What is Data Visualization?
One of the most useful tools available for presenting complicated material in an accessible way is data visualization. This rapidly evolving field is focused on using visual representations like graphs or charts to convey raw data. Presenting data in a visual manner makes it easier to understand and faster to process, even for those who aren’t mathematically inclined or trained in analytics. These visual representations of data aren’t just visually appealing, they also tell a story about the information, allowing audience members to spot outliers, notice trends, and see patterns emerge from data. Visually conveying points is a powerful way to leverage data in order to achieve a desired outcome.
There are many kinds of data visualizations, each of which serves a specific professional purpose. Some of the most popular techniques for conveying information are:
- Histograms
- Waterfall charts
- Area charts
- Scatter plots
- Infographics
- Maps
- Pie charts
- Bar charts
- Box-and-whisker plots
- Heat maps
Because we live in an increasingly visual culture, those who know how to present information in visually engaging stories have the power not only to help make sense of past events but to offer predictions for the future as well.
This article will provide a brief overview of the history of data visualization, from ancient times to its present-day uses.
A Brief History of Data Visualization
Ancient history: While it’s not possible to precisely trace the ancient history of data visualization, it is thought that the earliest data visualizations were likely sand drawings and rock scratchings. The Lascaux cave paintings date back approximately 40,000 years and are thought to depict astronomical illustrations of the constellations that would have been observed during this time.
The ancient Greeks, Chinese, Babylonians, and Egyptians each found dynamic ways of visually depicting information so that they could chart the movement of the stars, as well as help with navigation, plan cities, and schedule crop planting. The Babylonian World Map, created in 600 B.C.E., was one such example. This document was drawn on clay. Eventually, papyrus became a popular medium for visualizing data, as it was easier to share and edit documents on papyrus.
1600s: Before the 1600s, the field of data visualization revolved mostly around mapmaking, in which roads, cities, resources, and landmarks were displayed. However, with time, there was a greater need for better and more precise measuring and mapping, and for more accurate visualizations.
Flemish Astronomer Michael Florent Van Langren was the first to present a visualization of statistical information. In 1644, he designed a one-dimensional line graph displaying twelve estimates of the difference in longitude between Toledo and Rome. This was the first known instance of an individual using a graph to depict variations in estimates, rather than a table.
1700s: Thematic mapping emerged during this century. This sort of mapping involved collecting economic, medical, and geologic data. Toward the end of the 1700s, new techniques and methods were introduced, such as measurement error, empirical data collection, and abstract graphs of functions. It was during this time that Scottish Engineer William Playfair invented a variety of diagram types, such as the pie chart, circle graph, and bar chart. In addition to Payfair’s contributions, this time period also saw the invention of several other popular types of charts, such as time series plots, contour plots, scatterplots, and histograms.
1800s: The “Golden Age” of statistical graphs took place in the second half of the 1800s. There were many contributing factors that led to this Golden Age: The Industrial Revolution informed the creation of the modern business model; official government statistical offices were introduced to help bring awareness to the global populace; an increased pressure to make sense of large sets of data pertaining to medicine, military, transportation, and commerce.
During this century, French Civil Engineer Charles Minard proposed combining statistics and cartography. In 1845, he developed a “flow map” depicting the area around Dijon and Milhouse in France, which showed traffic data that had been collected on area roads. His flow map was given to the stakeholders in that area before a new railway line could be built. In addition, John Snow mapped the cholera outbreaks in the London Epidemic of 1854, and in 1869, Charles Minard charted the number of men in Napoleon’s 1812 Russian campaign army.
1900s: Some consider the early 1900s to be the “Modern Dark Ages” of data visualization. Statisticians favored exact numbers over images, which they viewed as inaccurate. However, even though innovations within the data visualization field slowed considerably during this time, the 1900s did lead to an increase in the public consciousness of data visualization. Textbooks, as well as business, government, and science documents incorporated various types of graphs and charts.
In the second half of the twentieth century, the field of data visualization experienced a rebirth of sorts, which was a result of computer processing entering the arena. Computers revolutionized the way Statisticians could gather and store larger volumes of data, and also provided a way to easily depict this information visually. In the 1960s in America, John Tukey invented the science of information visualization, and in France, Jacques Bertin made advances with information visualization cartography. Then, in the 1980s, Edward Tufte wrote The Visual Display of Quantitative Information, a seminal work on statistical analysis and data visualization that is still being taught at universities today.
2000s: From the last decade of the 1990s up to today, the field of data visualization has grown and evolved to include hundreds of focus areas. Software developed during this time, such as data discovery tools, scorecard applications, analytics suites, and dashboards, empower researchers and those working in business to find new, creative ways to interact with their data.
The current state of data visualization is an exciting time for those working with data. New ways to find, aggregate, and visualize data are constantly being discovered. However, with these developments are the challenges of protecting data privacy and preventing the misuse of data.
The Future of Data Visualization
More data is being created than ever. With this onslaught of data comes the need to continue to find new ways to effectively visualize it. This is why the role data visualization plays in businesses continues to gain importance in 2022. As new technologies like augmented virtual reality emerge that deal with multidimensional imaging and intelligence, as well as new cognitive frameworks, large amounts of complex data can be visualized and presented in new and dynamic ways.
As more data becomes available on different platforms, different challenges and considerations may emerge for visualizing it. New departments will likely emerge in the future that will be devoted to finding additional methods for pre-processing, character recognition, and post-processing of visual information. In order to accomplish this, artificial intelligence, along with machine learning, is expected to be increasingly used by tech companies.
Hands-On Data Analytics & Visualization Classes
Noble Desktop’s data analytics classes are open to students with no prior coding experience. These full-time and part-time courses are taught by top New York Data Analysts and provide timely and hands-on training for those wishing to learn more about topics like Python, SQL, Excel, or data science, among others.
In addition, Noble’s also offers live online data visualization courses for those looking to learn how to transform their data insights into engaging visualizations. These courses are a great option for students who prefer learning in the virtual format. Noble Desktop’s Classes Near Me tool is also available for those who want to locate other data visualization courses in the area. Over 200 courses are currently listed, from three days to five months in duration. Classes cost between $119 and $12,995.