Data has killed the Designer – Or has it?

Data-driven decision-making is great because it helps us remove bias. Instead of listening to the Highest Paid Person’s Opinion, data-driven decision-making is based on what the users are actually doing and exposes the product or service to the cold reality of data. Being actually data-driven, not just data-inspired, means building actionable metrics that measure the actual business outcomes. Always seek to find evidence that is contrary to your beliefs – trust no intuition before you can verify it!

As we approach the 2020’s, we have an unprecedented ability to generate data from activities, transactions and interactions. Powerful AI solutions can then use that data to automatically generate variations of interfaces, optimize inventories, balance server load and so much more. When a genetic algorithm can independently explore and test hundreds of alternative and choose the ones that are proven to work, who needs a designer anymore?

Clearly data helps us optimize our systems based on their results,

but how do you know what to optimize for?

This is where design and data meet in an interesting way. Right now, any designer can already start using data to create smart defaults, determine what kinds of features are worth building, and track the actual journeys our customers are taking. As the level of automation increases, the designer’s role becomes that of a gardener: What is the customer experience you want to optimize for? What is the tone this AI assistant should deliver news we can’t imagine yet?

Zalando presented a really useful model for understanding the relationship between data, intelligence and experience. Let’s say you’re building a smart service that helps grocery shoppers discover suitable recipes and gather them at their local grocery store. As the customer walks out of the store with flour, milk, sugar and eggs, data is being generated. Based on the ingredients and the customer’s history, a solution’s intelligence layer can draw a conclusion that this person is baking a cake. But the AI can’t understand the experience of baking a cake with your friends, which is the reason the customer went to the store in the first place. Experience defines the purpose of the solution that is enabled by intelligence and built on a foundation of data.

We here at Nitor know that digital engineers, data scientists and designers have to collaborate seamlessly to create value. Customer experience should drive the design of solutions to make sure they will deliver value to the customers, and solutions should be developed with the intended customer experience in mind to make sure the experience is actually delivered. Cross-functional teams combining design, implementation, data science and business are the way to create experience-driven, AI-based, data-enabled digital services.

And if we go all the way with automation and can seamlessly deliver thousands of variations to millions of users every second, where does that leave the designers of today?

Let’s take another example. What if an amusement park would build an intelligent solution that optimizes the customer experience: how to organize eating and shopping, how to manage queues, what information to show where? There is an abundance of data – the number of visits per season, what services were used and in what order, how long was customer at the park, and so on. And if the data is accurate, we can create intelligent solutions that take the data, make conclusions, adapt and measure against key performance indicators. But all that will still leave the final question of deciding what and how we want to optimize.

How would you define the KPI for joy?