Requirements of charts

See the minimum measures and dimensions required for each chart in Tellius, plus data-point limits and tips to optimize visualizations for large datasets.

Measures and dimensions required for each chart

The following are the essential requirements for building effective and insightful charts. Get the minimum number of measures and dimensions needed for each chart type.

Chart	Min. measures	Min. dimensions
KPI	1	0
Pie chart	1	1
Bar chart	1	1
Line chart	1	1
Scatter plot	2	1
Combo chart	1	1
Table	n	n
Detail table	n	n
Pivot table	n	n
Histogram	1	0
Heatmap	1	2
Treemap	1	1
Sankey chart	1	2
Bubble chart	1	1
Boundary map	0	1 (geo dimension)
Location map	0	2 (latitude & longitude)
Waterfall chart	1	1
KPI target chart	1	0
Growth chart	1	1
Funnel chart	1	1
Bar conversion	n	0
Confidence range	3	1
YoY chart	1	1 (date)
Cohort chart	1	2

Data point limits for each chart

In this section, we'll outline the default number of data points allowed for various chart types. Understanding these limits is crucial for creating effective and visually appealing charts.

Type of chart(s)	Default limit of data points
Line chart Area chart Growth/Trend chart Combined bar chart Heatmap Confidence range chart Scatter chart	10,000
Bubble chart	5000
Treemap, Bar chart	1000
Location map, Boundary map	1000
Pivot table, Cohort chart	500
Waterfall chart	100
Pie/Donut chart, Funnel chart, Tables	50

Optimizing your chart data

Understanding the default data point limits for different chart types is essential for effective data visualization. However, there may be instances where you need to work with larger datasets. In such cases, consider the following options for better data representation:

1. Alternative chart types: Explore alternative chart types that are better suited for larger datasets. For example, if you're dealing with extensive data, a scatter plot or a bar chart might be more appropriate than a pie chart.

2. Data optimization: Before creating your chart, consider optimizing your dataset. This can involve summarizing or aggregating your data to reduce the number of data points while retaining key insights.