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Home Machine Learning

Information Visualization Defined: What It Is and Why It Issues

Admin by Admin
September 22, 2025
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appeal to all of the hype lately inside knowledge science, however I’d argue they’re each secondary to a extra necessary—and often-ignored—part of the sector.

When coping with knowledge, there are two important steps:

  1. Processing and analyzing the information to extract significant insights.
  2. Conveying these insights to others.

The second level is essential and sometimes missed. The world’s most superior algorithm or useful perception is ineffective if nobody can perceive it. As a knowledge scientist, you have to be taught to convey your insights to others. There may be a couple of cause for this, with the obvious one being that if the fitting folks perceive the information, the world at massive will profit. Nevertheless, there may be one other equally necessary cause: It’s usually in describing our findings to others that we uncover errors, extra profound information, or additional areas for exploration.

On this article, we’ll study a strong and efficient device which can assist obtain the second step above: knowledge visualization. That is the primary in a collection of articles that can take absolute novices deep into the realm of information visualization. This primary article is basic and lightweight, meant as an introduction to the sector as a complete. In later articles, I’ll get into the extra technical elements, finally concluding by instructing you easy methods to construct your individual knowledge visualizations.

With that information, you’ll be armed to sort out your knowledge in new, thrilling methods.

“The best worth of an image is when it forces us to note what we by no means anticipated to see.” –John Tukey

What Counts as a Information Visualization?

Many individuals view knowledge visualization by means of a restricted lens, solely classifying normal graphs, akin to bar charts, line charts, and the like, as true knowledge visualizations. Seen from this angle, knowledge visualization didn’t materialize till the center of the 18th century. (We’ll see some examples under.)

Nevertheless, we might do nicely to broaden our minds. Visible transformations of information are in no way restricted to our conventional concepts. They’ve been round for hundreds of years. For instance, right here is the Imago Mundi [1], the oldest recognized map on the earth, found as a relic of the traditional metropolis of Babylon:

Picture Supply: Wikimedia Commons

This map locations Babylon on the middle and was doubtless a particularly great tool for visualizing what we now formally name geospatial knowledge. It is among the world’s earliest knowledge visualizations.

There are a plethora of comparable figures and pictures from varied historic civilizations—cave work, calendars, stone carvings, even Egyptian hieroglyphics—these are all successfully visible representations of information that have been obscure of their preliminary kind. Viewing these examples as knowledge visualizations leads us to an necessary precept:

At its core, knowledge visualization is nothing greater than taking some knowledge—be it numerical, textual, or in any other case—and making use of a metamorphosis to signify it visually.

This foundational precept results in a number of associated subjects primarily involving the simplest strategies to conduct these transformations, the place efficient loosely interprets to “trustworthy, straightforward to grasp, and informative.”

Early Examples of Information Visualizations

Now that we now have broadened our views regarding what constitutes a knowledge visualization, allow us to check out some trendy examples. Beneath is a chart from 1644 developed by Michael Florent Van Langren [2]. It is among the earliest graphical representations of what we contemplate to be conventional statistical knowledge, depicting estimates of the distinction in longitude between Rome and Toledo.

This map depicts 12 estimates of the distinction in longitude between the cities of Rome and Toledo.

Let’s contemplate a extra concerned instance subsequent—one which immediately highlights Tukey’s quote above.

Beneath is a map of London’s Soho District in 1854 [3]. It was designed by John Snow in an effort to decide if there have been any patterns within the cholera outbreak that was debilitating the city on the time:

A map of London’s Soho District depicting deaths from cholera throughout an outbreak in 1854. Picture Supply: Picryl Public Area

Trying towards the middle of the map, we will see an exceptionally massive variety of deaths close to the water pump on Broad Avenue. An investigation decided that this pump was contaminated and was a serious reason for the unfold of the illness.

This instance highlights precisely the precept from John Tukey we famous above: The most effective makes use of of information visualization is to shortly see insights which are tough to search out within the knowledge’s preliminary kind.

Precision and Flexibility

Information visualization is a broad and deep subject that may be approached in some ways. That mentioned, there are two ideas that you must remember regardless of the particular type of knowledge visualization you interact in: precision and flexibility.

A great knowledge visualization doesn’t attempt to accomplish ill-defined duties, akin to displaying the essence of or summarizing all the pieces necessary a few knowledge set. Statements like these are subjective and basically inconceivable to attain.

Relatively, a superb knowledge visualization highlights a selected and well-defined side of the related knowledge in a approach that makes it simpler to grasp for the consumer. You must at all times articulate precisely what you wish to categorical about your knowledge earlier than you even start designing a visualization.

To internalize this precept, it’s useful to recall what the aim of a knowledge visualization is to start with: to show insights from a knowledge set in a transparent and helpful approach. We wish to make the information simpler to grasp. Being exact ensures we obtain this purpose. A visualization that makes an attempt to do an excessive amount of would possibly find yourself complicated the viewer much more. It’s a lot better to supply a visualization which covers much less knowledge in a clearer approach. High quality is extra necessary than amount.

Check out the information desk under, which incorporates details about salaries from completely different cities round the US.

Identify Metropolis Earnings Occupation
Sarah Mitchell Denver, CO $72,500 Advertising Supervisor
Jamal Rodriguez Houston, TX $58,300 Electrician
Priya Desai Seattle, WA $91,200 Software program Engineer
Thomas Nguyen Chicago, IL $64,800 Nurse

Which of the next is the higher visualization alternative for the above knowledge?

  1. A visualization that makes an attempt to simplify the knowledge within the knowledge desk utilizing a bar chart that has names on one axis and salaries on the opposite axis, makes use of coloration to distinguish amongst cities, and makes use of a texture on the bars (dashed strains, diagonal strains, and so forth.) to differentiate amongst careers.
  2. The identical visualization as above, however this time excluding the majors. In different phrases, a bar chart of names and salaries which colours the bars based mostly on location.

It’s tempting to decide on the primary one, however the truth is, it tries to do an excessive amount of. Higher to show restricted, focused data than to confuse your viewers.

Along with being exact, sustaining flexibility can also be necessary. There isn’t any such factor as an ideal knowledge visualization. There may be at all times room for enchancment, and knowledge visualizations usually develop into higher with every revision. After all, sooner or later, a knowledge visualization should be shared with others and serve its goal.

This results in a quandary—how a lot revision is sufficient revision? There isn’t any definitive reply to this query. The method of revising a visualization should be undertaken with care. Asking too many individuals for recommendation will doubtless end in a bunch of half-baked, conflicting opinions. However, publishing the primary draft of a visualization—i.e., not revising it in any respect—is more likely to result in a subpar outcome.

Though there isn’t a excellent resolution, there are a number of tips you possibly can observe:

  • Establish 2-3 folks to provide you suggestions in your visualization.
  • Strive to make sure your checklist of individuals encompasses the next:
    • A reviewer who’s proficient in designing knowledge visualizations
    • A reviewer who has a robust understanding of the information that’s getting used to develop the visualization (e.g., a political scientist for election knowledge)
    • A reviewer who’s a part of the meant viewers for the visualization
  • Undergo 2-3 rounds of suggestions and revision with this similar checklist of individuals. It will be sure that enhancements to the visualization are steady and logical.

Last Ideas and Trying Ahead

In some ways, knowledge visualization is akin to writing. Even probably the most prolific and proficient authors have editors, and their books undergo intensive revision earlier than being authorized for publishing. Why? For the straightforward cause that good writing is essentially depending on the viewers, and thoroughly curated revision ensures the perfect expertise for the eventual readers of a guide. The identical thought applies to knowledge visualization.

By following these tips, you possibly can make sure you develop a strong knowledge visualization which is grounded in greatest practices, appropriately shows the information at hand, and is comprehensible for the meant viewers.

They’re the important thing to efficient knowledge visualization, and the inspiration for superior visualization methods that can be mentioned in future articles. Till then.

References

[1] https://commons.wikimedia.org/wiki/File:The_Babylonian_map_of_the_world,_from_Sippar,_Mesopotamia..JPG
[2] The Visible Show of Quantitative Info, Edward Tufte
[3] https://picryl.com/media/snow-cholera-map-1-cbadea

Tags: DataExplainedMattersvisualization

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