As part of my research about data science and design, I spoke with Ovetta Sampson, design research lead at IDEO, a global design company. Sampson’s advice that stood out most to me about how to improve the use of data in the design of better experiences: Stop thinking about data scientists as “that nerdy guy or girl behind the scenes.” Instead, she says, think about them as designers who happen to use data to craft experiences. This echoes a similar sentiment heard at last month’s San Francisco Design Week during a session titled Bots are coming: Designing with AI.

Sampson’s other tips for combining human-centered design and data science:

Include data scientists throughout the design process by inviting them to your research sessions or building out “data-people journey maps.” These maps catalog points where data is created, transferred, transformed, or used. Next, complement this by adding the people who create, transfer, transform, or use that data. The goal is to reveal all the places where human bias can change the data. She also suggests asking data scientists to moderate feedback sessions on prototype models. Sampson laments (and our research confirms) that data scientists are often brought into the design process too late — hurting customers and the business.

Let data scientists iterate just like designers and help them prototype the models they use. These can be real and tangible or be simulations contained in a virtual world. For example, IDEO prototyped a system to help a travel company’s call center agents by hiring actors to call into a test group of agents who tried to use the new system.

Build in “exposition time” for data scientists to share with designers what they’re working on. Rather than relying on individual designers to seek out data scientists and ask them questions they might be embarrassed to ask, create an environment where data scientists share their process and what they’re doing at each step.

Find passion projects to work together on and prove the value of design to them — especially if your organization keeps design and data science separate. For example, Sampson worked with data scientists at IDEO to create a tool the company uses to analyze interview notes and transcripts. Do this by appealing to data scientists’ desire to have an impact, because, as Sampson told us, “No data scientist wants to sit at home and create things nobody uses.”

For more examples and guidance about these challenges, see my new report, “Data-Fueled Products: How To Thrive On The Design And Data Science Collision.” If you aren’t a Forrester client, this blog post has more information: Data Science And Design Collide — There’s A Better Way.

As always, if you’re working on these challenges or have questions, get in touch — I’d love to hear from you.