Requirements and Considerations for Using Explain Data
Explain Data is always available to authors in Tableau Desktop.
For Tableau Cloud and Tableau Server: When Explain Data is enabled for a site, Creators and Explorers with the appropriate permissions can run Explain Data when editing a workbook. All users with the appropriate permissions can run Explain Data in viewing mode in published workbooks. For more information, see Control Access to Explain Data.
Explain Data works best on visualisations that require deeper exploration and analysis, rather than infographic-style, descriptive vizzes that communicate summarised data.
- Row-level data is necessary for Explain Data to create models of your data and generate explanations. Vizzes with underlying, row-level data, where relationships might exist in unvisualised fields are good candidates for running Explain Data.
- Vizzes based on pre-aggregated data without access to row-level data are not ideal for the statistical analysis performed by Explain Data.
When you are using Explain Data in a worksheet, remember that Explain Data works with:
Single marks only – Explain Data analyses single marks. Multiple mark analysis is not supported.
Aggregated data – The view must contain one or more measures that are aggregated using SUM, AVG, COUNT or COUNTD. At least one dimension must also be present in the view.
Single data sources only –The data must be drawn from a single, primary data source. Explain Data does not work with blended or cube data sources.
When preparing a data source for a workbook, keep the following considerations in mind if you plan to use Explain Data during analysis.
- Use a data source with underlying data that is sufficiently wide. An ideal data set has at least 10-20 columns in addition to one (or more) aggregated measures to be explained.
- Give columns (fields) names that are easy to understand.
- Eliminate redundant columns and data prep artifacts. For more information, see Change fields used for statistical analysis.
- Don't discard unvisualised columns in the data source. Explain Data considers fields in the underlying data when is analyses a mark.
- Low cardinality dimensions work better. The explanation of a categorical dimension is easier to interpret if its cardinality is not too high (< 20 categories). Dimensions with more than 500 unique values will not be considered for analysis.
- Don't pre-aggregate data as a general rule. But if the data source is massive, consider pre-aggregating the data to an appropriate level of detail.
- Use extracts over live data sources. Extracts run faster than live data sources. With live data sources, the process of creating explanations can create many queries (roughly one query per each candidate explanation), which can result in explanations taking longer to be generated.
Sometimes Explain Data will not be available for a selected mark, depending on the characteristics of the data source or the view. If Explain Data cannot analyse the selected mark, the Explain Data icon and context menu command will not be available.
|Explain Data can't be run in views that use:|
Explain Data can't be run if you select:
|Explain Data can't be run when the measure to be used for an explanation:|
Explain Data can't offer explanations for a dimension when it is: