The development sector really does manage to get its knickers in a knot at times. While agreeing that ‘it’s all rather complex’, they continue to hold on like shipwrecked sailors to linear models of planning, monitoring and evaluating, scratching their heads about why ‘it isn’t working’. And the sharks circle ever closer –dissatisfied donors who want to see value for money, frustrated activists having to provide data, overworked staff trying to chase up and make sense of the information that does trickle in. Innovation is not allowed to fail and indicators must be set years ahead of time that guarantee ‘poverty alleviation’ and ‘citizenship strengthening’ impacts. The sector partly needs to shake off unrealistically ambitious expectations. But it needs to start understanding more fundamentally the nature of the beast with which they are dealing.
Since attending the CE London April course, I’ve been airing the Cynefin framework among development organisations in the UK and the Netherlands. I reckon bringing across the notion of ‘multi-ontological sense-making’ and implications for learning is particularly important. I’ve discussed narratives and tagging with those managing diverse funding portfolios at their wits end to ‘make sense’ of such messy stuff, sketched the framework on napkins over dinner, on flipcharts and in reports. And people are keen. Very keen. One example, an NGO supports work on the whole power / equity / democracy / governance / advocacy gamut of political change (small ‘p’ of course, just in case the Charity Commission is reading this blog). They were intrigued by the possibility to justify why pre-set indicators may not be useful for some of the innovative advocacy work that it supports in conflict-rife contexts. High levels of ambiguity, dynamic constellations of partners who come and go, trial and error with strategies makes linear planning an impossibility. So perhaps standardised indicators across the global was a problem for very good reasons! Recognising innate features of the different domains – and the consequences of this, is like seeing a cloud lifting on the faces of some with whom I’ve talked. Finally something that seems to make sense …
What we need now, though, is practical examples, stories to share about how narratives have helped pick up on weak signals and helped make sense of wildly diverse portfolios of funding, about how to understand which parts of the work are complex and which are not, about planning for action in different domains, on integrating learning strategies that recognise the differences, and more. One recurring question has been ‘But how do I recognise when something is complex? What is a useful definition?’. Maybe that’s a good place for me to start with making it practical. All suggestions welcome
Learning by Design
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