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Most Unusual Data Visualization Techniques You Can Imagine

Why Do We Need Data Visualization Techniques?

Even in your first few years of school you will have been encouraged to turn data collected from a simple tally chart or spreadsheet and turn it into a bar chart. Raw data is notoriously hard to interpret and understand while a “picture” is worth a thousand words as is often said. Being able to use a data visualization tool such as a simple bar chart allows us to quickly see how different variables and data sets are related and how they affect each other. Something that may be impossible to spot purely by looking at a list of numbers in a spreadsheet. Visual representation of data is a vitally important part of any analysis work; not just for communicating what you have found but for also spotting patterns and relationships in the first place.

statistical data analysis schema

What Should Data Visualization Techniques Show?

Any data visualization tool will need to clearly show the quantitative data analysis that you are working with as well as communicate the right information to the intended audience. This often means having to select from several different possible data visualization techniques to find the right one to show what you need to show. On the whole a visualization tool needs to clearly;

  • Get the audience to consider the actual data and what it shows; not the tool or the methodology
  • Show the actual data in question
  • Will not distort that underlying data
  • Allow the presentation of a large amount of data in a small area
  • Make the information understandable; even with the use of large data sets
  • Be able to provide different levels of detail such as an overview or precise detail

Types of Different Data Visualization Techniques

There are a large number of different data visualization techniques that you can use but all seek to allow us to quickly see what is going on through simple visual techniques. While the human eye and the brain cannot quickly see trends or relationships within a simple spreadsheet or table it can very easily see differences in the sizes of bars on a bar chart or different colors and clusters of points on other forms of graphs.

The data visualization techniques that you choose should very much reflect the audience for the visualization. While an academic paper will expect to see more traditional representations of your data there is no reason not to think beyond these for other purposes. For instance for the 2012 Olympic games they created 19 milled plates that represented the 12 million or so tweets that were generated around the world; one plate for each day of the event. This artful data representation would certainly not be at home within an academic presentation but is definitely provides a way to communicate with its intended audience.

Below are listed some of the different data visualization techniques that you could employ to ensure that your data and findings are communicated to your intended audience effectively;

  • Bar charts and line graphs; while one of the simplest methods these are still some of the most effective ways of demonstrating data effectively
  • Network charts; use of color and size allows relationships to be clearly shown
  • Maps; overlaying data onto maps (or plans) can effectively show how factors change by location
  • Time charts; use of simple techniques such as Gantt charts
  • Scatter graphs; similar to line graphs but you are not just limited to 2D, it is possible to create a 3D scatter graph

TDA (Topological Data Analysis) allows the graphical representation of data in multiple dimensions

Interested in representation of data? Learn about it today!