The Importance of Data Visualization
It is the process of converting raw data at hand into easy and understandable image-photo-graphics for fast, effective and accurate decision making.
Everyone knows this name in the geography I was born, Florence Nightingale. She was a nurse until I wrote this article and learned about her. But at the same time, both a statistician and our headline have made studies that can be counted as almost the first examples for data visualization.
At the time 1843 Crimean War was going on Florence Nightingale was a nurse and she was collecting some data from the dead people in the war and these are the pictures of her datas. The picture above represents her data with a polar diagram.
To the best of our knowledge this diagram was used for showing the deaths in the hospital of army. During the Crimean War between 1853 and 1856, in 1854, Florence Nightingale was working as a nurse in the hospital of army¹ barracks where injured British Soldiers were having a treatment.
The statistical data from the army hospital and the visual presentation of this data to the British Empire had a great impact in that period.
So what does the diagram tell us?
Red zones indicate war injuries, blue zones indicate soldiers who died from preventable diseases, and black zones indicate soldiers who died from other causes. The diagram documented that many more soldiers died from cholera, typhoid and dysentery in the face of Russia’s attacks at that time. (Right side represents the first year and self side represents the second year) This situation convinced the decision makers of the period that more measures should be taken in hospitals.
Data Visualization in recent years
There has been a significant increase in the amount of data worldwide over the past 30 years. The biggest factors to this data increase can be said that the software that makes the raw data and the amount of data meaningful have increased and are easily accessible. Sometimes, it is very difficult to understand and interpret this gathered raw technical knowledge. This is where Data Visualization comes into play.
What is Data Visualization?
Data visualization is the graphic representation of data. It involves producing images that communicate relationships among the represented data to viewers of the images. This communication is achieved through the use of a systematic mapping between graphic marks and data values in the creation of the visualization. This mapping establishes how data values will be represented visually, determining how and to what extent a property of a graphic mark, such as size or color, will change to reflect changes in the value of a datum. [Source]
In short, Data Visualization; It is the process of converting raw data at hand into easy and understandable image-photo-graphics for fast, effective and accurate decision making.
It also has the power that allows us to see stories hidden among numbers and it triggers us to share and spread those stories.
Data visualization -especially- has become important in recent years Following the global economic crisis in 2008, the term data visualization has increased its share in Google searches significantly. Looking at the Google Trend data, we observe that these searches are increasing day by day in the chart for 2010–2020.
Data visualization workspaces have separate titles in themselves. In general, visualization studies are classified as Static and Interactive. The way the data is presented; data analysis affects the success of the visualization work with preferred technique, chart type, size and colors used. However, the success of data visualization is not only about design knowledge, artistic touches, but also needs to be blended with basic statistical knowledge.
In addition, the software tool used in visualization studies is also of great importance. The selected tool ; Whether code-based (Python, R, js, D3), web-based or on its own (such as Tableau, Power BI, Looker), the success of the visualization work will vary depending on the possibilities and conveniences that the tool offers or does not offer.
On the other hand, although a significant part of these tools are open source (free) or semi-paid (freemium) initiatives, Data Visualization is generally a workspace that requires a certain budget. The number of data visualization tools is increasing day by day. However, most of these tools (especially those providing additional facilities) offer paid solutions. This also includes data wrappers that offer free use at the beginning or in beta.
Despite all these difficulties, what keeps the data visualization alive is the existence of the free or freemium tool resources.
Although their numbers are few, many are either unknown or not yet discovered. Since I see this as a problem (since this article is longer than I thought), I briefly compiled data visualization tools that are both free and do not require code information in my next article.
Conclusion
Data visualization is a large IT field with many tools and working disciplines. When you add your dream world to the width of the field, it is really enjoyable to navigate the visualization ocean.
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Source:
https://www.sciencenews.org/article/florence-nightingale-passionate-statistician
https://www.dataceutics.com/blog/2018/4/27/a-passionate-statistician-florence-nightingale
https://www.sciencemuseum.org.uk/sites/default/files/styles/embedded_image/public/2018-12/E2015.0127.JPEG?itok=kyS1tNAP
https://www.britannica.com/biography/Florence-Nightingale/Homecoming-and-legacy
