Any idea or hunch we have is informed by our own experiences and our own biases
When it comes to working in complex local contexts, it’s safer to assume that our assumptions are more likely to be wrong than right – or at least wrong enough to stop things working out as we would like.
A good innovation process will help you rapidly test your assumptions in a cost effective way. People think of innovation as wild and crazy, but a good innovation process is actually a good risk mitigation process. A process that spots mistakes early, before time and money is invested in implementing things that were never going to work.
In the office / out of the office
Practically, testing assumptions means looping between ‘in the office’ and ‘out of the office’ work. ‘In the office’ we name and frame assumptions. Then we go ‘out of the office’ to test them in the real world with real community members. Design research methods and prototyping are two frequently used approaches to testing assumptions.
The more times we test and reframe our assumptions through real-world input, the more likely it will be that our assumptions fit reality. The more likely it will be that we have an accurate understanding of community life and what will enable change in the community.
Three important assumptions for place-based social innovation
When you are seeking to create change through place-based initiatives, there are three sets of important assumptions.
Assumptions about people, their lives now and what people want for their lives in the future.
Assumptions about the supports and services that will create change for people.
Assumptions about the broader system and how place-based initiatives will shift that system to work more effectively.
When we say that ‘young people want local jobs’, we’re making assumptions at the people level.
When we say ‘early interventions services will support local families’ we’re making an assumption at the service and support level.
When we say that ‘collective impact is the best approach to create change in our community’ we’re making an assumption at the systems level.
Assumptions may be wrong or right, and more often than not they are a bit of both. Assumptions may be informed by evidence of different kinds (research evidence, data, practice wisdom, lived experience) or they may be intuitive. We have to make assumptions; there is no progress without them. A good innovation approach will help by quickly identifying what’s wrong or right about them, avoiding investment into assumptions that don’t match reality.
Tools for testing assumptions
Handily, for each of the three sets of assumptions outlined above there is a corresponding set of tools to help you test them.
Tools from design research are particularly effective for testing assumptions about people’s lives and how services and supports work for them.
Methods from service design, including prototyping, can help with visioning and testing future service models.
Systems thinking tools can help you model systems (current and future) and identify effective leverage points.
Tools designed for start-ups can also help you test assumptions about what model of initiative will work for you.