In my experience, data densification typically occurs in two scenarios:
1. Domain padding: Domain padding happens when Tableau fills in missing values along a continuous axis, such as dates or numbers. For example, if I have monthly sales data with missing months, Tableau can pad the missing months with null values to create a continuous timeline. This helps me see trends and patterns more clearly in my visualizations.
2. Domain completion: Domain completion occurs when Tableau fills in missing combinations of dimensions in a cross-tabulation or matrix. For example, if I have data on sales by product and region, but some product-region combinations are missing, Tableau can complete the matrix by adding null values for the missing combinations. This enables me to analyze the data more effectively and identify potential gaps or opportunities.
A useful analogy I like to remember is that data densification is like filling in the gaps in a puzzle, making it easier to see the whole picture.
An example where I used data densification was when I had to analyze website traffic data with missing days due to tracking errors. By using domain padding, I was able to create a continuous timeline in Tableau, which helped me better understand the overall traffic trends and identify potential issues with the tracking system.