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5 min read
Christine Virjee
Data visualization isn’t just about presenting numbers—it's about telling a story. Whether you're a seasoned people analytics professional or new to the field, mastering the art of data storytelling is crucial in driving insights and actions at your organization. Annotations are a powerful way to make your charts and graphs not only more informative but also more engaging and accessible. What Are Annotations? Annotations are textual elements added to data visualizations to provide additional context or highlight specific insights. Think of them as the notes in the margins of a book, drawing attention to key points or clarifying complex information. Annotations can take many forms, including labels, callouts, or even small visual elements like arrows or shapes that guide the viewer’s attention to critical areas of the chart. Why Annotations Matter in Data Storytelling Annotations play a pivotal role in transforming raw data into a compelling narrative. They serve several essential functions in data storytelling: Highlighting Key Insights: Annotations can spotlight the most critical data points or trends, ensuring that your audience doesn’t miss the story you’re trying to tell. Providing Context: They help clarify the meaning behind the data, explaining anomalies, trends, or outliers that might otherwise be overlooked. Improving Readability: Annotations make data visualizations easier to understand and interpret by breaking down complex information into digestible pieces for a broader audience. Guiding the Narrative: By strategically placing annotations, you can control the flow of information and guide your audience through the data in a logical and impactful sequence. How to Effectively Use Annotations in Your Data Visualizations Start with a Clear Objective: Before adding annotations, clarify the main message or insight you want your data to convey. Annotations should support and enhance this message, not overwhelm or distract from it. Be Selective: Not every data point needs an annotation. Focus on key areas that are critical to your narrative. Over-annotating can clutter your visualization and dilute the impact of your message. Consider adding annotations on drill-down levels. Keep It Simple: Annotations should be concise and to the point. Use clear and direct language that adds value to the data without overwhelming the viewer. Use Visual Cues: Enhance annotations with visual elements like arrows, shapes, or colors that draw attention to the most important parts of your data. This helps guide the viewer’s eye and emphasizes the significance of the annotated data. Tip: On Windows, use the shortcut start + . (or CTRL + CMD + Space on Mac) to insert emoji symbols. Consider Placement Carefully: The placement of annotations can significantly impact their effectiveness. Ensure they are positioned where they naturally draw the eye but do not obscure critical data. Examples of Effective Annotations Before: A chart without an annotation could potentially mislead the reader. After: Include an annotation to clarify the scope of the data in question. Before: Annotation covering the data point causes ambiguity. After: Annotation placed to the side of the chart to improve readability. Annotations on Drill-Down: Annotations can be placed at different levels of multi-level hierarchies. Below is an example with an annotation on Level 1 of the Termination Reason. After you drill down to Level 2 – in this case Voluntary Termination – you can find further annotations specific to that level. In this example, you can quickly see that "Personal" is the main reason for Voluntary Terminations. Final Thoughts Annotations are more than just a minor detail in your data visualizations—they’re a powerful storytelling tool. By carefully considering their use, you can turn raw data into compelling narratives that not only inform but also inspire action. Whether you're explaining a complex trend or highlighting a key insight, annotations help ensure your message is heard loud and clear. For more information about One Model's annotations capabilities, visit our Help Center.
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Featured
6 min read
Christine Virjee
Charts play a crucial role in conveying insights, which is why elements like chart legends are essential tools for interpreting colour variations and understanding complex data sets. Let’s dive into the nuances of chart legends with five tips designed to make your charts more impactful and easier to understand. What are chart legends? Chart legends label and help explain colour variations in your charts. When used effectively, they improve readability and prevent misunderstanding. Chart legends work best when there is more than one metric or dimension, and where colour variations need an explanation in a chart. Let’s work through a few real-world examples and tips to see how chart legends can enhance or detract from a data visualisation. Tips and real-world examples Tip #1: Use legends to explain colour variations In this first example, variation occurs when there is more than one metric and the chart legend explains their meaning. In the chart below, dark blue indicates Start of Period (SOP) Headcount, while light blue represents End of Period (EOP) Headcount. It is easy to see at a glance which number and bar corresponds to each metric. In another example, variation can be depicted when there are one or more Dimensions with more than one group selected. As shown below, the colour variation indicates Male and Female Gender – dark blue for Male and light blue for Female. Again, meaning is quickly and easily discerned with the chart legend. Tip #2: Set legend position to None to hide legend in single-colour charts What happens to chart legends when there is no colour variation? Single-colour charts such as the example below with a single metric like (EOP) Headcount over time don’t usually need a chart legend because all of the bars will be the same colour. In these situations, a chart legend is unnecessary as it only serves to repeat information. Tip #3: Hide metric names when only a single metric is included For cases where only a single metric is included, we recommend the Option to Hide Metric name as this information will likely be already mentioned elsewhere, likely in the chart title or the Storyboard page. In Tile Settings, slide the toggle for Hide Metric Name to On. For pie charts, the Hide Metric option will apply when Legend is set to None and Series named selected as Data Label. Tip #4: Choose a filter to limit chart clutter Chart legends also include the selections for the Dimensions. A selection controls what is visible on the chart, whereas a filter controls what the dataset includes. When only a single selection is made, they work similarly. However, selections show in the chart legend, while filters display on top of the chart and can be hidden if desired. Therefore, it’s best practice to choose a filter to limit chart clutter in cases where selection is used to select a single point of the data. Tip #5: Use Group legends for multiple metrics In the case that multiple metrics are included on a chart or the metric name is not mentioned anywhere else, Group legends should be turned on to avoid any ambiguity. Make your charts legendary Mastering the use of chart legends is essential for creating clear and effective data visualizations. By following these tips, you can ensure your charts are not only visually appealing but also easy to understand. Whether you're dealing with multiple metrics, color variations, or trying to minimize clutter, thoughtful use of chart legends can significantly enhance the impact of your data. Visit One Model's Help Center to learn more about custom chart legends.
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