What is data visualization and how is it important?

  • data visualization

Data visualization refers to the representation of information and data in a visual format. This is achieved by using tools like pie charts, bar graphs, histograms, etc. It helps in displaying the data more accurately and conveying the message more efficiently. Presenting data through visualization makes it easier for the audience to assert patterns and assess the data.

Data visualization helps in absorbing big data groups in less time. It makes the information more appealing to the audience and helps to retain their full attention during the presentation. Pie charts and bar graphs have been used to convey data by almost every industry for a long time. The recent years have witnessed some changes in the trends of data visualization.

Types of data visualization:

Data visualization can be categorized into five main categories:

  • Temporal – It includes all the data visualizations which are linear and one-dimensional. Some examples are as follows, line graphs, scatter plots, and timelines.
  • Network – These are the visualizations that show their relation with data graphs in the same network. Examples are matrix charts and alluvial diagrams.
  • Hierarchical – This includes the visualizations that represent data sets within data sets. In simpler words, they convey groups of data in a visual format usually with a single point of origin. Some examples are tree diagrams and ring charts.
  • Geospatial – This refers to the visualization of data over a physical location in the form of data graphs over maps. They can be used to show the distribution of a material over a certain location or to display a specific target set by a company to ensure its productivity. Some examples are as follows, flow maps, cartograms, and heat maps.
  • Multidimensional – It includes all the data visualizations which are multi-dimensional. This comprises all the data with two or more variables present. Some examples of multidimensional data visualizations are pie charts and histograms.     

Uses of data visualization:

Data visualization techniques are so effective that they are used in almost every industry. The variety of options available to represent your data makes it a viable option. Some spheres where data visualization is used are as follows:

  • Marketing and research: The use of data visualization is most commonly seen in the sales and marketing fields. Companies use pie charts and flow maps to represent the market acquired by them in a certain geographic location or to show the clients’ location over the map.
  • Political campaigns: Politicians use the geospatial form of data visualization to represent their vote bank in a certain area. This helps them to plan out the campaigning in an organized manner.
  • Educational institutions: Institutions like schools and colleges use data visualization tools to keep the data of different students class-wise. This enables them to maintain a record of the academics, attendance, and extra credits of the students.
  • Healthcare and well being: Hospitals and government agencies employ data visualization techniques to maintain a record of the patient’s activity and distribution of a disease over a certain locality.
  • Financial analysis: Data visualization is used by market analysts to determine the trends and patterns in the stock market. Visual representation of the data like closing price and opening price helps in a  better understanding of the market situation.

Importance of data visualization:

Data visualization is the representation of tabular data in a visual format. It is important as it helps in conveying the data more efficiently and in a lesser time. Data Visualization is used to determine the following:

  1. Change in data over time – Data visualization helps to adequately represent the changes or trends in data over a particular period. The visual display of data makes it easier to determine a pattern and make out the changes and reasons behind it.
  2. Frequency – Data visualization provides the best aid to keep a track of the frequency of data, like measuring the number of products a company supplied within a period. 
  3. Market research – The visual representation of financial data like the price of stocks, ensure accurate assessment of the trends prevailing in the market. It is less prone to mistakes and provides a better result in relatively less time.
  4. Correlations – Data visualization determines the relation between two or more independent variables. Doing the same with just tabular data will be time-taking and less precise.
  5. Determining the reward per risk – Measuring the value of risk taken for a certain reward is difficult without visual representation as it involves large spreadsheets and tons of data. Placing the same data in a visual format ensures that the information is conveyed at a more efficient rate. 
  6. Accurate analysis – Data visualization ensures the accurate reading of the information and data conveyed through it. Tabular data can be misleading sometimes as it does not have much impact on the brain. On the other hand, visual data is easier to grasp and retain, which helps in deriving the analysis accurately.
  7. Business Insights – Visually represented data enables you to gain more information from a network in less time. It’s useful while managing the insights of your business over a particular period.
  8. Scrutinizing errors – Finding and rectifying errors within a data set is much easier if the data is represented visually. Some mistakes can get overlooked in a tabular or spreadsheet form owing to the exhaustive amount of data present. This is where data visualization proves significant. 


Data visualization should be adopted as a prominent way to convey information. Visual formats like bar graphs and pie charts help to assess the patterns and changes in data with just a look at the graphs. The readings are far more accurate and obtained in relatively less time. Data visualization is the most crucial step of data analysis and is used by several industries to represent different types of data and information.