Understanding Attribute and Measure Columns

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Dashboard components are based on information calculated from a dataset. The creation of components of all types involves selecting columns from the chosen dataset. Dataset columns can be of two types: Attribute and Measure. Attributes appear within the dataset with blue column headers whereas measures appear with red column headers.
  
        
  
The above screenshot shows a chart created from an attribute column - 'Main Competitor' - and a measure column - 'Revenue Expected'.
  
Attribute columns contain descriptive information about the underlying data - eg names, dates, identifiers, descriptions etc. Measure columns provide results that are calculated from the underlying column values - eg sum(Sales), CountDistinct(CustomerID).  In a chart the attribute provides the x-axis values and the measure the y-axis ones as in the above example.
  
Attributes and measures can be used in various combinations in differing component types. One of the main advantages of the Intuitive Dashboards software is that it does not require the dashboard designer to understand the derivations of the attribute and measure columns within the chosen dataset. Measure columns can contain sophisticated mathematical expressions such as ( sum(sales) * TotalCountDistinct(CustomerID) ) / ( CountDistinct(CustomerID) * TotalSum(Sales) ).
  
For readers familiar with common Business Intelligence terminology. Attributes would typically be found within dimension tables in a BI schema whereas measures would typically be found in fact tables.

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