Charities, change agents and the diffusion of data-driven practice

Diffusion is the process in which an innovation is communicated through certain channels over time among the members of a social system” Everett M. Rogers.

In looking at how charities use spatial data, the majority of cases I’ve explored so far have been large charities that could be classed as ‘innovators’ or ‘early adopters’ of data and technology. Therefore, it was refreshing to attend the launch of Superhighways and partners’ Datawise London programme, a two-year offer of training, advice and practical support for small charities in London to develop data and digital skills.

It got me thinking about how ideas spread and the innovation adoption curve, developed by the sociologist Everett M. Rogers and based on wide-ranging studies of innovation adoption. The blue represents categories of adopters, and the yellow overall market share.

Rogers calls groups like Superhighways, DataKind UK and CAST ‘change agents’, as they are seeking to influence and assist with the adoption of new ideas and working practices. If change agents are focusing on encouraging small charities to change their practice, and small charities make up 82% of the registered sector (NCVO, 2019), then it could be rather crudely suggested that the small charities seeking support sit in the ‘early majority’ group of adopters. This is exciting, as at this point market share typically begins to rapidly take off and data-driven practice becomes the norm.

But why do organisations adopt ‘new’ practices? What should change agents include in their programmes to encourage a greater rate of adoption? Rogers outlines five characteristics that are fundamental to the adoption of innovations:

– Relative advantage
– Compatibility
– Complexity
– Trialability
– Observability

Programmes such as Datawise London demonstrate elements of all 5:

Relative Advantage
Organisations must be convinced that new ways of working are better than current practice. With the promise of big data and the fear of being left behind, there is often an appeal to the unattainable ‘data imaginary‘ to encourage data-led thinking. The Datawise programme avoids this by using case studies of previous success stories to demonstrate the benefit of a new way of working. Of course, there is still scope for imagining the possible, but demonstrable uses for data show realised rather than potential change.

Compatibility
New ideas must be perceived to fit with the needs, capacity and values of organisations. In addition to observing fellow organisations with similar value-sets use data in new ways, the training and assistance offered by Superhighways and partners allows organisations to increase their capacity to a level that is suitable for each organisation. Having the HEAR network as a partner ensures data use doesn’t conflict with the accessibility needs of staff, volunteers or service users.

Complexity
Ideas that are simple to understand are adopted quickly, is Rogers’ point. Datawise will reference the data maturity framework created by Data Orchard, to assess an organisation’s maturity and adjust the complexity to suit them, while incrementally increasing data maturity in manageable stages.

Trialability
Trialability removes the uncertainty in adopting new data practices, and Datawise’s introductory workshops expose organisations to new ways of working before sunk costs occur.

Observability
Seeing similar organisations adopt innovative and new data practice is a fundamental part in not only assessing relative advantage, but for developing opportunities for peer-to-peer learning and developing communities of practice. By developing public case studies of Datawise success stories, the programme fosters innovation beyond the immediate workshops and training sessions.

Datawise is funded by the City Bridge Trust as part of the Cornerstone Fund, which is seeking to facilitate long-term systems change in infrastructure support for London’s civil society organisations. The Cornerstone Fund’s outcomes framework and the Datawise programme speak to all of Rogers’ five necessary characteristics for the adoption of innovations and pave the way for greater data use by small charities.

Note: this blog is independent of and not in any way affiliated to Superhighways or the Datawise London programme.

Further reading:
Diffusion of Innovations, Everett M.Rogers. 3rd edn.

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