Gauge Measures and Dimensions Explained


This article provides additional information on gauge measures and dimensions. 



Measures are the mathematical portion of a gauge (e.g., count, count distinct, min, max, sum, average) that is aggregated from an existing raw field within your dataset. In the context of data visualization, measures typically map to the Y-axis of a chart. Examples of measures include ticket count, average time to resolution, and total revenue. For instance, you may calculate the average response time per day over 90 days. In this case, the Response Time field is your measure because you want to get the average response time.

Aggregation functions within BrightGauge perform a calculation on a set of selected values and result in a single value. For example, a measure that contains the values 2, 5, 4, 1, 6 aggregated as a SUM results in a single value of 18.


You would like to see which end users create the most ConnectWise Manage service tickets. Currently, you have 100 service tickets from 25 end users in Manage. To display the data, you create a bar chart gauge with the following measures and dimensions:

  • Measure: Service Ticket IDs
    • Aggregation function: COUNT
  • Dimension: end_user

The resulting gauge displays the number of service tickets submitted by each end user and allows you to see which user submitted the most tickets.

BrightGauge provides the following set of predefined aggregations.



Result for cells that contain

1, 2, 2, 5, 3


Computes the sum of the numbers in the measure's cells. Null values are ignored.



Computes the arithmetic mean of the numbers in the measure's cells. Null values are ignored.



Selects the smallest number in the measure's cells. Null values are ignored.



Selects the largest number in the measure's cells. Null values are ignored.



Counts the number of values in the measure's cells.


Count Distinct

Counts the number of unique values in the measure's cells.



Dimensions group quantitative data (measures) into useful categories (e.g., number of tickets closed by technician). They usually refer to categorical data, such as technician name, ticket status, or even units of time (e.g., day, week, month). These fields typically map to the X-axis in a line chart or vertical bar chart.

For example, 90 days is your dimension. For a non-date range graph like Tickets Closed by Technician, the technician is your dimension since you are trying to measure the number of tickets closed against that dimension.

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  • Brain,

    Is it possible to have a Date filter for Year to Date?


    Thank You


  • Hi Nataly,

    Our current default datasets only pull back 120 days worth of data so the year to date filter most of time wouldn't pick up all the YTDd data.  So we purposefully did not include that filter to not confuse anyone.

  • When choosing a date field, it seems that the only aggregation option is count. Is this correct?

  • IS it possible to calculate a field? customer + ticket type + subtype for example