We recently held our third Interactions virtual meetup and heard from three speakers on the intersections of design and data. There are so many places where design and data meet in digital, and our panel had some fantastic practical examples of this.
Our first speaker was Emily Blanford, who leads the information design practice for Mudano, a data consultancy focused on innovation and cultural change.
Data visualisation for decision-making – Emily Blanford
Emily spoke about using a design process to improve data visualisation for decision making. Firstly, she explained the two main categories for data visualisation. The first is ‘storytelling with data’, using either static or interactive graphics to tell a specific story with a message. For example, during the Covid-19 pandemic, we’ve all become familiar with the idea of ‘flattening the curve’ through the data journalism presented to us.
The second category is ‘data visualisation for decision-making’. The difference between this and ‘storytelling’ is the variability of the data sets. You might understand them today, but by tomorrow these data sets could tell a completely different story. We need to be able to analyse the messages without having to start again from scratch every time, which can be very frustrating. There may be specific questions you need the answer to or a hypothesis you are checking. The answers provided by this kind of data analysis will inform your operational decisions.
Data visualisation is the communication tool that we use to extract the information we need from data sets. However, sometimes there is too much focus on the data visualisation itself and people forget about the actual users! It’s crucial to remember why you’re making the data available and who it’s aimed at. Making visualisations is fun, but it’s also a challenge, as it involves taking complex information and working out how to bind it all together in a way that makes sense.
The design process can really help you avoid getting bogged down in the data details. Start with understanding the situation, gathering context and coming up with ideas to address the specific pain points users have. Then try new ideas out, testing to see what actions work best and how people respond to them. This is a good example of what the Design Council calls ‘divergent and convergent thinking’: opening up to new ideas during the research phase and then narrowing down and prioritising once you understand what will work best.
It’s not a linear process and there is the freedom to experiment. Prototyping is a great way to understand what people will use and how they’ll interact. With well-designed data visualisation, the user doesn’t have to work hard to understand what they want from a particular chart. For example, in one marketing campaign, you might want to make sure you are looking at where a product is being sold and match it up with warehouse capacity in a particular location to make sure that you are making efficient use of space, rather than looking at a chart that just shows you the typical ‘sales over time’ information.
Using a dashboard, or another kind of analytical tool, can give you an overview but also allow you to delve into the details of why something might be happening. However, there are situations where people just don’t use the dashboard. It’s important to dig into why it’s not being used and find a solution. For example, training people to interpret the data, improving their access to the dashboard, or changing the functionality.
So, observe and research the users, find out their motivations, understand their daily schedule, what decisions they need to make, and what forums they use to make those decisions. Then design a tool that sits easily within all these processes and enables them to get value from the data.
Are we design or are we data? – Dominic Hurst
Known as “the data guy”, Dominic Hurst is a senior designer and user researcher at Infinity Works, who has worked in digital for more than 20 years.
Dominic applauds the move away from top-down decision-making. The problem with this ‘stakeholders-know-best’ business model was a lack of delegation resulting in a single point of failure. This is why design and beta thinking are so important. The synergy between the designers and those people who will use that design in real life are vital. There is no such thing as global best practice, there is only your own best practice. Design frameworks allow businesses to be agile and adapt, spinning off different designs in response to the changing needs of their users.
Of course, brand has an impact on design, but take a deep dive into data – it’s transactional and very measurable. You can analyse details such as sales over time, website visits and conversion rates. This informs the setting of KPIs (key performance indicators) and OKRs (objectives and key results). One thing to bear in mind is that data is complex and there are also different types of data. No business has a fully accurate picture of what’s going on. For example, a user might start off a website journey on their mobile phone, go into the office and onto their desktop and then end up back home on their laptop. Many businesses will not connect these journeys together and so they make decisions based solely on where the sale took place, forgetting the journey leading up to it. If we don’t connect those data sets, we could make bad decisions.
Dominic asked: are we design or are we data? Is data informing our design or do we design to create data? There’s no right answer, but whatever you decide will have a big impact on your team structure, as it informs whether a business invests in designers or data scientists. In today’s market, we have tool overload. If you invest in data, you also must invest in the people resource behind it. Business goals and objectives must not be at odds with the user. Instead, focus on how you can turn data into meaningful feedback, or what Dominic calls ‘sentiment analysis’, which can then inform what the design team focuses their efforts on next.
Understanding the ‘CRM apocalypse’ – David Mannheim
Our final speaker was David Mannheim, who founded one of the UK’s largest independent conversion optimisation consultancies. He is now Global VP of (CRO) conversion rate optimisation at Brain Labs.
David predicts that CRO will evolve, or is already evolving, into something else. To prevent, or even to just accept, the ‘CRO apocalypse’, we need to understand some myths about the laws of conversion optimisation.
Digging into analytics in order to understand opportunities is a complicated task. Sometimes it is the smallest of tweaks that can be disproportionately impactful, for example changing a checkbox to a radio button creating $500 million of extra revenue.
Testing nuances really does matter, as demonstrated by Google testing 41 shades of blue before deciding on the best option. However, most businesses are not Google, Facebook, Uber or Netflix and the thing to remember is that the purpose of optimisation is to create a change in either behaviour or attitude. The variable that really matters is the one that will change a user’s motivation, rather than one that will simply improve useability.
CRO managers should have that perfect combination of design and data as a skill set but this doesn’t often exist in one individual. Instead, those in design and those in data need to work together, which is better than having a single individual responsible for both.