Data and design blog #3: a design wrap around for data projects to achieve impact
This is the final blog in a series of three about how design can support the effective delivery of data projects. The first focused on how design could help communicate what data opportunities are (and encourage people to take them up), and the second showed how design ensures that there is a real user need or social focus, rather than the data being the end in itself. This final blog reflects on both the data projects that Uscreates have worked on over the last six months (around open data, creating interfaces to relay real-time data monitoring and blockchain possibilities) together with examples from other agencies to show how design can ‘wrap around’ the analysis part of the data project.
In service design, we think about journeys. A data project is not just about analysing data. Different stages in the project journey are:
1) raising awareness about what data and computer techniques are
2) collecting the data
3) analysing the data
4) making sure the insight effectively turns into a decision/action that delivers impact
5) ensuring continued oversight about the process.
These are all ‘touchpoints’ between people (citizens, policymakers or service providers) and the data project. We have seen how design can help communicate what data is, but it can also help create experiences around data collection or use of insight that make the project more effective (blog #4 to be published):
The act of generating and providing data can be seen as a ‘touchpoint’ between a citizen and an organisation. Clearly, consent is an important consideration during that interaction. Legally, consent is not required in all circumstances (see the Information Commissioners’ blog for when it is and isn’t), but it often is and the recently in force GDPR means that many organisations are considering how they can be compliant and go beyond a standard question with a tick-box answer. But — with good design — the act of generating and providing data can achieve wider social outcomes than ensuring legal compliance. For example, it can:
- Build trust. There are many ways of recording explicit consent. ‘Projects by IF’ has drawn together a highly-visual catalogue of these methods. Setting out clearly at the point of data collection how consent will be given and how the data is going to be used for social good is important in raising public acceptance.
- Nudge behaviour. Service design often uses behaviour change to achieve social outcomes. Self-awareness is an important principle here. Providing people with data they have generated — either consciously (e.g. responding to a question) or unconsciously (e.g. data generated through using Fitbits or accessing apps) — can make them aware of their behaviour and nudge them to change it. For example, Mappiness is a research tool that collects data on wellbeing at work, and through doing so is generating the largest dataset on work-based health at the same time as activating behaviour change in participants. Also, online research with Doncaster residents not only elicited insight into early health prevention, but indicated a change to healthy behaviours among 55% of those most engaged.
- Engender a sense of collective empowerment. Consider the experiences of reporting sexual assault to the police and writing a #MeToo social media post. Clearly these are different reporting mechanisms which lead to different paths of action and legal remedies, but the former can often be a disempowering experience, leading to low prosecution rates by the criminal justice system, while the latter has anecdotally been reported as a more empowering and collective experience, and has been effective in leading societal conversations about the need for change.
The data project does not end with the production of impact. In order to meet a user need, or create social good, the insight must prompt a decision or action that leads to a positive outcome. This is important because research shows that people’s acceptance of data projects (especially where there are concerns around privacy and intrusion) is based on the level of social good. Service design can help data scientists and policymakers think through how the data or insight will be used to achieve the intended impact.
- Determine where along ‘data value chain’. The value of a data project is that an action — a decision, a change in behaviour, etc. — will be taken as a result. This action could be taken by a policymaker, a frontline member of staff, a service user, or a citizen. Different people will need the data to be presented in different ways along a ‘data value chain’: raw data; information grouped into charts and graphs; insight, knowledge about the overall issue. Our work with nuron identified a number of ‘personas’ (pen portraits of people in different roles across a water network) and designed interfaces that provided them with data/information/insight in different ways. For example, a planning analyst might want to access the data to perform secondary analysis (raw data) whereas a a customer might want knowledge that their water company’s track record of service was excellent (knowledge).
- Co-design with frontline staff and users. As well as with users, it is extremely valuable to co-design how the insight will be used with those who might be delivering the service. In debates around data, there is some concern being expressed regarding predictive modelling, computer-led decision-making and their impact on jobs — that these will automate jobs, or that open data will replace the need for intermediary advice. Hartford, one of the Champion Cities in the 2018 Bloomberg Mayor’s Challenge is thinking carefully about how its idea (to link up gunshot data with schools data to identify those who might have indirectly experience gun violence) would be delivered, and the important role of teachers as intermediaries in receiving that data and approaching identified pupils.
- Prototype and test. Service design uses prototyping to mock up solutions, get feedback, spot errors early and improve its chances of achieving the intended outcome. During our Doncaster work on democratising career advice data, we created a data prototype (to test quickly what was technically possible) and a wireframe (to test with users what was desirable and useful), both which helped us refine the idea. More speculative uses of prototyping can be used to help people imagine transformation solutions in a safe space. One of the risks around data, is ‘opportunity cost’ of not proceeding with potentially socially valuable work because of overall public concerns or public sector lack of confidence about data and associated techniques. Our Blockchain project created a prototype for a blood transfusion drone in order to give a tangible example of how distributed ledgers could provide a secure, automated blood transfusion service, and to create a proactive, confident conversation about it.
If you would like to help us develop our thinking on the data project journey, we’d love to hear from you. Or if you want to think through how you are collecting data or using data in a way that drives the right behaviours among customers, patients or users, please do get in touch.