Data visualization

What is data visualization?

Data visualization is the representation of data in a visual format such as graphs, charts, and other visual aids. Data visualization assets are found in dashboards, reports, or notebooks. They are usually set up by the data team which choses the best representation of their analysis.

Data visualization allows users to see patterns, trends, and relationships that may not be apparent in raw data. By using visual aids to represent complex data, users can quickly identify key insights and take action.

Data visualization and business users

In today's fast-paced business environment, decision-makers need quick and easy access to relevant data to make informed decisions. Data visualization provides an efficient way to convey information to users, regardless of their technical expertise. Visual representations of data enable business users to quickly identify trends and patterns, identify outliers, and make informed decisions.

Types of data visualization

There are many types of data visualization, including line charts, bar charts, pie charts, scatter plots, and heat maps. Each type of visualization is best suited for specific types of data, and it is essential to choose the right one to ensure the most effective communication of information. For example, funnel charts are excellent for visualizing a sequential process or a series of steps with different stages or levels, such as measuring a conversion rate - as pictured under.

Best practices to follow

Effective data visualization requires careful planning and execution. Here are some best practices to follow when creating visualizations:

• Know your audience: understand who will be viewing the data and what their goals are. This will help you design visualizations that are relevant and impactful.
• Keep it simple: avoid cluttering visualizations with too much information. Stick to the most critical data points and use color, font, and layout to make the information easy to digest.
• Use appropriate charts and graphs: select the right chart or graph for the data you are presenting. For example, line charts are best used for displaying trends over time, while bar charts are better for comparing data between categories.
• Label axes and legends: ensure that axes and legends are labeled correctly and clearly. This will help users understand the information quickly and accurately.
• Consider interactivity: interactive visualizations enable users to explore data and uncover insights on their own. Use interactive features to help users drill down into the data.