If you had any doubt as to why you should be giving Human-Centered Data Science some serious thought and attention, here are six reasons why applying the practice will help you create better MedTech products.
We’ve previously mentioned that Human-Centered Data Science can help you maximize the value of your data by contextualizing and humanizing it, which ultimately results in MedTech products that deliver more value. But how exactly can implementing Human-Centered Data Science do this?
6 Ways Using Human-Centered Data Science Helps Improve Product Development
Let’s explore the specifics of how leveraging Human-Centered Data Science can result in better MedTech product creation.
1. Integration of Data Science and UX Tools
Using the tools available from each discipline allows you to understand what elements of your product (e.g. data outputs, visual design, features) matter to your product users.
For example, you may think that because you’re able to collect X data, it would be a great idea to translate into Y feature because you assume the feature would be useful to your end-users.
Using tools & techniques like journey mapping and ethnographic research will provide insight on if the feature really would be valuable and if it’s designed in a way that your user is able to find value in.
Even if the feature is useful, does it disrupt the user’s workflow too much for them to actually use it as intended? This is valuable information to surface, ideally in a product’s design and testing, before it’s officially launched to market.
2. Integrated Teams Spark Better Collaboration
When your data and research/design teams are deeply integrated, you gain a common understanding of the languages, workflows, and goals of each discipline.
This allows for better collaboration on product design and development.
In essence, your teams get each other. They understand each other’s needs, how they work, and what they’re looking to achieve/create. This allows them to work as a holistic team and better support each other.
Using their own domain expertise and training, they can then enhance each other’s ways of thinking and approaching product-related problems by providing their unique perspectives and ideas.
Fluency in the languages of each discipline is a key contributing factor in helping data and research/design team members work better together to solve problems. Some terms can mean different things to different cross-disciplinary specialists – a term may mean something different to a data engineer vs. a user research specialist. Or, one party may not be familiar with the term at all.
3. Joining Quantitative and Qualitative Data
When your data science and research/design teams work in an integrated fashion, they’re aware of, and can tap into, the data each other has to offer.
This enables smarter decision making regarding design and development.
Quantitative data alone can’t tell you everything; qualitative data gathered and articulated using research and design tools add context to what your quantitative data is telling you.
For example, quantitative data can draw your attention to something unusual or concerning (e.g. data around the usage of your product).
Quantitative data can shed light on why that’s happening.
By having an awareness of, and access to, both of these data types your team is better equipped to make smarter product decisions and changes.
4. A Holistic and Seamless Product Experience
When your data and design teams collaborate on deliverables, the end result will be a product that is more holistic and seamless.
When your teams are working on product elements/features in silos and then trying to combine their work, The result is going to feel fragmented.
When Human-Centered Data Science is properly implemented, this team collaboration is formalized into the product delivery process to prevent reactionary work.
5. Increased Leadership Support
Implementing Human-Centered Data Science inherently encourages integrated support from a leadership level.
This helps ensure each team’s outputs are valued equally and prevents product red flags from being overlooked.
When Human-Centered Data Science is embraced and effectively applied, there is leadership support from someone looking at both disciplines holistically from an integrated discipline evolution and business value perspective.
This person ensures the bridging of the disciplines, recognizing and advocating for the value that each team offers.
Because of this, a red flag surfaced by the outputs of one team is valued by all, and so, is addressed before launching the product.
For example, if a product usability test doesn’t meet a certain threshold, it’s addressed until it meets the usability standards set, which results in a more user-friendly product.
Without embracing Human-Centered Data Science, you may have specific people accountable for the success and growth of each discipline.
But this doesn’t guarantee that each team will recognize and value the work that each other does (encouraging silos).
When this is the case, a red flag surfaced by one team may not be valued enough to address it before the product launch.
6. Enhanced Product Usage Data
By analyzing the system that your product exists in, through both a human-centered design lens and a data science lens, you’re able to fully understand how people interact with your product.
This will help you create products that are truly designed with people and their environments in mind.
When applying Human-Centered Data Science, systems thinking will become a foundation for how your teams work.
Products and people exist as part of dynamic interdependent systems.
A human-centered lens is one view of the system, a data science lens is another view of the system, but in isolation, neither is the most effective way to analyze the system.
Start Leveraging Human-Centered Data Science
“OK, I’m sold on the idea of using Human-Centered Data Science, but where do I start?”
The first step would be to evaluate how well you’re currently implementing Human-Centered Data Science today as a benchmark for your team’s progress. The guide we’ve developed below will be able to help you out with this and will give you a better sense of the steps you need to take to start implementing Human-Centered Data Science effectively.
Download: Introducing a Human-Centered Data Science Maturity Model
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