Data : Quantitative or qualitative?

Data in this era of technology is more precious that gold or diamonds. And the title already mentions the types of data that we know of. The simplest defination of Quantative data is the type of data that has numbers and qualitative is the one without numbers.

If you have worked at a tech or product company, you would know the value of results that can be exactly “measured”. Now when one says “measured”, what automatically comes to our minds? Numbers! Right? Afterall that’s how we have learn’t to interpret it. Numbers have the power to have a magical impact on us. Everybody loves numbers. The management surely does! And it’s in this race to collect numbers to impress is where qualitative data gets overshadowed. And as UX designers, we know how dangerous it can get to blindly follow only quantative data.

I am not undermining the value of quantitative data by any means, all I am saying is that we need to make a conscious effort so that one does not overshadow the other. It is at the intersection of the two where the most empathetic insights lie. For example,when you just glance at analytics data of a day, it does not give you any insights. But when you start observing data over a period of time and take delebrate efforts to observe trends and patterns, that’s when it clicks. That’s what gives you real insights. Hence it is very important for UX designers to be able to “quantify” qualitative data. Some of the easiest examples of this are as follows :

1. Using frequency to depict a usability problem: There is huge difference of impact when you say ‘most’ of our users vs ‘9 out of 10’ or ‘90%’ of our users are facing xyz problem.
2. What is the real problem? : Being able to pin point the exact problem helps greatly. Best way to do this sometimes is to observe the user in the ‘system’ of you product, cause then the user doesn't need to tell us what the problem is, they simply show us the problems.
3. Combining Net Promoter Scores and comments: Using a Net promoter Score is not helpful if you end up just collecting numbers. But if you combine it with some qualitative follow-up questions, say for example ‘tell us why you gave us this rating’, that would help develop a deeper understanding of user problems.

By using these techniques, you can create a powerful impact of numbers which are backed by valuable qualitative data. And I believe that’s a great way to go about it. It’s no longer quantative vs equalitative, its the intersection of the two. Know of more techniques to “quantify” qualitative data? Shoot in comments below!