How to Integrate Data Science, Research and Design into Your Product Delivery Process

Jennifer Fraser | November 5, 2020 | 3 Min Read

The more intentional you can be about integrating both data science and human-centered design activities into each phase of the product life cycle, the more clarity and alignment your team will have around what the best next steps are in development and why.

If you’re reading this, hopefully you understand the value of data science and research/design collaboration when it comes to developing more thoughtful, human-centric MedTech products.

We’ve previously discussed how leveraging Human-Centered Data Science can help you create better products, and one component of being able to effectively implement the practice involves the formal integration of your data and research/design practices into how you develop and deliver products.

Here are some things you can do to effectively integrate data science and human-centered design activities and outputs into how your work is planned and executed within the product life cycle, including post-product launch to inform future product versions.

Tactics for Integrating Data, Research and Design Into Product Delivery

1. .Get aligned on priorities and requirements.

Identify what work/features/milestones need to be prioritized so everyone has clarity on the activities they should focus on to meet team goals.

There will likely be wasted efforts and re-work if each discipline isn’t aligned on product priorities. Implement dedicated times for team alignment into your product delivery process.

2.  Make the contributions from each of your data science and human-centered design teams intentional within your product delivery process.

Understand and educate how those activities contribute to developing a well-designed product and see if you have the appropriate people to conduct those activities.

Once you figure these things out, you can identify where in the delivery process these activities should be intentionally executed and which activities should “gate” further activities in the process. Which leads to our next point…

3.  A red flag raised by the outputs of one discipline should be resolved before continuing to move forward in the development process.

For example, if user research results indicate that there’s a major product usability flaw, that should be addressed before launching your product.

Or, if the data analyst has identified a trend on the user account information indicating a significant drop-off of users in your alpha phase, you should conduct some user research to better understand why that is happening before proceeding with releasing your beta.

The outputs of each team should be considered with equal value and weight before moving ahead with the development process.

You should also get alignment on which outputs should gate the continuation of the development process, considering the risks associated if you were to going ahead without those outputs.

4.  Leverage quantitative data and qualitative data together in each phase of product delivery.

Integrate data from both the data science team and the human-centered research/design team into the product research stage to inform feature prioritization, as opposed to prioritizing features based on assumptions of customer/user needs.

Continue this collaborative research in the post-launch stage of the product life cycle to inform future releases and updates.

Make feature decisions based on quantitative and qualitative data, not based on assumptions.

Minimize Ad Hoc Work & Create Clarity in Product Delivery

The more intentional you can be about integrating both data science and human-centered design activities into each phase of the product life cycle, the more clarity and alignment your team will have around what the best next steps are in development and why.

If you’re interested in learning more about integrating your data and research/design disciplines so that your team can create MedTech products that users truly appreciate and value, take a look at 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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