Data is like garbage. You’d better know what you are going to do with it before you collect it. -Mark Twain
What is data anyways?
Collecting facts and statistics together for reference or analysis is defined as data.
What role does data play for a UX designer?
The reason we practice UX design is to increase the value of a product or service — for both users and the company that provides it — by creating more effective ways of interacting with it. Since today’s big data is constantly changing and its scope is ever increasing, UX designers must ensure that a product or service can cope with change well and scale as the amount of data it must handle increases.
Human beings are amazing pattern matchers and, of course, a picture is worth a thousand words. The need for data visualization is especially important for complex structures such as those found in big data. Data visualizations may represent a single complex structure for a big data set or multiple structures that result from processing this data considering many different variables — much as a CAT scan can capture multiple image slices. As we unlock and use big data, we will discover ever more ways of using and analyzing this data. We must ensure that these systems are easy to update, so we can take advantage our learnings.
Big data is ripe for high-quality, automated, data-visualization techniques. These visualizations could be the key to understanding big data and, in turn, to understanding the future. In these automatically generated visualizations, we will discover both expected and unexpected structures.
Qualitative research in UX:
Many UX designers believe that the only difference between qualitative and quantitative research is that the former is an open-ended question, while the latter is not. This distinction does not give the full picture.
A qualitative study typically describes the characteristics of behaviour, such as why and how a sample performs a certain task. Qualitative data generates data around the what, the why, and the how of a particular point of interest.
In-depth interviews, task analysis & contextual inquiries and surveys are three primary pillars for deriving a qualitative research.
Quantitative research in UX:
Qualitative research and quantitative research have the same goal: describe the population in the best possible way. In UX design, the same purpose is present for both forms of research. However, the methodologies differ.
Quantitative research methods are typically trying to answer the how many or how many types of inquiry. Often, it is possible to turn qualitative research into quantitative research by applying it to a large enough sample.
The most important difference between these two forms of research has to do with the reliability of the data. Qualitative research can often provide insights rarely attainable through other means. However, they are difficult to implement in reliable ways.
Data is insightful. All data however, needs context and needs to be validated with qualitative insight, from your users. You can collect all the data in the world, but in the wrong hands, assumptions will be made and you won’t know what to do with it.
The data you hold will then be more actionable and you will be able to use your findings to make decisions and to influence design.
Facts Do Not Cease To Exist Because They Are Ignored. -Aldous Huxley
Conclusion:
I believe visualizing data is all about relativity and relationships. With most user research data in an agile environment, you’re not going to have a ton of data to wrangle, so it is difficult to argue that this data should be graphed or charted at all, when the sample size itself might call into question some of the assertions that such a visualization would represent. Instead, it’s more important to put the qualitative data on some sort of spectrum that is relative, not concrete, in order to find out the relationships between the data points and the context that they build.
