Difference Between Data Visualization and Data Analytics

Last Updated : 30 Sep, 2022

Data Visualization: Data visualization is the graphical representation of information and data in a pictorial or graphical format(Example: charts, graphs, and maps). Data visualization tools provide an accessible way to see and understand trends, patterns in data and outliers. Data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions. The concept of using pictures is to understand data has been used since centuries. General types of data visualizations are Charts, Tables, Graphs, Maps, Dashboards.

Data Analytics: Data analytics is the process of analyzing data sets in order to make the decision about the information they have, increasingly with specialized software and system. Data analytics technologies are used in commercial industries that allow organizations to make business decisions. Data can help businesses better understand their customers, improve their advertising campaigns, personalize their content, and improve their bottom lines. The techniques and processes of data analytics have been automated into mechanical processes and algorithms that work over raw data for human consumption. Data analytics help a business optimize its performance.

Below is a table of differences between Data Visualization and Data Analytics:

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Based on	Data Visualization	Data Analytics
Definition	Data visualization is the graphical representation of information and data in a pictorial or graphical format.	Data analytics is the process of analyzing data sets in order to make decision about the information they have, increasingly with specialized software and system.
Benefits	Identify areas that need attention or improvement Clarity which factors influence customer behavior Helps understand which products to places where Predict sales volumes	Identify the underlying models and patterns Acts as an input source for the Data Visualization, Helps in improving the business by predicting the needs Conclusion
Used for	The goal of the data visualization is to communicate information clearly and efficiently to users by presenting them visually.	Every business collects data; data analytics will help the business to make more-informed business decisions by analyzing the data.
Relation	Data visualization helps to get better perception.	Together Data visualization and analytics will draw the conclusions about the datasets. In few scenarios, it might act as a source for visualization.
Industries	Data Visualization technologies and techniques are widely used in Finance, Banking, Healthcare, Retailing etc	Data Analytics technologies and techniques are widely used in Commercial, Finance, Healthcare, Crime detection, Travel agencies etc
Tools	Plotly, DataHero, Tableau, Dygraphs, QlikView, ZingCHhart etc.	Trifecta, Excel /Spreadsheet, Hive, Polybase, Presto, Trifecta, Excel /Spreadsheet, Clear Analytics, SAP Business Intelligence, etc.
Platforms	Big data processing, Service management dashboards, Analysis and design.	Big data processing, Data mining, Analysis and design
Techniques	Data visualization can be static or interactive.	Data Analytics can be Prescriptive analytics, Predictive analytics.
Performed by	Data Engineers	Data Analysts

Difference Between Data Science and Data Visualization

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Difference Between Data Visualization and Data Analytics

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