The myth that design is just the creativity of designers is unclouded by the introduction of data into UX. UX is now getting complex as the tech world gets more sophisticated.
Being Data-Driven implies you are backed with substantial data needed to make a decision. “It will tell you exactly the answer you need to know in terms of what to do next.” Data-Informed means that the majority is aware of the product’s current performance and the reason behind its behaviour to make optimizations to your strategies. Data Awareness is mostly trendspotting. Here you understand the wide range and limitations of data collection and make decisions on which methodology is best on a problem by problem basis.
Making use of all three data mindsets will grow the number of insights you need to make a decision. The most crucial part is finding out when to use each technique so that the expected outcome is in-line with the data collected. Making use of all three data mindsets will grow the number of insights you need to make a decision. Data-driven insights might need more planning and custom implementations, while data-aware is more focused on giving provision to optimise things in the future. All the three mindsets do have their place and once you figure out which one to use, it’s Yaay! Data Science + UX Design = More Conversions!
Creating personas for background research, qualitative and quantitative research, and objective market research will help understand user behaviour. Designing the Task Models by precisely and consistently documenting business and user information can work wonders. Redesigning the UX by understanding and documenting factors that did not work well previously, and conducting analysis by data clustering or segmentation can augment the design. Heuristic Evaluation with qualitative data research is another smart way to learn the why’s of user behaviour.
To conclude, data merged with UX is essential for a design to be successful. This amalgam has the key to the user’s behaviour and expectations. It’s always important to learn how to use it and to be aware of the potential pitfalls of data.