When working with data visualization tools and breaking down your data to illustrate values, you’re also inadvertently grouping it into measures and dimensions. Here’s a handy guide to help you understand what these terms mean and how to accurately sort different parts of your data under the correct term.
Measures are numerical data that are calculated or aggregated. They represent observations about your data or calculated values, such as the sum of revenue, average cost, or profit-per-capita, or even non-numeric data that is counted.
Measures have an aggregation type associated with them. By default, the SAP BusinessObjects Lumira application, a sophisticated data visualization tool, sets this type to sum. For example, if the chart includes “Revenue by Country” and the sum is associated with “Revenue,” SAP BusinessObjects Lumira allows you to customize the prefix or suffix to indicate data, such as CAD, EUR, or USD for currency.
Dimensions constitute categorical data, such as year, product, country, and salary range. Categorical data is also known as nominal data, and is used for discrete values. The dimension called “Product Type” may include the values of “Men’s Clothing” and “Women’s Clothing.”
In ordinal data, the dimension has a fixed order. If the dimension reflects the outcome of a survey result, the resulting value might appear as “Agree,” “Neutral,” or “Disagree,” showing up in that implicit order.
In interval data, each value in the dimension represents a range of values. The dimension “Salary” can be categorized into the following values of salary ranges: “<$20K,” “$20-$40K,” “$40-$80K,” and “>$80K.”
How to tell the difference
When you aggregate the object, it must make sense for the column to be a measure. “Sales Revenue” is a measure, but summing up product list prices isn’t—that’s a dimension.
On the other hand, you can create measures from categories by counting their elements. For example, “Total Number of Countries Visited by Our Customers” is a measure.
Do you have any tips and tricks to effectively and quickly grasp the definition of measures and dimensions for use in data visualization? Share your advice with us in the comments!
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