Column Aggregations

In DataGOL, users can apply various aggregation types to columns to summarize and analyze data effectively. These aggregations help transform raw values into meaningful insights — such as totals, averages, trends, and patterns.

To apply an aggregation, simply click on the column chip within a widget or visual and choose the appropriate aggregation from the list provided. The available aggregation types vary based on the column type — whether it's a dimension, metric, or date field.

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Core data column types

Column type	Description
Dimension/Attribute	Categorical data used for grouping or labeling. Examples: Product Name, Region, Status. Includes text and single-select fields.
Metric/Measure	Numerical data that can be aggregated. Examples: Sales, Quantity, Profit. Includes numbers, currency, and percentages.
Date Time - Truncated	Date or timestamp data grouped to a specific time unit (e.g., Year, Month, Day). Useful for seeing trends over distinct periods.
Date Time - Part	Extracts a specific part of a date (e.g., Month of Year, Day of Week, Hour of the Day). Useful for analyzing cyclical patterns.

Aggregations by column type

Aggregations on dimension columns

Aggregation	Description
Count	Returns the total number of rows associated with a dimension. Useful for counting records, transactions, or occurrences.
Unique Count	Counts the number of distinct values in the dimension. Helps analyze diversity or uniqueness within a field (e.g., number of distinct regions or products).

Aggregations on metric columns

Aggregation	Description
Sum	Adds all values in the metric column. Common for total revenue, total sales, etc.
Average (Mean)	Calculates the mean of all values. Useful for metrics like average order value or customer rating.
Minimum	Returns the smallest value. Often used in benchmarks or identifying low-performing entries.
Maximum	Returns the highest value in the dataset. Useful for performance ceilings or peak values.
Count	Number of rows that have a value for this metric.
Unique Count	Number of distinct values across the metric column.
Percent (%)	Represents the value as a percentage of a whole. Often used for contribution analysis.
Positive Rate	Calculates the ratio of positive values over the total. Useful for metrics like pass rates, success ratios, or click-through rates.
Standard Deviation	Measures the variability or spread of the metric values.
Variance	Indicates how far values are spread from the average. Used in statistical modeling and risk analysis.
Median	The middle value in a sorted dataset. Provides a better central tendency measure when data is skewed.

Aggregations on date columns

Date-Time Truncation

These truncations roll up timestamp values into larger, structured time periods. Useful for trend analysis, periodic comparisons, and summarizing data over time.

Truncation Type	Description
Yearly	Groups data by calendar year (e.g., 2023, 2024).
Quarterly	Buckets data into Q1 2025, Q2 2025, Q3 2025, and Q4 2025 per year.
Monthly	Groups by individual months while keeping year context (e.g., Jan 2023, Feb 2024).
Weekly	Aggregates data based on calendar week numbers.
Daily	Useful for tracking daily performance or changes.
Hourly	Common for time-series data with high granularity.
Detailed	Keeps the full date-time value without truncation.

Date-Time part

This extracts specific parts of the date and groups data accordingly. It allows comparison across the same time units from different years (e.g., all Januarys together).

Date Part	Description
Month of the Year	Groups values across years by month (e.g., Jan from 2023 and 2024 are combined under "January").
Quarter of the Year	Combines Q1 data from all years into one group.
Week of the Year	Compares activity across the same week number regardless of year.
Day of the Month	Useful for patterns around billing cycles or mid-month spikes.
Day of the Week	Ideal for identifying weekday vs weekend performance.
Hour of the Day	Helps detect hourly trends or peak traffic periods.