What is Data Visualization and Why is it so Important?

What is Data Visualization and Why is it so Important? | Data Science and Analytics | Emeritus

Data visualization is the cornerstone of the modern data-driven culture. When analytics is visually presented, it helps decision-makers identify patterns. Further advancements in data visualization technologies such as increased visual interactivity will fundamentally change the way we look at processed data. As companies seek an innovative edge over their competitors, understanding what is data visualization becomes a key business imperative. So, let’s find out the core processes involved in using this effectively.

The graphical representation of analyzed data in the form of multiple visual elements such as charts, graphs, and infographics, is known as data visualization. Data visualization is concerned with finding out the most relevant insights from an unstructured dataset to spot hidden patterns, trends, and outcomes. It is also a means to communicate with non-technical audiences about the relationships among unique datasets to put data-driven decisions into action.

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Examples of Data Visualization

Here are some relevant real-life applications of data visualization across different walks of life:

  • Uber and transportation requests
  • Navigation systems and digital maps
  • Surveillance and protection of national property
  • Marketing strategies and user personalization tactics
  • Weather forecasting
  • Healthcare and fitness trackers
  • Political support and hosting fair elections
  • Financial success and investment health

ALSO READ: What is Data Collection? Why is it Important for Your Business?

Why is Data Visualization Important?

A McKinsey report claims that by 2025 the world will produce 463 exabytes of data—one exabyte equals one trillion gigabytes. While the figures keep getting gigantic, data analytics deals with a more efficient logical dissection of the available raw information, and visualizing it becomes a key aspect of the entire data life cycle. Data visualization equips us to make smart decisions by making the analyzed information accessible to not just technical experts of data but also a diverse range of non-technical professionals who will finally act upon the calculated insights. 

Types of Data Visualization

Here is a list of the major types of data visualizations that scientists usually use: 

  1. Pie charts show how a specific variable is divided into its different categories and visually establish the relationship between the categories and the larger variable. 
  2. Tables use rows and columns to organize data into structured sets. 
  3. Line charts track the evolution of more than one numeric variable over time. Straight lines connect the different data points and graphically represent the change of an entity over time. 
  4. Heat maps represent numerical data using colors, giving you access to the raw data points, making it a valuable tool for marketers to study website traffic, post engagements, and the efficiency of social media strategies.
  5. Histograms analyze data range, distribution, and description of data over a continuous interval and graphically represent all the different aspects of this distribution.  
  6. Scatter plots are a critical tool for regression analysis. They are primarily used to determine the relationship between two variables—one on the x-axis, and the other on the y-axis—of the Cartesian graph.
  7. Treemaps provide visualization for hierarchical data that cannot be usually analyzed by other kinds of tools listed here. Treemaps usually comprise nested rectangles of different sizes depending on the highest values of the data sets.  

Data Scientist InterviewWhat are the Pros and Cons of Data Visualization?

Understanding what is data visualization is incomplete without understanding its pros and cons.  


  • Increased consumer support and engagement
  • Increased employee engagement
  • Intelligent strategy placement
  • Visualizing patterns and trends of predictive analytics to determine future prospects
  • Strong idea illustration and observing trends over time
  • Idea generation and design thinking


  • Uncleared data bias leads to wrong conclusions
  • Misinterpreting data and using the wrong visualization tool decreases the effectiveness of the analyses
  • Correlation between variables is often wrongly interpreted as causation 

When Should You Visualize Your Data?

Knowing when to visualize your data informs the overall success of your endeavors. It also informs you about data visualization’s importance in relation to the entire data life cycle. So, after executing the processes of data generation, collection, processing, storage, management, and analysis, you reach the juncture of visualizing the analyzed information to finalize the interpretations.   

Visualization Tools and Software

Some of the best data visualization tools include: 

  • Tableau 
  • JupyteR 
  • Google Charts 
  • RawGraphs 
  • IBM Watson 
  • R
  • Python
  • FusionCharts
  • Data Wrapper
  • Infogram
  • Grafana

ALSO READ: All About Tableau: A Guide to the Powerful Data Visualization Tool

Why Use Data Visualization?

What is data visualization’s purpose? There are four primary areas where it is useful:

  1. Idea generation: Visualization of ideas allows teams and organizations to stay on the same page and address individual concerns about getting the vision right.
  2. Idea illustration: Visualization helps in illustrating ideas to explain them better. For instance, it helps in optimizing organizational workflows by graphically representing them and facilitating communication between teams. 
  3. Visual discovery: Data teams form a coherent narrative on the existing analysis and fill the gaps in interpretation through visual discovery.
  4. Data visualization: It is a key step in the data development lifecycle to help finalize conclusions and relay them to key decision-makers and execution teams.

Can You Learn About Data Visualization Online?

A GlobeNewswire report states that the data visualization software market will cross the $5 billion mark by 2025. Moreover, a vast pool of resources in the form of videos, podcasts, books, and academic papers already exist surrounding data visualization. So, it is absolutely possible to learn data visualization online.

The U.S. Bureau of Labor Statistics projects a 36% increase in demand for data scientists between 2021 and 2031. Apart from a technological boom, it is also a major breakthrough for data visualizers whose demand will only increase in the coming years. Learn all about the latest industry trends in the field of data sciences through the comprehensive online courses on Emeritus, in association with the best global universities. 

By Bishwadeep Mitra

Write to us at content@emeritus.org

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