10 Must-know data visualization techniques in 2023

In a world flooded with data, data visualization is a crucial element as it can guide us effectively in making well informed decisions. Leveraging data visualization tools and techniques we can extract valuable information which help businesses and individuals manage the challenges. Here are the 10 data visualization techniques that you should know:

Interactive Visualizations: Interactive data visualizations help users to understand information better allowing us to explore and interact with data in real-time. It's not just a static image. In interactive visualization, we can click, hover, or interact with it to get more information. They often involve elements like tooltips, filters, and drill-down options. Interactive data visualization helps us make smarter choices based on the insights we gain leveraging it.

Artificial Intelligence:  Nowadays, people are using machine learning and artificial intelligence to automate the process of data visualization. When AI and Data visualization intertwine, it helps people to analyze big sets of data and build complex models that use NLP and learn from data. AI also facilitates suitable visualizations suggestions based on the content and context.

AR and VR Technology:  AR and VR technologies are being used to create immersive data visualizations, particularly in fields like gaming, education, and training. Data scientists can use 360-degree virtual environments to create highly detailed and realistic models of complex datasets. These models can include multiple dimensions of data which makes it easier for regular people to understand. Likewise, augmented reality (AR) is also used for data visualization and enhancing real-world experiences with digital information. These techniques can provide a unique way to explore complex data.

Infographics:  Infographics and data art blend data visualization with artistic design. It is like a visual tool that helps people understand a complex story or information. Infographics include small charts, graphs, and catchy text with pictures. They often use creative and visually appealing elements to communicate data-driven messages. It makes the data or message more appealing and easier to grasp.

Streamgraphs: A variation of stacked area charts that are used to display changes in data over time. It's handy when you have multiple sets of data that overlap or stack on top of each other. Instead of traditional lines or bars, it uses colorful layers to represent each dataset. Each layer starts at a common baseline and moves up and down to show how the data changes. This way, you can easily see how different data sets evolve over time in a visually appealing manner.

Real-time Dashboards:  A real-time dashboard is like a live report that shows constantly changing information. It shows us updated information and can be seen by many people at the same time. It's useful for monitoring data that's constantly changing, like website traffic or stock prices, as it provides up-to-the-minute information. As real-time data is becoming increasingly available, dashboards that update continuously are becoming more common. These dashboards are used in fields like finance, IoT, and social media monitoring.

Data Storytelling: Data storytelling involves creating narrative-driven dashboards that guide the viewer through the data. Storytelling data dashboard helps people to understand and draw insights from it. The techniques help make data more engaging and relatable.

Small Multiple Visualizations:  Small multiples involve creating a grid of small, similar charts or graphs to compare multiple datasets or time periods. They help in quickly identifying patterns and differences.

Data Sonification:  Data sonification is the process of representing data using sound. While data visualization helps you understand information by looking at it, data sonification does the same thing but through your sense of hearing.  It's an emerging technique that can be valuable in conveying information to people with visual impairments.

Explanatory Data Visualization:  Explanatory data visualizations focus on guiding the viewer through the data and explaining the insights in a clear and understandable way. They are often used in journalism and data-driven storytelling.