If a picture is worth 1,000 words, then a single data visualization can be worth millions.
Since the dawn of human civilization, people have developed methods to visually represent data in order to make it easier to understand, memorize, and transmit. Today, all manner of businesses and institutions use visualizations to convey information about subjects like inventory management and trade routes, but also about more entertaining topics like sports stats or pop culture. The history of data visualization is a long one: here is a mere sampling of some examples from different points in time that demonstrate how visualization has changed over the course of history.
Visualization Before Computers
The history of data visualization has been a long one. It can be traced back to antiquity when simple charts were used for accounting and record-keeping purposes. As writing evolved from symbols on rocks and clay tablets into alphabets, so did the ability to represent information visually. There is an evolution from tick marks on cave walls to symbols for groupings to maps for trade and commerce.
The earliest known visual representation of statistical data is believed to have stemmed from the work of Michael Florent van Langren (1598–1675), a Dutch astronomer who published maps illustrating his calculations regarding eclipses in 1644. He developed the first known graph of statistical data to estimate longitude, displaying the broad range of estimations of the distance in longitude between Toledo and Rome.
Basic Charts and Graphs
The history of data visualization is largely a story of statistical thinking, the widespread collection of data for planning and commerce up through the 19th century, advances in technologies for drawing and reproducing images, new developments in mathematics and statistics, and new ways to collect empirical observation.
William Playfair (1759 – 1823) was an engineer and political economist who is considered the father of statistical graphics. He invented the line graph we use so often today (along with many other innovative ideas). He also created another popular chart known as a bar chart. We call this kind of chart a “bar” because it shows comparisons over time by showing bars that represent each year on a graph with vertical axes representing years going back as far as you want them to go – usually 20 or 30 years at least – but sometimes longer. The X-axis represents categories such as countries or products sold; Y represents quantities sold between those categories over time periods represented by different colors along each bar's length; Z represents dollar amounts spent per year for each category (you could also use color here).
Data visualizations were first created to help people understand large amounts of information that were previously too hard to grasp at a glance. As our society has grown more connected by technology, we’ve had access to more sources for collecting statistics about everything from the weather forecast to consumer spending habits. This increase in accessibility has driven an evolution toward more sophisticated visualizations that leverage computer graphics instead of just simple charts and graphs like pie charts or bar charts.
Computers in Data Visualization
With the introduction of digital computers, the first big shift in data visualization occurred. People had been visualizing data for centuries before this, but their capacity to quickly and efficiently depict enormous, complicated data sets is relatively recent.
True high-resolution graphics were created, but it would be some time before they were widely used. Computer science research would continue to collaborate with advances in data analysis, display, and input technologies (pen plotters, graphic terminals, digitizer tablets, the mouse).
Microsoft Excel developed less than 40 years ago, became a revolutionary tool for data visualization. Excel allows users to interact with data in unaggregated rows and columns. And then with the press of a button, generate a visual representation of all this information!
Since then, newer tools have been developed that can handle bigger data sets and provide a greater range of array chart types.
The Internet Age of Data Visualization
The internet age of data visualization has seen tools evolve from fixed counts to interactive presentations made of numerous data sources. They have improved in capability and performance while also becoming more user-friendly. There is no doubt that the Internet has made them a lot easier to use and more appealing but it has also created some problems that need addressing.
There are still gaps in people’s ability to use them due to a lack of data literacy. Many people lack the experience needed for identifying what questions you should ask about your data before diving in headfirst. Data visualization tools do not provide context needed for understanding the underlying data so knowing how it was collected will help you understand what questions you should be asking yourself before trying out various charts or graphs on your own.
Data visualization tools are now evolving again as they move away from being “one size fits all” solutions toward being more use case-focused (i.e., BI Reporting, Exploratory Data Analysis (EDA), etc.). The future holds great promise as these new types of platforms allow users to build their own custom dashboards tailored specifically towards their needs which would ultimately allow them better insights into whatever problem they might be facing at work!
The Future
Data has traditionally been a specialized field, where professionals like cartographers, statisticians and scientists have been responsible for visualizing their data. These practitioners knew their data inside out and were able to tell stories with charts and diagrams that are easy to understand.
Over the last decade or so there has been an explosion of interest in data visualization due to its ability to tell complex stories quickly and clearly. Businesses want the same ability as these chart-savvy professionals, but without the technical expertise required for creating them on their own.
Looking ahead into the future of data storytelling, we see more non-technical users becoming “data storytellers” who can use these tools effectively because they allow anyone with basic computer skills (and even no computer skills) to create compelling visualizations from their business systems using drag-and-drop interfaces that require no coding experience whatsoever! Spoggle runs on those same values where non-technical users can gain insights and create visually pleasing charts from their data, quickly and easily.
Conclusion
In conclusion, data visualization has been around for centuries. It's a simple concept -- visualizing data to make it easier to understand at a glance – but its applications continue to grow and change as technology advances. We'll never stop finding new ways of representing information, but the goal is always the same: to explain complex ideas with simplicity and beauty.
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