Data Visualization Software

Introduction

The history of data visualization starts in the second century A.D when data was organized into simple columns and rows. Most of the data collected was from merchants and scholars who needed to keep track of their inventories. From here, it evolved into the first quantitative representation of data in 17th century France.

French philosopher and mathematician Rene Descartes paved the way for the important work by Scotsman William Playfair. Descartes created a coordinate system that was two-dimensional for displaying values that at the time showed great potential for graphical communication and quantitative data.

In Modern History

In the 20th century, Jacques Bertin continued the advances in data visualization by using quantitative graphs for the presentation of intuitive and accurate information. This came at the rapid movement of industrialization and globalization. The bounds of data visualization have been greatly expanded by the statistical approaches of John Tukey and Edward Tufte, who redefined data visualization techniques with their data analysis approach.

The Future of Data Visualization

The advances in technology have also played a major role in the history of data visualization, resulting in a trend that has shown the exponential progression of technology and data visualization. It has evolved into more technical applications such as interactive designs in software visualization, which can serve more purposes other than just business.

In the field of statistics and data analysis, programs like SAS, SOFA, R, Minitab, Cornerstone and many more allow for data visualization for statistical purposes. D3, Python, and JavaScript are all programming languages that have made the visualization of quantitative data more viable. Currently, there are free programs for those who wish to learn data visualization sofware programming such as The Data Incubator and General Assembly.

The current trends indicate that cognitive frameworks and nextgen technologies that are evolving will boost the importance of data visualization in organizations and society. The IoT is also expected to make an impact, with connected devices capturing more and more data.

Conclusion

It is evident that data will only continue to play a major role in all sectors of society - from personal use of data to enterprise data management. The need to understand it from a statistical and technical perspective as well as being able to articulate it to audiences will increase in demand. The long-spanning history of data visualization software has set the groundwork for a future that will require more skilled and prepared workers with a working knowledge of data visualization principles.