a more relational framing
Firstly, some context on our way of learning and working out loud
In detectorism insights#1 we collected the stories, experiences and wisdom of doers and encouragers in Dudley that are forging a different social space, civic experiences, and sense of community. We presented a cultural portrait that is rooted in welcome, affirmation, sharing, creativity and empathy. We looked for patterns in the stories, artifacts and reflections shared. We wanted to learn together what it was about the space, the mindsets, the behaviours and the design of experiences that meant deep connections, creative action and a rich sense of place was emerging in gather and its community.
Detectorism Insights #2 will be more of a distributed compendium of learning notes and artifacts relating to the journeys of doers in different projects. In addition we will link these notes to brief explainers about tools (practical and conceptual) we are using to better understand those journeys and their collective and cumulative consequences.
This learning note is about scaling and data
In this learning note we want to introduce an alternative approach to scaling and data that we have found really useful in our work.
As a society when we think about scaling impact in relation to social change we have been trained to think in terms of numbers or their proxies. Our way of evidencing even the most complex emotions — like happiness — is now measured in numbers.
In this quantitative led framing we are hard wired to reward, fund, design for, and evaluate scaling successfully as more and bigger.
As a social lab rooted in participatory and ethnographic learning we have always wondered out loud “are there limitations to what these numbers tell us about the depth of socio-cultural impact, or the resilience of that impact?” Indeed: “what does prioritising a quantitative framing at the expense of other data obscure in our critical analysis of and design for socio-cultural change?” “Is there a risk in our rush to focus upon growing numbers that we default to passive monitoring rather than shared and iterative learning?”
The lessons of habitually resorting to numbers for scaling can be witnessed all around us. An obsession with framing scaling global growth in terms of increased GDP has had a devastating effect on our design for a healthier and more equal world. An alternative approach to scaling is needed in all we do, and for us that includes our work as a platform for a kinder, more creative & connected Dudley High Street.
Thinking in terms of patterns of relationships
What if in addition to the straight forward ‘more and bigger’ metrics our scaling was informed by qualitative and relational data? Through the weaving of different data sources together and seeking to understand how they relate we hope to help reveal a picture of inter-connection not separation. Gregory Bateson argued it is our failure to see the inter-dependencies and relations within our system that means all too often today’s solutions become tomorrow’s problems. By not seeing these relationships and connections we — unintentionally — break them. If the pathology is in the pattern of relationships that follow we need urgently to embrace a narrative of connection. [Bateson’s articulation of our pathology of wrong thinking and the double bind we face as a civilisation is beautifully presented by his daughter Nora Bateson in her video portrait An Ecology of Mind]
Nora Bateson advocates a practical response to this pathology of wrong thinking through being and designing in this world informed by the ‘warming up of data’. She describes ‘warm data’ as a data typology that is “(trans)contextual information about the interrelationships that integrate a complex system.”
We are striving to take these lessons onboard and are being even more intentional in using a relational and contextualised approach to data — in terms of what we view as data, the reason for collecting it, how and where we collect data, how we analyse, visualise and share it with a focus upon relationships. Through this relational approach we have begun to observe and sense a much richer story of patterns of inter-connectivity between doers, events, behaviours over time, system structures, and the mental models that shape those structures.
Scaling but not as you know it
In terms of the ‘reason for collecting the data’ we have lent heavily on the insights and provocations of Gord Tulloch in adopting an approach to scaling that resonates with this relational framing. Gord talks about 5 different types of scaling:
- Scaling Up (impacting upon laws and policies)
- Scaling Out (impacting greater numbers)
- Scaling Deep (impacting cultural roots)
- Scree Scaling (impacting norms and expectations)
- Scaling Initial Conditions (impacting infrastructure)
Gord explains that for the most part social innovation has historically prioritised scaling up and scaling out .
“Scaling-up and out are often confused and the terms used interchangeably, probably because they are fundamentally about spreading or growing solutions in order to bring social benefit to as many people as possible; they are about numbers. Whereas these forms of scale dominate the social innovation literature, little is comparatively said about scaling-deep. Scaling-deep recognizes that there is power in transforming culture. It acknowledges that interventions at the level of meaning and culture can prove powerful axes for levering change. Sadly, there are relatively few solutions that are attempted in this space and even fewer of which are understood by funders or investors.” (Gord Tulloch @ InWithForward)
He argues plainly that this focus has resulted in a social change blind spot.
Conditions for an intervention can never be the same in any given time or place. Social challenges are the product of highly dynamic and context specific forces. Therefore, replication of interventions within complex systems are destined to generate differing and often unintended outcomes.
This is not an argument to ignore scaling up and out — it is not a binary debate. Rather, we feel we have been encouraged to expand the aperture of scaling approaches prioritised in our design of experiences, spaces, projects and the platform itself.
Taking on this alternative approach we now use our data to consider a more expansive treatment of scaling beyond ‘up and out’ by also paying attention to: the conditions needed for change; scaling deep for shifts in cultural values and norms; and the quality of connections and flows across many different smaller activities within the ecosystem that contribute to that shift.
This extended framing of scaling with its many layered insights is a valuable resource in the messy and uncertain work of place based change. In particular we have been interested in how our work is scaling deep. Gord describes scaling deep as:
“ … activations intended to promote transformation at the sociocultural level of individuals, organizations or communities.”
We have been using the Iceberg Model in helping us with our systems mapping and thinking about scaling deep in our lab work. This involves paying attention to the stuff below the surface, (like patterns of behaviour, system structures and mental models) within our team, community and further afield. Systems thinking tells us that by impacting upon mental models the change affected is potentially more transformative. [We will be sharing a learning note on the way we are using the Iceberg Model in thinking about scaling deep in the next week or so.]
Two additional forms of scaling Gord argues are useful in terms of supporting systems change are: scree scaling and scaling initial conditions.
Scree scaling, in particular, is also related to shifting norms. As a community has more and more members advocating and modelling certain mindsets the more likely the system as a whole will adopt those ways of seeing the world. As a social lab and platform these types of scaling are of particular relevance to us and we will be sharing learning notes on examples of how they are playing out in our work over the next month.
“Scree scaling: This conception of scale is less about growing and spreading single solutions and more about legitimizing and cultivating many “small” ones. It represents the view that system change is less likely to occur as a result of a few big ideas than by the accumulation of many little ones.” (Gord Tulloch @ InWithForward)
“Scaling initial conditions. Within the private sector there are a range of public and private mechanisms to support and scale innovation — access to capital, data, talent and connectivity (knowledge dissemination and networking). None of this infrastructure exists within the social sector, or at least, not in any sort of coherent way. If we want to see a verdant proliferation of solutions, and if we want to see them succeed, we need to attend to the ground that gives birth to them and that nourishes them.” (Gord Tulloch @ InWithForward)
We have noticed in applying this approach it is important to stress the inter-relationship between these different forms of scaling. For example, both scree scaling and scaling for initial conditions feed and in turn are fed by scaling deep actions and consequences.
Practical and conceptual tools like this expanded approach to scaling and warming up of data have revealed a raft of new perspectives that both enrich and challenge our existing insights. They continue to raise valuable questions for us as lab such as:
- How do we hold all these different aspects of scaling in our planning?
- How are we observing/ paying attention to the inter-relationship between these different aspects of scaling?
- Should we be prioritising one aspect over an another? Perhaps at different times?
- What mindsets help support this more layered view of scaling?
Look out for lab notes soon on crafternoon and festival detectorism where we explore scaling deep through shared learning sessions with doers and creatives in our community regarding cultural norms and values.
Top tips:
- To learn more about these 5 types of scaling read Gord Tulloch’s blogs (p.s. thanks Gord for letting us use the image of 5 types of scaling).
- To learn more about warm data read Nora Bateson’s blog
- To learn more about the Iceberg Model and other systems tools dive into the Donella Meadows Project
- Beautiful and user friendly advice for being in this more relational framing of the world by Graham Leicester & Maureen O’Hara
