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Annotations

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What Is It?

The Annotations tool enables users to leave notes, commentary, or detailed explanations to communicate insights and document their analytics process.


Why Is This Important?

Annotations help users document their process and add commentary or perspective in their analysis through storytelling. By creating these threaded visualizations and narratives, organizations can build a portfolio of analytics projects that include rich context, making it very easy for wide audiences of varying disciplines to comprehend the analytics process and the insights. As an example, Business Analysts can go beyond creating a dashboard of multiple charts by linking visualizations and annotations in a sequential, organized fashion that guides consumers through the key findings in the analysis. Instead of expecting that interpretation will be easy, teams can build stories that cover both the techniques used in analysis and the overall takeaways from the analysis. Providing pathways to easily interpret results and understand the components of the analysis is critical for organizations looking to empower citizen data scientists and consumers to adopt AI and ML techniques.


How?

Types of Annotations

  • Point Annotation - Can be added by right-clicking a point. Very useful for keeping track of a single point of interest across multiple visualizations since this Annotation stays attached to the point when changing the plot.
  • Mapping Annotation - Can be added by right-clicking the plot. Best for adding comments specific to the current plot, this Annotation will disappear when changing plots.
  • Dataset Annotation - Can be added by right-clicking the plot. Useful for adding overall context to your dataset, this Annotation will persist across all plots in the current dataset.



Linking multiple plots through Annotations

Annotations can also be used to quickly switch from one plot to another using the linking functionality:

  1. Simply click on the link icon when hovering over an Annotation to link to another visualization (either in the same dataset or another dataset). 
  2. You can adjust the color of both the Annotation text and window, as well as convert between mapping and dataset Annotations from the Annotations settings bar. 
  3. Clicking the Annotation pip (the circle with a number inside) will minimize or show the Annotation, and all Annotations can be shown or hidden from the Tools section of the application menu.


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