What if I told you that less than half of design teams frequently work with analytics or data science employees during an iterative design process?

It’s hard to be “dataful” without talking to people who have lots of data (to borrow a phrase from design author John Maeda).

Earlier this year, I worked with my colleague Brandon Purcell on a report about creating data-fueled products — a digital product that recognizes patterns and anomalies relevant to a user’s goals in large quantities of data and adjusts parts of its user interface in response. You can read a blog post about it here. In that report, we outlined the design and data science collision and how it prevents companies from getting the most value from both practices.

There’s another way data science and design work together: through a data-informed process that incorporates insights from different parts of the organization. We uncovered some insights into how this collaboration was going but wanted more.

Now, I’m excited to share data from our recent 2019 survey on the state of design teams. The basics:

  • Just over half (53%) create or validate hypotheses based on data science or analytics findings on a monthly basis.
  • About the same number (51%) review data science and analytics findings in cross-functional meetings on a monthly basis.
  • Just under half (44%) of design teams work with analytics and data science employees during an iterative design process on typical projects.

It’s pretty concerning that about half of the design teams that responded don’t create or validate hypotheses based on data science or analytics findings, isn’t it? And why aren’t people with data and analytics backgrounds part of the iterative process? I hope the product managers and marketers involved are making up for this gap between the groups.

By now, you’re probably thinking, well, those numbers aren’t great, but they’re also not too bad. If you care about deep collaboration between these groups, here are even more concerning numbers:

  • About 21% say data scientists attend readouts of qualitative research findings or attend research sessions at least monthly (e.g., usability testing, customer interviews, ethnography, etc.).
  • Just under 20% include data science and analytics team members in design sketching sessions at least monthly.

To me, this says that there’s little participation from data science or analytics in valuable and complex design work. Most collaboration seems to be pretty superficial. And this gap goes against the best practices we laid out in our report on data-fueled products and in previous blog posts.

Does this sound familiar to you? If so, stop relying on a product manager or marketer to translate data for you. Go directly to find someone in analytics or data science, and start asking questions about how products, features, and customer or business metrics are performing and what those individuals are working on, then enlist them in some sketching and enrich your perspective. You’ll be better equipped for the future — and your customers will benefit, too.

If you’re working on these challenges or have questions about this, get in touch — I’d love to hear from you.