This is nothing but data visualization! As mentioned, converting information or data into visuals such as maps, graphs, or charts makes it easy for the human brain to understand and drive insights. The main goal of data visualization is to understand the patterns and trends in large data sets.
Any tech company understands the importance of big data. However, only a few can process and interpret complex data into understandable formats.
Altogether, there are three data visualization categories: charts, graphs, and maps. In this article, we’ll walk through the types of data visualization, shedding light on their characteristics, use cases, and more.
Let’s get started.
Sharing the Categories of Data Visualization
The common types of data visualization are charts, graphs, and maps. By picking the proper data visualization for your data, you can unlock great insights and guide you toward making a firm decision in business success.
Discover New Insights with Graphs
Graphs play a crucial role in the world of data visualization. It presents data in value or organized formats, giving the correct insights. The chart shows the relation between two quantities, mainly two variables.
There are various types of graphs:
Line Graphs
Line graphs are great at showing trends over time. Additionally, they allow us to track the increase and decrease in the data points, unveiling patterns that would not be noticed otherwise.
Bar Graphs
Bar graphs are used for comparing different data sets. They give a clear overview of which data set is smaller, more significant, or equal.
Bullet Graphs
A bullet graph is a bar marked with extra encodings to share the progress toward a goal or performance against the reference line.
Showcasing the Result-Oriented Charts
Charts are another popular tool in data visualization. They offer a great way to compare, categorize, and comprehend data. Also, there are different types of charts, as given below:
Pie Charts
They usually depict the percentage or proportions. They offer a clear visual representation of how each part contributes.
Bar Charts
Works best for comparing different data sets or categories. They offer a visual representation of which type is larger or smaller or if they are equal.
Histograms
Histograms are used for the representation or distribution of data. They help to identify patterns, such as if the data is distributed normally or skewed.
Gantt Chart
Gantt chart is a different bar chart version showing the project schedule. The project managers generally use this type of chart for scheduling projects.
Unlocking the Power of Maps
Maps are the geographical representation of data visualization. They transform spatial data into visual expression, revealing patterns that would have otherwise been left hidden whether you need to visualize population density, market fluctuation, population density, etc.
Below, we’ve listed the different types of maps:
Choropleth Maps
Depicts the variations in data across different geographic regions, with the help of color gradients to represent magnitude.
Heat Maps
Highlight concentration or density patterns are used in fields like website analytics, consumer behavior, and crime analysis.
Flow Maps
Visualize movement or connectivity between locations, promoting transportation planning, supply chain management, and migration studies.
Cartograms
Distort geographical boundaries based on data values, offering a unique view of spatial relationships and distributions.
Best Practices for Effective Data Visualization
While there are endless possibilities with graphs, charts, and maps, implementing the best practices ensures clarity, accuracy, and impact of your data visualization.
Simplicity is the Key
Data visualization represents data insights in a way that is easy to understand and accurate. Make sure to include only the vital information. Less is always better and more.
Labeling
When dealing with graphs, provide precise titles, axis labels, legends, and comments to guide interpretation and avoid ambiguity.
Test Data Visualization with Users
Before deciding on the data visualization, testing it with the actual users is vital. This will ensure the visualization effectively communicates the data you want to analyze.
Be Consistent
Use consistent colors, scales, and formatting across different visualizations to promote interpretation and comparison.
Continuously Iterate
Data visualization is an entirely iterative process; thus, it is vital to endeavor for improvement. They update the visualization accordingly whenever new data or insights are obtained.
Wrapping it Up
In data analysis, graphs, charts, and maps are essential tools that enable the conversion of intricate data sets into insightful understandings. Data analysts can maximize the potential of their data by understanding the various types of data visualization and adhering to best practices. These three categories can be used to interpret data for your business, even though there are many more types of data visualization. Selecting the best one to display your data is essential to succeed in the vast world of data. To enter the pool of tech-related information, visit our blog posts.
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