Many UX designers are somewhat afraid of data, believing it requires deep knowledge of statistics and math. Although that may be true for advanced data science, it is not true for the basic research data analysis required by most UX designers. Since we live in an increasingly data-driven world, basic data literacy is useful for almost any professional — not just UX designers.
Aaron Gitlin, interaction designer at Google, argues that many designers are not yet data-driven
But First, Some Data Concepts
In this article I will talk about structured data, meaning data that can be represented in a table, with rows and columns. Unstructured data, being a subject in itself, is more difficult to analyze, as Devin Pickell (content marketing specialist at G2 Crowd, writing about data and analytics) pointed out in his article “Structured vs Unstructured Data – What’s the Difference?.” If the structured data can be represented in a table form, the main concepts are:
DATASET
The entire set of data we intend to analyze. This could be, for example, an Excel table. Another popular format for storing datasets is the comma-separated value file (CSV). CSV files are simple text files used to store table-like information. Each CSV row corresponds to a row in the table, and each CSV row has values separated (naturally) by commas, which correspond to table cells.
DATA POINT
A single row from a dataset table is a data point. In that way, a dataset is a collection of data points.
DATA VARIABLE
A single value from a data-point row represents a data variable — put simply, a table cell. We can have two types of data variables: qualitative variables, and quantitative variables. Qualitative variables (also known as categorical variables) have a discrete set of values, such as color = red/green/blue. Quantitative variables have numerical values, such as height = 167. A quantitative variable, unlike a qualitative one, can take any value.