Design Thinking, Meet Data Science

In the age of intelligent systems, data is everything. Data reveals patterns that inspire product innovation. It’s data about user preferences and past behaviors that informs personalized experiences. With the application of data science, every interaction can be designed to feel more relevant and intentional to individual users—and simultaneously scaled to an unlimited number of users at once.

But there are limitations to what any data point can communicate about human needs. Design methodologies, rooted in an ethnographic approach, deepen understanding by providing the right context.

Data-Backed Design Research

Data science enhances traditional design research techniques, revealing connections between seemingly disparate pieces of information. These inferences turn into testable insights later backed by still more analytics.

During a recent engagement with a retail client, one interdisciplinary frog team ran parallel design research tracks to put this to the test. Data scientists analyzed the client’s available data to identify patterns to explore. Meanwhile, ethnographic design researchers took on a more immersive, qualitative understanding of the customer on an individual level. Their combined findings revealed a rich understanding of the client’s target customers in context with millions of other customers analyzed in the data. The team was able to match human faces to relevant data patterns to keep a sharp focus. In addition, they could use data to create simulations and projections to consider the impact of design decisions in a measurable and demonstrable way.

Next-Level Prototyping and Testing

Designing with data makes it possible to segment users with incredible precision. Personas defined by user data can inform any number of personalized prototypes. With the efficiency of data science, testing and refining is becoming available at speeds and levels of fidelity previously impossible in traditional methods.

Traditional user testing practices get a boost from data science, ones that have the power to scale exponentially with the help of artificial intelligence. As users engage with an intelligent system, it continues to learn. As more patterns emerge in the data, so do more opportunities to enhance and refine flows, interactions and processes.

Having data science integrated into the design process also helps mitigate risk, making it possible to design with fewer assumptions. Considered data analysis shines an objective light on potential blindspots to user needs that could compromise experience and tarnish a brand’s reputation. Human-centered design is what makes sure these data-backed experiences are intuitive, meaningful and memorable.

Experiencing Data

Every time a customer interacts with an intelligent product or service, data is core to their experience. Here at frog, our teams use data to conceive of new and better customer experiences. We have partnered with clients to create new offerings that expand their reach and influence with the application of data both in operations and as a component in product and service design.

When well designed, data can completely transform an organization’s offering or processes. Yet data-driven products, specifically those powered by artificial intelligence, are still new terrain for many businesses and consumers alike. It’s important to manage expectations and communicate how data is powering an experience, not interfering with it. Ultimately, how well data is designed in service of the user’s experience determines the success of an intelligent product.

This article was made with help from frog alum, Ricky Hennessy.