Engaging effectively with data users

A dynamic relationship ensures value is realised for everyone involved, and that there is transparency and openness within a productive open data ecosystem

The Communication Loop

There is, ideally, a dynamic relationship between data publishers and data users. A dynamic relationship ensures value is realised for everyone involved, and that there is transparency and openness within a productive open data ecosystem.

The communication loop describes the productive two-way relationship necessary to fully understand what each party requires from open data publishing, and to facilitate the best ways to meet identified needs. This goes beyond simple feedback on likes and dislikes, but involves meaningful engagement with the aims of improving, collaborating and innovating.

The potential to enhance the value of data and the potential to enhance the value of relationships can be realised through more effective engagement between data publishers and data users. Similarly, there is ‘shared value’ to be gained through working towards a common goal or having similar ambitions in addressing a common issue, such as improving child wellbeing in an area or reducing carbon emissions, for example.

Do you have a relationship with your data users? How would you describe this relationship? Is there potential for increased shared value?

Suggestions for meaningful engagement at different points in the user journey

1. Co-production of analytical innovation >> discussing novel ways to apply data at the beginning of a project or new data initiative.

2. Building in feedback sessions at strategic points >> such as sense-check after analysis of data or a review prior to publication of a report.

3. Follow-up once a data project has ended >> for the purpose of sharing 'lessons learned' at the end of a project (or within an agreed timeframe).

4. Data showcasing sessions >> where data users come together to demonstrate how data was utilised, for example highlighting which reports, articles and publications the data contributed to, or what kind of policy decisions were influenced.