The AI driven "Smart Visualization" engine attempts to draw the data for a user using the most appropriate visualization for the data selected. This includes deciding on the chart type (including grids) and position of the data elements. It is used by both Smart Discover and Natural Language Querying.
To better understand its mechanics, the following elements are taken into account:
- The number and type of metrics (values) selected
- The number and type of hierarchies (dimensions) selected
- The size of the number of elements (members) per hierarchy. This helps plotting very large dimensions vs very small dimensions etc.
- The types of hierarchies (regular, time based, geospatial)
- Any filters and sorts applied to hierarchies
- Any slicers (data filters) selected
With these elements, the engine will plot the data in the most appropriate fashion. This includes:
- Decisions related to data positions:
- categories (X-axis) and series (legend)
- rows and columns
- trellising (multi-charts)
- Decisions to Visuals type
- Cartesian charts (column, bar, line etc)
- Segment charts (pies, donuts etc)
- Plots (scatter, bubble etc)
- Advanced charts (circle packing, tree maps etc)
Overriding the Automation
Depending on the tool using the Smart Visualization engine (Smart Discover or NLQ), these choices can be overridden to specify a chart type to use. However, the engine will automate the LAYOUT of the data elements for the given visualization.
- Click to see examples of the override in NLQ
- Click to see examples of the override in Smart Discover