scaling and data — a more relational framing

In this learning note we introduce an alternative approach to scaling and data that we have found really useful in our work; thinking in terms of patterns of relationships.

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scaling and data — a more relational framing
Problematising Scale in the Social Sector [Source: Gord Tulloch, 2018]
Source: Graham Leicester & Maureen O’Hara, (2009) Ten Good Things to Do in a Conceptual Emergency (p13)

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 and 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.”