In this section, we reflect on developing the SERT and summarise what worked well, and what needs further development in the future.

Building collaboratively

A key platform of our approach to building SERT was a strong commitment to learning from a range of key stakeholders. For future work, the following elements worked well for SERT:

  • Early and frequent engagement with a range of stakeholders.
  • Prototyping is a highly valuable process and enables interested stakeholders to give you honest feedback.
  • Be open to this feedback: if you’re not listening to the end-users, then it is unlikely you’ll produce a tool that will satisfy a good number of their needs.

Designing to capture ‘everything’

As SERT began to take shape, we had to make a very clear decision about what measures were ‘in’ and ‘out’ of scope. Naturally, it would be great to develop a tool to measure everything, but as the recommendations for government show, this task is fraught with challenges. Some of the lessons here include:

  • Simplify at all times: this helps to keep the design of your tool coherent and user friendly. Always make the webtool in accordance with accessibility requirements too.
  • If measuring, where possible use validated measures, ideally backed by rigorous research.
  • Don’t over-promise: very early on, we made a call to keep the social impact element of SERT to a very basic level. This downplays SERT’s usefulness as a social impact measurement tool, but focuses its utility more on simple business reporting, clarifying its point of difference.

Handling the data

Being able to easily capture and represent your data has become an accepted norm for webtool users. The availability of relatively inexpensive web platforms, data analysis tools and expertise has pushed this expectation further. We found that visualisation is important, however:

  • Build data privacy and security into the heart of everything about the webtool. We did this from the outset and have been very clear about what we can promise in this regard.
  • It is very easy to ‘over-do’ data visualisation. Consider the audiences and users of this data; not all people will find all the data interesting.