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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.

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.

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.

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

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.

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.

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.

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

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

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

Creating a visually balanced composition in your data visualization. Example: Evenly spacing elements on a dashboard to avoid overcrowding one side.

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.

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

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.

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

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.

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.

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

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.