UX Design with Data Science is the perfect combination
2022-06-03
Commons between data science and UX design
I think UX design and data science have a lot of common elements which is shared. As you know, UX design is all bout understanding user behavior based on data and observation as ethnography. First of all, UX designers and data scientists establish research questions that they want to find answers to. This becomes an assumption for research projects what they do. Based on observation or A/B test and interview scripts, the researcher writes an assumption around the user’s context to build business value for them.
Why machine learning will be a great tool for UX design
One of the core capabilities of machine learning is to find patterns from large data sets. Computers and algorithms are good at finding patterns from large sets of data. These patterns can be an assumption for business models and strategy models around users. And also they could figure out hidden values from a data set and they can apply those values to their UX and business model. Here is the cycle of process for UX design using data science. First, they collect data put that data into the algorithm, and find patterns. and then creating a UX/UI interface design system for users. Finally, let users use this designed software system to test design and business assumptions. These are full cycles of UX/UI design processes using data science to collect, build test, and finally collect data again for future release of products that they make for their customer.
Design research becomes an algorithmic process
algorithmic is all about establishing a set of rules with programming. User behaviors change over time and they generate all sorts of data as user activities within software or service we make. This isn’t a small set of data. It is a big data set that is collected within a service or software. To analyze these data sets, We need certain algorithms to understand and process data. This is why I say that design needs algorithms to process user data. More and more dataset comes into the system, and we will get more understandable facts about how users behave and are motivated in certain contexts. This is true value for designing new business models and becoming the future of design