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Consistency means using the same visual encodings across similar types of visualizations. For example, using the same color for a data series in multiple charts for easy comparison.

Proper color use enhances comprehension and should convey differences or similarities. Example: Using color-blind friendly palettes in charts to ensure accessibility.

Ensuring the visualization accurately represents the underlying data. Example: Not using truncated axes that can mislead viewers about the data scale.

Data visualizations should not distort the information. Example: Ensuring map projections do not distort the size of geographical areas when displaying data geographically.

Adding interactive elements to allow users to engage and personalize data discovery. Example: Implementing filters or hover details in an online visualization.

Proportionality ensures that visual elements are scaled proportionate to the data values. Example: Making sure bar heights in a bar chart accurately reflect the value they represent.

Hierarchy and emphasis guide viewers to the most important parts of the data visualization. Example: Using larger or bolder text to highlight key data points or titles.

Providing a story or context around the data to make the visualization more meaningful for the viewer. Example: Including annotations that explain peaks or drops in a time series chart.

Designing visualizations that are accessible to users with disabilities. Example: Including descriptive alt text for users who rely on screen readers.

Simplicity involves avoiding unnecessary decorations and focusing on the data. For instance, applying Occam's razor to decide between chart types, choosing a bar chart over a complex 3D pie chart for clearer comparison.

Labeling provides context to the data visualization. Example: Clearly labeling axes and including a legend when multiple data series are present.

Effectively representing data with multiple dimensions in a comprehensible way. Example: Using small multiples or trellis charts to represent complex data broken down into simpler, related parts.

Choosing the right chart type based on the data and the message to be conveyed. Example: Using a line chart for trends over time instead of a pie chart.

Elements should be aligned for a neater result and easier comparison. Example: Aligning charts and text boxes on a dashboard for a cleaner look and easier data interpretation.

Using animation and transitions wisely to guide viewers through the data visualization. Example: Animating the change in a bar chart when a user selects a different time frame.

Directing the viewer's attention to the most important parts of the data visualization. Example: Dimming less important parts of a chart to focus on key trends or outliers.

The data-ink ratio is a concept by Edward Tufte, which suggests maximizing the ink used to represent data relative to the total ink used in a display. Example: Minimizing gridlines in a graph to highlight the data.

Negative space refers to the area around and between elements in a visualization that is left empty, improving readability. Example: Ensuring there's enough space between lines in a multi-line chart.

Visualizations should facilitate comparison of data points. Example: Standardizing scales on multiple bar charts intended for direct comparison.