Applications of Visualization Techniques

Abstract

We are living in a data-driven society, and everything you see and do is data. Over 2.5 quintillion bytes of data are created every single day, and it is only going to grow from there. By 2020, it is estimated that 1.7 MB of data will be created every second for every person on earth. In such a scenario, it is almost impossible, labor-intensive, and time-consuming to mine data in traditional ways to bring out insights from it. Data holds the key to smart decision making, and almost every disruptive technology today highly depends on it, but if we do not devise an efficient way to dive into data and map learnings from it, then this potential resource is of no use. To all the doubts and required advancements, data visualization is the answer. Data visualization provides a way to represent quantitative data in a graphical manner. It holds the potential to transform any kind of data into visuals; something which is easier to perceive and process by the human mind. Through data visualization, we can map years of messy bulky data into expressive visualizations to discover new trends and unknown facts. The visualization methods vary from the trivial line charts to the bar, column, pie, heat maps and what not. Good data visualizations are created when intelligent communication, data science skills, and impressive design techniques collide. It offers key insights into complicated datasets in ways that are meaningful and intuitive. It is a highly versatile field of research and development and finds enormous applications in the world of business intelligence that drives industries, in education and learning space to better communicate ideas, in geospatial studies, social network analysis, prediction analysis, and an insanely huge number of other fields. New ways to incorporate data visualization into work evolve every day. The rapid development of data visualization tools and technologies has enabled harnessing of data and transforming it in a way that turns it into information. In this chapter, we answer the question of why we should use data visualization along with discussing associated technologies and impressive hands-on applications to support our reasons.