Best practices for combining data science and design
By: Ricky Hennessy
Tags: Design, Data Science, Design Thinking, Collaboration, AI

By Ricky Hennessy, Sheetal D Raina & Amanda Ward

At Fjord, we’ve been combining data science and design on integrated teams for the past three years. By bringing together these two disparate disciplines, we’ve been able to deliver tremendous value to our clients through data-driven products and services that address user needs and solve critical business problems. Despite the enormous potential, most organizations struggle to enable effective collaboration between data science and design.

In this article, we’ll share best practices and lessons learned on how data scientists and designers can work together to deliver transformative products and services.

Create a Shared Understanding

With different backgrounds and ways of seeing the world, it’s important to create a shared understanding between data scientists and designers so we can get the most out of this powerful collaboration. Instead of parallel tracks working in isolation, take advantage of opportunities for knowledge sharing and question asking. By remaining flexible and open to different approaches, data scientists can benefit from a deep understanding of user needs and designers can unlock possibilities presented by large sets of data.

When working with one of our aerospace clients, our data science team was performing exploratory data analysis while our design team was interviewing users. By co-locating the team and providing opportunities for spontaneous check-ins, the design team was able to validate research findings using real data, and the data science team was able to gain insight into the peculiarities contained in the data. This led to a much deeper understanding of both the end users and the data.

Don’t Lose Sight of Business Goals

Data science is a process for solving business problems using data. Before we can begin the process, it’s critical that we have a clear understanding of the business. Too often, the design process leads to an overly user-centric view that ignores real-world realities and constraints. On the other hand, data scientists can become so focused on solving technical problems that they lose sight of core issues that need to be addressed. For any data science and design collaboration to be successful, it’s critical to align user needs with business goals. Ensuring that the team is equipped with business understanding will lead to solutions that deliver value while also addressing user needs.

While working with a Fortune 100 manufacturing company, it became apparent that the initial problem at hand was too broad. If we had focused exclusively on what users were looking for or what project stakeholders wanted, we would have developed solutions that delivered little to no value for the organization. Instead, we viewed user needs and stakeholder input through a “business goals” lens, allowing us to narrow our focus to high value problems.

Execute Through Seamless Collaboration

Defining the right problem starts with the utilization of a “business goals” mindset. However, defining the problem is just the beginning. Individual understanding leads nowhere unless there is effective collaboration between team members. At every stage, integrating various viewpoints with different skills and expertise helps the team solve the right business problem in unison.

At Fjord, we have developed a framework that translates business problems into solvable data science problems using a human-centered design approach. The key lies in integrating strategy, design, and data science in a way that they are more potent than when they work separately.