Tactics for Increasing Data and Design Team Collaboration
Jennifer Fraser | November 5, 2020 | 3 Min Read
Stop allowing your data and your research and design teams to work silos! Start helping these two teams collaborate effectively and leverage their unique tools in an integrated way to enhance how you design and develop products.
You get that it’s important to consider both the data and its context when creating new MedTech products.
When your data and design teams work in a more integrated fashion, their outputs become richer because they’re informed by what each discipline has to offer.
Quantitative data + qualitative data = a more complete story describing what’s happening and why.
This concept of data and design collaboration sounds ideal. But some might say data scientists/engineers and UX researchers/designers are from two different planets.
So how do you get your data and research/design teams to see eye to eye and reduce friction in collaboration so that your product can reap the benefits?
Tactics for Increasing Collaboration
1. Work towards integrating the collaboration of the two teams into the formal product development process.
Start looping your data and design teams into regular product meetings together.
Do you practice Scrum? Whatever your meeting style may be, tailor it to include both teams.
Deliverables should include contributions from both teams and should be seen as co-owned by both teams.
Updates on those deliverables and roadblocks each team member is facing should be discussed in your regular syncs.
This is also an opportunity to provide any feedback to team members on outputs leveraging their unique experience/perspective/available data.
Are there activities that can be better shared between team members?
For example, is a user experience researcher conducting a longitudinal study with interviews? If so, does it make sense for a data analyst to listen in on some of those interviews?
2. Create the technical infrastructure to be able to share data across teams.
Whether that be by giving each team access to each other’s data tools, shared Google Drives, or other internal hubs.
Update your team when new data is available (e.g. “I recently did a user research study and findings can now be found in X location”).
3. Adopt a mindset of being one team to achieve common goals.
Minimize the segregation and selfishly motivated activities done by each team by creating common goals where each discipline can make unique contributions to achieve them.
There will be an increased openness to collaboration if everyone is working toward something common as opposed to having separate goals and working in different directions to meet them.
With a common goal to strive for, teams are also less likely to favor viewing a problem through the lens of one discipline over another.
With both disciplines tackling a problem, they can decide which tools from their collective toolbox are the best to use.
4. Educate both teams on the value that each other brings to the table.
Educated both teams on the purpose of both data science and human-centered design. Do you provide staff training with domain 101s (e.g. Human-Centered Design 101)?
Discuss at a department level how data and research/design play into creating products that are thoughtful, intentional, and prioritize the user.
Discuss the impact of a world where these two teams don’t collaborate (current state) vs. what things could be like/what the business impact would be if you did.
Clarify for both teams how each discipline can complement the other and can collaborate to arrive at more valuable outcomes faster.
Start Supporting Data and Design Team Collaboration
Increasing collaboration between and integration of your data and research/design teams allows each discipline’s unique tools and methods to be used together in a more complementary way.
Goals and priorities are aligned and understood by all and everyone has an increased understanding of how outputs from each discipline can add value to the product designing and developing the product.
Once your data and human-centered research and design teams start working together in a more integrated way, we promise you’ll experience more product “aha” moments than ever before.
Download: Introducing a Human-Centered Data Science Maturity Model
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