Making data meaningful

How can we collect and communicate wellbeing data that’s useful not only to policy makers, but to the people who generate it and the services who support them?

We’re nearing the end of our involvement with OrganiCity, an EU-funded project that asks: how can we create good future cities using digital technology, data, co-creation and experimentation?

Our focus has been on whether it’s possible to use digital technology for long-term data collection in order to track, measure, understand and improve wellbeing.

We’ve been exploring this question alongside parents living in overcrowded homes and the services that support them.

Where are we now?

Through co-design with parents and services we created Squeezy, a community chatbot. Squeezy guides parents to reflect on their daily experiences, links them into an online parent forum, and sends them visual reports of the data they’ve shared.

We’re currently in the ‘develop and test’ phase of our service design process (see below), getting rapid feedback on this chatbot prototype. Over the last six weeks, 30 parents have been testing Squeezy, informing us about the user experience and inputting data so we can test data analysis and visual ways of communicating the data.

Design process FINAL

Our service design process

Headline findings

Prototyping is about discovering through doing what we don’t know when we set out.

Ultimately, we want to measure wellbeing — general, subjective and the drivers that influence it — and people’s attitudes to long-term data collection.

At this stage, our data collection period and sample size are too small to reach conclusive results about what the data tell us. What we do have is proof of concept, and that:

  • a digital tool to collect and communicate data can be accessible, relevant and motivating to parents and services
  • it appears possible to collect data sets over regular time periods that are rich in qualitative and quantitative data, that can inform service development and that can support parents directly.

How we’ve collected data

A chatbot provided a way to interact with parents that was both non-traditional (in terms of data collection) and something they were familiar with (chatting and online messaging).

It gave us regular, direct access to parents and gave them a platform to explain and expand on what affects their personal wellbeing.

Feelings are a powerful door into relationship for parents and services as well as for us with the data, and Squeezy starts every chat checking in on how people are feeling.

It then guides parents to track their feelings towards various factors related to wellbeing, interchanging standardised wellbeing questions (where responses were numbered scales), with questions expecting free text and image responses, and questions expecting parent-defined categorical data (eg emojis, keywords, topics).

Conversations 2

Typical chatbot questions and replies

What the data tell us

We’ve analysed and shared parents’ responses back with them and with services as visual ‘data diaries’. In this way parents’ personal data, their individual and collective experience, sit at the heart of the data collection.

Squeezebot parent data diary

Mock up of a parent’s data diary

Squeezybot dashbord

Mock up of the diary dashboard services receive

The diary gives visual snapshots of how parents have felt over time and why (in their own words). It shows which areas of everyday living preoccupied them and starts to look at how these factors influenced their feelings.

The data diaries include:

Parents’ emotional journey

The journey line chart shows that for the testing period, on a daily basis over time, individuals’ wellbeing stayed the same.

What’s been on parents’ minds and how this made them feel

The topics section shows parents’ preferred topics of discussion during testing (chosen from the ONS wellbeing domains) and whether these contributed to feeling better or worse. For example, initial analysis suggests parents were generally happy with services, and that learning and volunteering are areas to focus on.

We still need to explore why people choose topics. Is it because they want to chat about positive rather than negative experiences?


Focus on topics chosen and how parents felt about them

Parents’ self-assessed overall wellbeing

We were able to collect data that shows a relationship between subjective wellbeing (according to OECD core measures, see p31 of this report) and the variable topics of everyday living.

Our data isn’t sufficient to provide a reliable and significant relationship. However, with more participants and more time, it shows we can determine factors that positively or negatively impact subjective wellbeing. This has huge potential applications for service development.

More thoughts on data

While we’re still at the develop and test stage, we have been able to create a structured dataset that can be easily analysed.

Unfortunately, we’ve not had time to carry out thorough analysis of unstructured data, eg free text and images, and hope to do this in the future.

Here are a few parting thoughts on our data collection.

A future of stories

Asking for emojis and numbered scales correlated well with the tone of parents’ stories.

This suggests that if successful analysis of human sentiment can be carried out in the future (through Artificial Intelligence (AI) and Natural Language Processing (NLP)), we can imagine a world where individuals sharing stories will be enough to infer wellbeing. There would be no need for standardised surveys that may be less likely to be filled out.

The keywords provided by parents to tag their free text matched perfectly with the content of their story. Again, apart from showing we can engage people creating data to do this tagging, it suggests successful keyword extraction using AI could in the future help determine causes of wellbeing.

Combining data sets

Although we didn’t have time to add to parents’ data sets with external data (eg weather, crime rates, social data), we did confirm that this is possible. It would be great to explore the relationship between data which comes directly from parent chats and other open data.

What’s next?

We hope to continue working with our partners to gather more data, from more parents, over longer periods of time.

If we’re able to do so, we need to keep developing all of this — the data collection tool, analysis and visual reporting — with parents and services as co-design partners. This would make sure we’re providing them with the information they need to make decisions about change, individually as well as collectively, on community and service development.

Get in touch

If you’re interested in finding out more about the project and our team, or in collaborating on Squeezy’s development, we’d love to hear from you.

In our next blog post we’ll share perspectives from the team, parents and our partners about what it’s like to collaborate on a co-design process. Visit for all our blog posts.

Blog authors: Emma Field, Eunji Kang, Denise Xifara, Marcin Ignac, Colour-in City

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