Data and Design: When the whole is greater than the sum of the parts

Data and Design: When the whole is greater than the sum of the parts

Design projects have a greater impact when designers and data experts work hand in hand.

As researchers and designers, we regularly join forces with other disciplines to create services that have a positive impact on people’s lives. Over the last few years, I have seen how projects increasingly combine data and design, and exceed what each approach could have achieved on its own.

Data as a design material

Data is a design material, just like user interviews and observations. It is traditionally an input to the design process, but also needs to be an output: the new service must generate the data needed to support continuous improvement.

When data feeds into sense and decision-making, we need large volumes of data. When it is an output of design, we pay attention to the granular level, so that the data is accurately defined and captured. This is necessary to make future data sets truly useful.

Here are seven specific ways I’ve seen data-informed design grow in complexity and impact to form into a professional practice in its own right.
1. Design effectively

The Double Diamond invites us to widen our perspective and explore all the potential aspects of the challenge before focusing on the key needs. We can certainly do this with user research and uncover insights that no other method would uncover. However, high-quality user research is resource-intensive. Data-informed design enables us to tighten the user research brief and achieve deeper insights.

Example: What do businesses need from local authorities?

What might we learn from applying, in the first instance, an asset-based approach to defining what we already know about the challenges that businesses encounter? We could consider contact centre data, enquiries, website analytics, social media channels, complaints, and freedom of information requests.

2. Design for inclusion

Understanding the needs of people who experience the greatest barriers to access is a constant challenge for Human-centred design. Data analysis enables us to expand the context of research and place the people we interview in the context of the wider community described in publicly available data sets – census, data sets on health, homelessness, connectivity, housing, and so on.

It is useful to understand how these data sets are compiled. For example, indexes of multiple deprivations vary but generally include the level of unemployment, the proportion of social housing, ownership of a car, all the way to the proximity of a bus stop (which might influence work opportunities), and of a pharmacy (which might impact health).

Design informed by data reminds us of our privilege and changes the nature of our designs.

3. Design for complexity

A successful service is designed for the intricacies of the human experience and the specificities of place. Being outsiders to the user community helps us see things differently and ask new questions that yield fresh insights – but it comes at the risk of overlooking some of the complexities.

Example: How might we help people in rural and suburban areas travel to their GPs?

Mileage doesn’t accurately describe the distance from the GP surgery. A 10-mile car journey isn’t the same experience as a 10-mile journey requiring three buses. The bus stop might be close but on the other side of an underpass that residents don’t feel comfortable using. In this case, we could work with spatial data practitioners to combine maps and data sets representing walkability, transport, and health services.

4. Design for impact

Designers want to deliver services that are valuable to people and organisations. Measuring the value of service design is always complex, but the first thing is to clearly define the results that the project intends to deliver.

The project needs to align its goals to the organisation’s strategy, understand the key performance indicators (KPI) and how they’re reported. As the project progresses, it needs to create the mechanisms to demonstrate that the predicted value is realised and that there is tangible impact.

Before a project can start, stakeholders must set forth an argument for how it is likely to create value and illustrate that it is worth investing in. The project needs to be mindful to validate this hypothesis and create the evidence that will justify the investment in the next phase.

5. Design for continuous improvement

Maybe this blog has convinced you to use more data, so you ask for data sets on users, services, uptake, usage, failures... but you’ll often draw a blank. Even the largest organisations can be data poor to a surprising degree: where the data exists, it can be difficult to extract or link. It might be out of date, poorly defined, or inadequately collected.

Designing a service must include creating the data needed to improve it in the future. It is extremely difficult – if not impossible – to do that after the fact. When the service data is readily available from day one, it can be monitored, allowing for continuous improvement and micro-refinements.

Example: Increase the percentage of users completing an application.

In a project for the Driver and Vehicle Licensing Agency (DVLA), the team noticed 85% of users would drop out at the last step of renewing their car tax. They realised that the text read “You’re nearly finished.” When it was changed to “One more step” people concluded the transaction.

Product Designers are well versed in using data to spot opportunities for improvement, but the same principle needs to be applied at the service level.

6. Data for innovation

Designers and data practitioners can fuel each other’s creativity to create new possibilities:

  • How might we share the data generated to create new services – internal or external?
  • How might we enable users to control how and with whom they share their data, even across organisations?
  • Can we use natural language processing to let people tell their stories in their own words instead of filling in forms?

Some of these examples are already happening, others might be far-fetched, but the point is that we need to work closely across design, data and digital to explore the possibilities.

7. Design a data service

For this to happen, the data needs to be:

  • generated
  • described
  • high quality
  • of a sufficient quantity
  • accessible
  • protected

... and combined with qualitative research so that we understand the what and the why.

Many organisations now turn to user-centred design to create ‘data as a service’. That means making it easy for internal services to share data, design platforms to hold the data and make it available and provide a service to enable other internal services to use the data. Beyond the service operation, the focus is on creating new value propositions.


These seven examples of data-informed design show that meaningful collaborations between designers and data practitioners have been growing over the last few years. Projects are increasing in complexity and impact, but we are still at the beginning of the journey.

There are still avenues that we need to explore going forward. First, develop lessons learned and evaluate the impact of these projects over a longer period. Second, turn individual successful projects into replicable methods so that they can be used widely. These steps are critical if we want to keep innovating.

We need to continue learning from one another. Were you part of a project that combined data and design? Did it achieve the impact it intended to have or was it a train crash? What did you learn? It’d be good to hear other people’s stories.