Data Visualization
Every day, we face a deluge of data in the digital age. This information is not of its own value, but it houses valuable data that can shape the future of decisions and create a culture for innovation. While this raw data tells stories about events in the world, turning raw news into clear and actionable insights is not quite as simple.
This is where the visualization of data comes in; it provides an approach by which one can make the audience understand a given set of data. This is where we find the science behind data visualization and how it helps us understand things better by being clear about them.
What is data visualization?
Data visualization shows information or data in visual form. Data visualization tools use visual elements like charts, maps and graphs to provide an accessible way of finding trends or anomalies in data.
At its most basic, data visualization can simply be taken to mean presenting complex data in simple ways that are easier for us as a human audience to see and use.
Why is data visualization important?
As we have seen, data visualization is essential, as it offers people a quick and effective way of understanding information. Why Data Visualization is Key
Complex Information: Visualization brings out patterns and trends in complex data.
Patterns & Trends: Visualization helps users identify patterns, trends and correlations that might go undetected in data.
Decision Making: Simplified data in an easy-to-understand layout enables decision-makers to make decisions efficiently.
Facilitates Communication: Hands down, data visualization is the most effective way to present and report findings.
The Science Behind Data Visualization
Data visualization is a science of its own based on principles from other disciplines like psychology, computer science and design. The research on how people perceive and process information visually is invaluable to developing strong understanding of how to design effective visualizations.
Cognitive Load
Cognitive load is the mental effort to understand content. Good data visualizations can lessen the cognitive load of our users by making sense out information in a glance. One of them is with the use clear, simple visuals. Another way is to combine inclusive design and data visualization best practice.
Visual Perception
Humans are hardwired to consume information in the form of visuals. And that is why data visualization works so well. It is key when developing visualizations to take into account the reliance of human vision on visual cues.
E.g.: take color for categories or size/shape for value. This is a nice way to communicate data and make it more understandable by the user.
Gestalt Principles
Gestalt principles are laws that describe how the human eye sees visual elements. We can apply those same principles to data visualization in order to create more effective visualizations.
For example, the law of proximate suggests that things more accessible to each other be considered connections, and thus we can make similar points on top of one another.
Types of Data Visualizations
Different types of data visualizations serve different kinds of data and purposes. Here are a few common types:
Bar Charts: Your best way to compare one quantity with a different commercial category.
In-line charts: Best for displaying trends over time
Pie Charts: The Best Way to Show % of Total.
Scatter Plots: Represents the relationship between two figures.
Heat Maps: For data density or intensity
Best Practices for Data Visualization
Following these best practices will assist you in developing good data visualizations. Tips to keep in mind
Know Your Audience
Most of the work really lies in understanding your audience. Think about what your audience already knows and need to know, as well as how they will interact with your information.
Select a Visualization
Data will always dictate the best visualization—certain data is fit for specific types of charts, and different questions should be answered by viewing respective visualizations. Select the appropriate type of visualization to depict your data while answering questions for your users.
Keep it Simple
Do not overstuff your visualization with too many information. Do not make it too complicated and go for the targeted message you want to deliver through a design.
Use Color Wisely
While color is a strong tool in data visualization, it should be used with caution. Color them; it will make certain information a little bit protruding and separate between categories. However, use them wisely because using too many colors might make the visualization very confusing.
Label Clearly
Label all axes, legends and data points properly. This is the way data is represented and your audience will understand it without any doubt.
Conclusion
The best practice for displaying the data as a visual is data visualization. It converts data into interactive, easy-to-understand visuals so that people can quickly understand information and make decisions.
The more technological it becomes, the better evolved data visualization will be, providing impressions that are even closer to reality and understanding and utilizing appropriate ways/means of using details for insight.
Comments