26 May The new normals: capturing the learning
What we are learning right now is priceless, but are we capturing it?
writes Ben Lee.
Local public services have been operating at (or even beyond) the limits of their tolerance for more than 80 days, revealing strength points and weak points in ways we rarely see. This learning generated over the past three months has immense value for the future. Where it can be curated and disseminated to inform future policy and practice it will accelerate public service modernisation on a scale dwarfing decades of improvement programmes.
The speed and adaptiveness shown by public services – in shielding the vulnerable, supporting businesses and moving some of their own staff to homeworking – has been astonishing. Challenges which frustrated professionals and communities for years have seemingly melted away.
- Relocating staff of entire sites to remote homeworking over single weekends
- Creating whole new cycle networks and pedestrian zones in days
- Business intelligence teams joining-up multiple resident datasets to identify the vulnerable
- Re-deploying and re-training frontline staff to work on brand-new shielding helpdesks
- Implementing video-calls for regulatory site visits like building regs inspections
- Repurposing public library 3D printers and Makerspaces to produce clinical PPE
There is also learning in what has not worked; laptops that could not be used remotely, resilience plans based on incidents lasting days not months, mailing lists covering only a fraction of local firms.
No-one working today has worked through anything like this and we hope we never do again. That also means we may never have a chance like this to learn from seeing systems at the limits of tolerance. The obvious risk is the pressure of events prevents learning structures being put in place. This is an area in which Shared Intelligence can help. For us ‘best practice’ is an illusory simplification. The best organisations learn as they go, share learning, build on past experience and learn from others. This requires structures to capture lessons, apply action learning principles and foster peer learning.
We understand how learning can be captured through behaviours and with tools. We have been using shared online spaces and collaborative tools to enable elected members and officers to capture and develop ideas. We have used short bursts of action learning in person and online to reflect on what worked or failed, unpick challenges, cement learning into practice, and explain learning to others.
A learning process
There is a real opportunity to use the immediate COVID-19 response to learn for the long term e.g.:
- Lists of small businesses could become new systems for local market intelligence.
- Data-led shielding projects could be the basis for the next generation of adult care.
- Collaborations built in urgency could become goal-orientated partnerships for the future.
We believe a learning framework for local public services can begin with these questions:
- Is space being created to reflect on learning, what is working, and to cement behaviours?
- Is rapidly gathered data (SMEs, shielding, community infrastructure) being used to build lasting systems? Are the individuals doing this being nurtured and given formal roles?
- Are goal-focused joint-working arrangements being crystallised into new forms of governance?
- Are failures at pressure points being used to identify how to strengthen and build resilience?
- Are successes being dismantled to understand how they work and how to replicate?
Pingback:The new normals: not just one new normal.. – Shared IntelligencePosted at 13:34h, 02 June
[…] Also, at the front of our minds is that no-one working today has worked through anything like the crisis we are currently in. We are all learning what to do. Besides patience and humility, most of all this means that any activity we are engaged in is as much about learning, as it is about deciding. We can do this by structuring discussion around action learning principles and enabling those we work with to remain connected as peer learning networks (see our paper on capturing the learning). […]