Data Binning vs. Data Grouping

Data binning and data grouping are both techniques used to organize and summarize data, but they do so in slightly different ways.

Data Grouping

• What it does: Groups data points that share qualitative (categorical) characteristics. These characteristics can be any non-numerical attributes like customer type, product category, or location.
• Purpose: To analyze overall trends or patterns within categories, identify outliers, and compare between groups.
• Example: Grouping customer purchases by product category to see which categories sell the most.

Bottom line: Data Grouping applies to a Dimension variable by creating a hierarchical grouping like grouping days into months, months into quarters, and quarters into years.

Data Binning

• What it does: Divides quantitative (numerical) data into a set of pre-defined intervals called "bins." Each data point falls into a specific bin based on its value.
• Purpose: To simplify complex data, reduce its dimensionality, and identify trends or patterns that might be obscured by individual data points.
• Example: Binning customer ages into intervals like 0-18, 19-30, 31-45, etc. to analyze buying habits across different age groups.

Here's a table summarizing the key differences:

Key takeaway:

• Use data grouping for qualitative data to analyze categorical trends.
• Use data binning for quantitative data to simplify the data and identify overall patterns.