The soaring adoption of Cloud-based Business Intelligence (BI) solutions that have doubled in 2018 from its 2016 levels is an indication of the growing importance of business data for business enterprises across geographies and industry domains. With data being the center of gaining competitive market advantage, enterprises are looking for innovative software tools to analyze and gain valuable insights into their collected data.

Despite the growing awareness, many business decision-makers fail to understand the difference between data analytics and data visualization as the means to make sense of their generated data. The rapid growth of data volumes in today’s connected world can lead to information overload, which requires the deployment of both data analytics and visualization tools in order to delve deeper into the generated data and generate maximum business value.

Today’s BI solutions must include tools to perform both data analytics and visualization to provide a complete end-to-end solution for the benefit of customers. While there are significant overlaps between these 2 tools, you, as the BI user, must also be aware of their major differences, which are discussed in the following sections.

All about data visualization

In simple terms, data visualization is the process of representing business data visually to enable BI users to understand data patterns and derive business insights.

Visual tools used in data visualization can include charts, graphs, pivot tables, or even interactive dashboards. Visualization tools can also be used to generate customized reports using the dashboard reporting functionality.

All about data analytics

While data visualization can be used as a front-end tool to impart a visual context to your data, it lacks the necessary backend tools and algorithms to assimilate and sync unstructured data from decentralized sources of corporate data. With the growing number of collaborators accessing the data, decentralized data sources are not reliable to present synchronized information for users to derive meaningful insights for their business.

This is where Data analytics plays a critical role in deriving insights from thousands of data points. Data integration and analytics tools are capable of extracting and cleansing data from multiple and diverse sources and consolidating them at a central location, thus preventing any distortion of the information presented through visualization tools. Thus, data analytics acts as input for effective data visualization.

Difference between Data analytics and Data visualization

The following table summarizes the major differences between data analytics and visualization.

From the above arguments, it is clear that modern business enterprise requires both data analytics and visualization tools for deriving maximum benefits from their business data.

An end-to-end BI solution must comprise of both the front-end tools that can create and present useful data through visualization and dashboard reporting tools and the robust back-end tools and algorithms that can collect, prepare, and analyze the large volume of data (including the unstructured and messy data) from different sources.


Along with data analytics and visualization, business intelligence tools are now equipped with the latest emerging technologies including natural language processing (or NLP) and analytics chatbots that can unravel complex data for even the most novice of business users. Selecting the right BI solution which offers a complete and comprehensive solution with front-end and back-end features is critical for decision-makers to get the most of their investment.

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