How to Create a Culture Driven by Data, Research and Design

Jennifer Fraser | November 5, 2020 | 4 Min Read

Your product team can't fully leverage all that data science, research and design have to offer until you create a culture and leadership support that understands the value of these complementary disciplines.

You know that your MedTech product design benefits from integrating your data science and human-centered design practices (aka Human-Centered Data Science).

Perhaps you’ve made some steps to improve how your data and design teams collaborate and intentionally integrate their activities into the product delivery process.

But take a look at your organizational structure.

Are your data specialists reporting to a VP of Product Delivery…or a VP of Data?

Are your user researchers reporting into a VP of Data…or a VP of Human-Centered Design?

You may come to realize that your data and/or research and design specialists aren’t reporting to people who can best support them and who truly understand the value of their work.

A crucial component of being able to fully leverage Human-Centered Data Science is creating an organizational culture and leadership structure that supports both your data science and human-centered design teams respectively and enables them to thrive.

Tactics for Creating an Organizational Culture that Supports Human-Centered Data Science.

1.  Create an organizational structure where your specialists have professional guidance and mentorship from senior management and executive leadership.

For example, this could mean having a Data Engineer report to a Data Engineering Manager, who reports to a VP Data.

When this might not be possible (e.g. you’re a smaller company), think about what you could do to increase fluency across the entire company in relation to data science and human-centered research and design (e.g. company-wide workshops/ training; see point 4).

Creating an appropriate org structure also ensures that your data and your research/design teams are being evaluated on success metrics that matter or make sense to them within the context of their work.

It also ensures that there are specific people accountable for driving the development of skills within both disciplines.

To illustrate this point, when marketing or product management is conducting product research and design activities, there’s no one really accountable for developing human-centered research and design as an internal discipline and your product design ends up suffering because of it (you don’t end up fully leveraging all the tools that discipline has to offer).

2.  Support continuous learning and development within data and research/design, especially if you don’t have the resources to onboard new leadership roles.

This will help create an encouraging environment for your teams to learn more about their own crafts, and also the different crafts of their colleagues, and fine-tune their skills.

This, in turn, supports elevated understanding between your data and research/design teams.

Supporting continuous learning and development can also increase your talent density and quality, which is great if you don’t have the budget to hire on a fully stacked UX or data science team.

You’re able to make the most of the resources you have.

By raising the literacy between the disciplines, the individual contributors can identify when someone from the other discipline would be an asset to a project.

For example, a data analyst could recognize when a user experience researcher could be a valuable addition to help with a challenge on a project.

3.  Understand the nuances of each specialist within both the domains of data science and human-centered research and design.

Understanding what a data scientist vs a data visualization expert vs a data engineer does will help you set realistic expectations for your team and their outputs.

You can’t hire a couple of data generalists and expect to get the same outputs as you would with hiring a set of specialists.

4.  Increase cross-disciplinary influence by sharing your work.

To get the rest of executive leadership to equally value the inputs of both data science and human-centered research and design, get in the routine of sharing what these teams have accomplished and the value they add.

“Lunch and Learns” or various working groups can be used to share knowledge across domains and increase fluency between disciplines.

Internally highlighting “success stories” where the collaboration between disciplines solved an issue or uncovered a valuable insight for the product can help reinforce the business value of this “mixed methods” approach to working.

Start Your Journey Creating a Culture that Values Data, Research, and Design

At this point, you should have a pretty good sense of whether your company’s culture truly values data science and human-centered design as complementary disciplines.

Even if you have the right talent in place to be able to use advanced methods in both data science and human-centered design, they won’t be able to excel or deliver their full potential until:

  1. They have support from appropriate leadership who understands the value they bring to the table and is an advocate of their work to other disciplinary teams and;
  2. Their work is understood as a valuable contribution to product development by people outside of their own discipline.

If you’d like to continue learning about how to integrate your data science and human-centered design teams and build a Human-Centered Data Science practice, we recommend checking out our guide below.

Download: Introducing a Human-Centered Data Science Maturity Model

Assess your Human-Centered Data Science maturity as a first step towards increasing the collaborative nature of your data and human-centered research/design practices. Fill in the form below and we'll email you a copy of the ebook.

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