Are you someone who considers data and numbers to be blatantly boring? Before you are so quick to judge, you should know that jobs in the field of data will increase by 35% between 2022 and 2032. In this scenario, it is smart to develop your ability to deal with data to acquire in-demand job positions.
But more than crunching numbers is needed to thrive in the professional world of data science. You need to know how to weave persuasive data narratives. For that, you will have to embrace the power of charts and tell intricate stories through them.
So, what are you waiting for? Check out some of the principles of data storytelling here:
- Establish a clear plotline
A great data story begins with a clear and defined objective, serving as a guiding force for your approach and perspective. As you determine these, it’s important to construct an engaging plot line consisting of key elements for data storytelling. Most importantly, every aspect of your plotline must adhere to your narrative. Any visual or written component that doesn’t seamlessly fit in must be replaced with one that propels your story forward.
- Choose predictable chart layouts.
Human beings are naturally attracted to visuals and their ability to convey important information quickly. Our brains are constantly programmed to look for patterns. So, if the visuals we are presented with are chaotic or nonsensical, it becomes easier to make sense of the data.
In order to effectively utilize this innate human inclination, use a chart maker that lets you structure the visual representation of data logically. This could mean organizing it numerically, alphabetically, or in a sequential order that aligns with our natural cognitive processes.
For instance, if the language used reads from left to right, it is important to present the data in a left-to-right format. Additionally, when using multiple graphs, consistency is key. Ensuring a clear connection between the data and maintaining a consistent order will prevent viewers from becoming lost as they interpret the information.
- Provide adequate context
Providing enough context is a key part of proper data visualization. Each aspect, from the setting and characters to the conflict and resolution, serves a significant purpose. This means that even if your data does not suggest a negative conflict, it is still vital to provide a resolution for the audience.
For instance, if your data showed success with a new pricing strategy, simply stating its effectiveness would not suffice. Your resolution should not only confirm the continuation of the change but also propose potential enhancements for the future.
- Be smart with colors
Color plays a significant role in data storytelling as it can effectively convey a message without the use of words. However, color should be used strategically to emphasize and elevate information. An excess of colors can result in a chaotic and overwhelming presentation, while using a single color or multiple shades of one color can lead to the data blending together.
Additionally, the choice of colors is important. Opt for intuitive colors that the viewer can easily comprehend, enabling them to process the information quickly. For example, when dealing with temperature data, it is wise to use red to signify heat and blue to denote cold.
Furthermore, the utilization of color can greatly impact the overall data narratives. Consistency in color across values can provide a sense of coherence, while contrasting colors can effectively highlight disparities in the data.
- Apply text carefully
Words have the power to enhance your data visualization, whether it’s the type of text you choose or the quantity used. While an overload of text or a lack of visual organization can be distracting, more than just relying on images may be required. As you incorporate text, be sure to highlight key details. Though our brains are wired to prioritize patterns and images, strategically incorporating relevant text can greatly impact your overall visualization.
When using text in data visualizations, focus on brevity, clarity, and amplification of the visual narrative. Extract key data stories and communicate them succinctly through clean headlines, summaries, and captions. Avoid walls of dense text that compete with graphical elements. Instead, let text and visuals complement one another. For example, pull out significant statistics from a chart and feature them prominently.
Future Trends in Data Storytelling and Visualisation
Data storytelling and visualization have come far, but given the rapid pace of technological advances, more innovative trends are on the horizon. Interactive dashboards will likely become more prevalent, enabling users to dive into data and customize views dynamically. We may see wider adoption of augmented reality, virtual reality, and 3D modeling to immerse audiences in data environments.
The rise of data journalism could also make visualizations more mainstream as reporters translate complex information into interactive infographics for media outlets. And as datasets scale up, expect smarter AI-driven tools to help automate analysis and pattern detection.
However, more than flashy tech is needed to ensure effective data representation. Design principles like clarity, meaningful context, and aesthetically pleasing visuals that spotlight the data will still be critical.
Another trend is using more artistic mediums to bring datasets to life through sculpture, paintings, installations, and the like. More focus will also be placed on ensuring data provenance and unbiased storytelling.
The key principles covered here provide a strong foundation for crafting compelling, persuasive data stories with charts. Focus on visual clarity, accuracy, audience needs, and purpose. Let the data take center stage through clean design and smart text integration. Make it engaging yet truthful. Follow best practices, but be bold and innovative. As skills develop, you can push boundaries to invent new narrative forms. Moreover, if you need more advice regarding chart storytelling, grab your copy of Storytelling with Charts now.
- What sets data visualization and data storytelling apart?
While data visualization presents information through charts and graphs, it lacks context. On the other hand, data storytelling utilizes visual aids to convey insights extracted from data effectively. The key difference is that data visualization focuses on how to display the data itself accurately. Data storytelling concentrates on why the data matters and what it signifies. Storytelling provides meaning, emotion, and memorability by framing the data within a compelling overall narrative.
- How can data be transformed into a compelling narrative?
It involves introducing and delving into the characters involved, establishing the setting of the data, identifying the conflicts it presents, and proposing potential resolutions. A key part of the process is deciding what story you want the data to tell. Determine the major takeaways and use supporting data to illustrate those key points. Think about how to structure the narrative in a way that will engage your audience. Bring dry statistics to life by tying them to real-world implications.