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Top 10 Data Visualization Techniques You Should Know

Top 10 Data Visualization Techniques You Should Know

Top 10 Data Visualization Techniques You Should Know

The graphical representation of information and data with the help of visual elements such as graphs, maps, charts, etc., is called data visualization. Businesses these days have access to a lot of data. They can use them to understand trends, predict patterns, and see if there are any outliers that require their immediate attention.

We are in the age of big data, and data visualization is a tool that many businesses cannot afford to ignore. Data visualization helps you tell stories that eventually make you understand your customers on a deeper level.

Let us look at the top 10 data visualization techniques that you need to know:

  • Histogram:

It is a graphical representation of information that uses bars of various heights, and each of these bars group numbers into ranges, and is also called a bar chart. A taller bar implies that a lot more data falls in that range.

  • The histogram shows the shape and spread of continual data
  • It shows the exact portrayal of mathematical data and it relates with just a single variable
  • This technique makes it easier to identify different types of data
  • You can determine statistical information with the help of the data presented in the histogram
  • Heatmaps:

It is a pretty unique way of representing data as it uses colors to mark different values. Thanks to the difference in color, the viewers will be able to grasp the trends more accurately. Heatmaps are great for visualizing correlation tables and for visualizing the missing values in the data.

  • Helps visualize the volume of locations/events with a dataset
  • They provide a great way of understanding how your users are behaving on your website or mobile app
  • It even enables analysts to segment and filters the data which helps you see how different types of users are engaging on a particular landing page
  • Charts:

One of the most popular types of data visualization techniques, there are several varieties of charts. We have already discussed bar charts aka histograms. The other types are line charts, pie charts, scatter charts, and bubble charts.

Line charts are used to plot the relationship of dependence of one variable on another variable. If you want to show continuously changing data over a long period, line charts are great.

Pie charts are circular statistical graphs where the pieces are used to denote a numerical ratio. The arc size of each piece is equal to the amount it indicates.

Scatter charts are two-dimensional plots denoting the joint variation of two different data elements.

Bubble charts are scattered charts, except that the data points are replaced with bubbles.

  • Word Cloud:

A word cloud is used to denote the frequency of the words that are used in a collection of text with its general size in the form of a cloud. This technique is usually employed on unstructured data to show how frequently a particular word occurs.

  • The bigger and bolder the word appears in the word cloud, the more often it is mentioned within a given text
  • It is a cheaper option to analyze text from online surveys
  • Word clouds are great to analyze answers for open-ended questions
  • Gauge charts:

Also called speedometer charts or dial charts, they use needles to show information as if you were reading them on a dial. When you use a gauge chart, the value for each needle is read against the colored data range or chart axis.

  • Used exclusively in executive dashboard reports to show KPIs
  • They are helpful for the comparison of a small number of variables
  • Types of gauge charts- Speedometer, rating meter, quarter gauge, linear scale, cylinder fill, LED gauge, bulb gauge and thermometer gauge
  • Easy for non-technical viewers to take insights from it
  • Wedge stacked graphs:

They are a type of visualization technique that displays hierarchical data in a radial system. Wedge stacked graphs are great for illustrating multi-level frequency data.

  • This graph type can depict negative values
  • The wedge graph type is not affected by the object size and number of side indicators
  • If you choose wedges on the stacked graph, then the graph type shifts to walls that are stacked
  • Streamgraphs:

It is a type of stacked area graph which is displaced around a central axis that gives it a flowy and organic shape or looks like streams. You should look for the peaks and the shallow periods over a period of time to understand the patterns. Check out the different colors which can be used to identify patterns or outliers.

  • They are ideal for displaying high-volume datasets
  • It can also be used to visually represent the volatility for a large set of data and how it changes over a period of time
  • It should be used for viewers who do not want to spend a lot of time trying to decipher the graph
  • Dendrograms:

It is a branching diagram that represents the relationship of similarity among a group of entities. The playoff tournament diagram, which is used commonly in clustering and cluster analysis, is the most common example of a dendrogram.

  • They are used to show the hierarchical relationship which exists between objects
  • It provides one of the best ways to allocate objects to clusters
  • Dendrograms are best suited when you only have a small number of findings
  • Box plots:

They give a visual outline of information through quartiles. Also called a box and whisker plot, they display the five-number summary of a set of data.

  • Often used in explanatory data analysis
  • It is used to show the shape of the distribution, central value, and variability
  • Boxplots work best when the sample size is at least 20
  • Treemaps:

It displays hierarchical data as a set of nested rectangles. Treemaps are an alternative way of visualizing the hierarchical structure of a tree diagram while representing quantities for each category through area size.

  • Treemaps were developed as a way of visualizing a vast file directory on a computer
  • They are a compact option for displaying hierarchies
  • To compare proportions between categories through their area size, treemaps are the best

Conclusion:

Creating effective data visualization is more than just about choosing the right kind of visualization technique. There are a lot of things that you need to have in place before presenting your data. The first step is to understand your audience and also ensure that there are no unnecessary distractions while viewing the data. Data plays a pivotal role in organizations these days, and you cannot afford to not employ them.

If you are looking for help with data visualization solutions, get in touch with the experts at Zuci who can get things started for you.

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Lini Susan John

Chatty & gregarious, you can find her with her baby plants when not with her marketing team.