Identifying the attributes of your data: the key to better visualizations

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In a previous post we explained how to choose the right chart and demonstrated a guide to different Qlik visualizations. In this note we will teach you how to interpret data attributes, which will be of great help when preparing your charts.

Quantitative and qualitative data
There are two categories of attributes: quantitative data and qualitative data. Quantitative information is information that can be expressed in numbers and is susceptible to measurement. Examples include units or products sold, quarterly revenue, department expenses, employee statistics, and the company's market share.

The strong emphasis on data dashboards and Key Performance Indicators (KPIs) shows the importance of measuring and monitoring company data.

Qualitative data refers to values that cannot be measured cyprus number data numerically, but can be described through language.

Within these two categories there are a total of four subcategories also referred to in the following table:

Data attributes

Quantitative data
1-Ratio (cost $10, $20, $30 or age 10 years, 20 years, 30 years): Data on which you can perform arithmetic operations (add, divide, etc.)

2-Intervals (temperature -5°, 10°, 25° or time 1am, 5am, 9am): data with a set value on which all arithmetic operations cannot be performed. For example: you cannot calculate the sum of the temperature for a week, but you can calculate the average temperature per day and the high/low for each day.

Qualitative data
1-Ordinal (small, medium, large or 1st place, 2nd place): data with a fixed ranking with an indeterminate distance between values.

2-Nominal (NFL football vs. English soccer, laptop vs. desktop): you can distinguish between values but not order them.

Conclusion
Today's organizations are increasingly using data visualizations and tools to ask better questions and make better decisions. Understanding and interpreting data attribute categories will help you build better charts.