Data is essential for making informed and effective decisions, but it can also be overwhelming and confusing if not presented and communicated clearly. In this article, you will learn some of the best tools and methods for data visualization and communication, and how they can help you improve your decision quality.
Data visualization is the process of transforming data into graphical or interactive forms, such as charts, graphs, maps, or dashboards. Data visualization can help you and your stakeholders understand complex data, identify patterns and trends, compare options, and communicate insights. Data visualization can also make data more engaging and memorable, and help you persuade and influence others with evidence.
I would change the phrasing here a tiny bit - "Data visualization WILL help you and your stakeholders understand complex data..." We know from lots of research in human visual perception and beyond that visualized data is pretty much ALWAYS better understood than the alternatives. Just Google "Anscombe's Quartet" for a compelling demonstration of the power of visualization as compared to looking at a table of numbers or statistical analysis of data.
When deciding on the right data visualization tool for you, there are many factors to consider. The type and size of your data, the level of customization and interactivity, the cost and availability, and the compatibility and integration should all be taken into account. You may need to look into different tools' capabilities for handling different data formats, sources, and volumes. Additionally, some tools offer more options for creating and modifying visualizations than others. Furthermore, some tools are free or open-source while others require a subscription or a license. Finally, some tools can easily connect and export to other tools or platforms while others are more isolated or proprietary.
Once you have chosen your data visualization tool, it is essential to design your visualizations in a way that conveys your message accurately and effectively. To do this, you should consider the needs and expectations of your audience, as well as the type and purpose of your data. Additionally, it is important to simplify and focus on the most important or relevant information, while using colors and shapes wisely to enhance the visualization. Lastly, add context and explanation for your visualizations to explain what they show, why they matter, and what actions or decisions they support.
Design is about problem solving. So what problem is your data visualization meant to solve? Consider the truth in your data, your audience needs, and your perspective. These are the three balancing points that should guide every decision, from chart selection, through setting scales, choosing colors, annotations, and labeling. Every time you need to make a design decision, just ask yourself if the approach will maximize your audience's ability see the truth in the data, based on the task they're meant to perform with it, and that your perspective is being communicated. All three of these MUST be in balance. (Your perspective can't be in conflict with the truth or audience need, for example.)
Creating data visualizations is not enough; you also need to communicate them effectively to your audience. Whether you are presenting your data in a report, a presentation, a dashboard, or a story, it's important to consider the structure, language, tone, and delivery of your data communication. Organize your data in a logical and coherent way with headings and subheadings. Use clear and concise language that your audience can understand. Select a tone that matches your purpose and audience. Make sure to deliver your data communication in an engaging way that captures and maintains attention. Utilize verbal and non-verbal cues, as well as visual aids, such as slides or videos. Interact with your audience by asking questions or inviting participation.
While all the tools in the world will help you create the data visually appealing,the delivery of it is the ultimate crux. Practicing your pitch till it sounds natural to you is crucial in your overall performance.
The final step in data communication is to evaluate and improve your results. You need to measure the impact and effectiveness of your data communication, as well as identify areas for improvement. To do this, collect feedback from your audience on their level of understanding, satisfaction, engagement, and action. Test your data communication with a sample of your audience or a peer group by using prototypes, mockups, or pilots. Additionally, learn from your data communication experience and make necessary updates. Track changes and trends in your data, audience, and purpose. Look for new tools, methods, or best practices for data visualization and communication.