How UX Research Informs and Improves Data Science

Scott Plewes | November 8, 2018 | 4 Min Read

User Experience is a tool that can be used to help frame the context in which data science and analytics are used to deliver key insights.

User Experience (UX) design is a tool that can be used to help frame the context in which data science and analytics are used to deliver key insights. Here is an analogy to set the stage for the discussion. In Raiders of the Lost Ark, there is a scene where Indiana Jones discovers the bad guys are “digging in the wrong place” for the lost Ark because they are missing key information. The bad guys are doing the right calculation based on the information they have at hand, but unfortunately, It is not going to help them get to the Ark. There is a hidden variable that they are not aware of – information on the back of an amulet, but they only have the front piece of the amulet. Whereas Indy and his friends have all the variables involved in the calculation, so they find out where the right location of the Ark is. Data Scientists, while doing the more complicated math, are at the mercy of the same predicament as the bad guys. If you don’t have the key variables needed, even if your math is correct, you may end up with a wrong or misleading answer.

This is where the field of UX design, and more specifically UX research, can be applied. More concretely, I recently worked with an organization that was trying to support young entrepreneurs. The company had a clearly defined set of business objectives, in addition to an understanding of :

  • what their clients required to be successful;
  • what functionality and services they needed to offer,
  • the available opportunities to mine and/or track data.

What they didn’t know, until they undertook UX research, was that the key system variable that impacted their clients’ success was regular access to a mentor. From a data science point of view, you can either look at the mentor as a new object in the ecosystem to track or as a hidden variable related to the entrepreneur (in the simplest model with value 1 – the entrepreneur has a mentor; or value 0 – they don’t have one). Either way, getting the service and program to work with help of data analysis is going work way better if you include this variable (or object) in the data analysis.

Learn More: Creating Ecosystem Maps to Prioritize Product Investment

Data Science can inform UX as well. For example, it can indicate where UX should be “digging” for more information given a decline in the use of a certain service or function. Or, data science can indicate that the overall efficiency of a certain process is degrading, so a broader UX investigation might be warranted. These are just a couple of examples of insights that data science might bring to UX.

It is important to remember the reverse is true as well. Data science related mathematical techniques depend on the model of the objects in the system and their attributes (the variables) and often UX research will reveal hidden variables or the likely priority or relationship of variables. This is key to doing the correct data analysis and ensuring you are not “digging in the wrong place”.

You Might Like: Conducting User Research to Collect Meaningful Data

If you have data scientists and a UX team and they are not working side by side, you are missing an opportunity for getting more out of both teams. Or if they are working together, but don’t understand each other’s discipline enough, you have the same issue. And that means there’s an opportunity to bring them together.

In product and service design, like the movies, you want to be Indy…not the bad guys.

How can Macadamian help?

Macadamian has a team of specialists that are experienced in UX research and design in the healthcare industry and also in other complex domains. Take a look at what UX services we offer and the user experience work we’ve done in our various case studies.

Contact us today to get help with conducting research that is inclusive, detailed, and provides the insight necessary to develop a successful product.

Download: Applications of Data Science in Medical Devices

In this eBook we demystify the domain of data science, articulate what’s involved, dive into the two main approaches to applying data science, and review the most common pitfalls experienced by teams that lead to failed data science initiatives.

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