Best Types of Data Visualization

Learning how to correctly display data may be the first step in utilising data analytics and data science to your advantage and the benefit of your company. By improving your abilities in common data visualisation techniques, you may take advantage of data-driven decision-making, which has numerous advantages including increased confidence and significant cost savings.

Depending on the kind of data you're working with and the story you're trying to tell with it, you'll use a different data visualization technique. You can improve your effectiveness in your role by using a variety of data visualization techniques. Here are some of the most important data
visualization techniques all professionals should know.

Tables. Tables are useful for communicating various quantities on different scales or multiple units of measure. But tables need to be read.

Scatter plot. Use a scatter plot to illustrate the link between two variables, such as the association between a customer's degree of satisfaction and the number of times they visited the store.

Although scatter charts can be used to display correlations between a variety of data sources, they perform best when at least one of your variables has continuous numerical data. When you wish to draw attention to the distribution of data involving two variables, scatterplots are very helpful. To get a feel of where your business falls on the scale of firm size vs. total revenue, for instance, you can use a scatter plot.

Line graph. or graphs are useful for plotting continuous
data and do not make sense for categorical data. Line graphs are often used to plot time. Line graphs are also useful for comparing multiple lines or trends. It illustrates how values relate to each other.

Histogram. Use a to see how a single variable is
distributed or spread out. One of the most well-known histograms is the "bell curve"-shaped distribution of IQ scores in a population, which
you may be familiar with.

How many people—or what percentage of the population—fall into each IQ range? What is the "distribution" of IQ scores within the population? Histograms might have X axes that use a lot of small data bins or a smaller number of larger data bins. The Y axis may display a proportion or a total count (which could be a fraction or percent value).

Bar Chart. A bar or line chart can be your first choice if you wish to compare one thing to another. Line charts work better for portraying longitudinal data (data collected repeatedly over time, on a periodic, or rolling basis) than do for cross-sectional data (data acquired only once and showing the status of the data at that point in time).

Pie Chart. are excellent for depicting proportions or comparing parts of an entity to their entire. Pie charts are best suited for audiences who might not be familiar with the information or who are simply interested in the essential takeaways because they are generally straightforward and simple to understand. Pie charts can't adequately display complex information for readers who want a more in-depth explanation of the data.

Box and Whisker Plot. Box-and-whisker charts and radial charts allow for more complex comparisons. Examine these more complex chart types to contrast data distributions between categories or to contrast degrees of extremeness between various scales of measurement, if appropriate.

Choropleth Maps. A represents numerical values over geographical areas by using color, shading, and other patterns. To distinguish between high and low values, these representations use a progression of color (or shading) on a spectrum.

Viewers can observe how a variable change from one region to the next using choropleth maps. The fact that the colors represent a range of values makes it difficult to access the precise numerical values, which could be a drawback to this style of display. However, you may add interactivity to your map using some data visualization tools so that you can get the precise values.

Knowing which technique is suitable for your needs is not enough to produce good data visualizations. You should consider several factors to convey data as effectively as possible. If you're presenting financial data to a non-technical audience, it's important to choose an easy-to-read illustration.

Avoid unnecessary distractions like animation, which can distract from the key points the illustration is trying to convey. Be mindful of the colors you utilize, as well as your overall design. Low contrast and bold colors can make it difficult for your audience to discern differences between data points.

Data visualization is a talent that all professionals need to have, regardless of their position or title within an organization. When it comes to sharing information with people inside and outside of your company, being able to successfully communicate complex data through simple visual representations is invaluable. Communicating data effectively adds credibility and impact to your business decision making.

Are you interested in automatization of data visualization technique? Learn more about self service tools that can help you use data to generate insights and tackle business decisions.