As the era of big data kicks into high gear, businesses and organizations are now more focused on how they can leverage the data to gain a competitive advantage. This has, in turn, powered the popularity of data science tools – specifically the concepts of analytics and visualization. Despite hearing these words on every street corner in major cities, some business owners are still in the dark about what they actually are and how they can use them to grow their entities. If you’re among them, don’t worry, this definitive guide outlining the differences between data analytics and data visualization will help shed light on how to use them to improve your company.

First Things First, What is Data Analytics and Data Visualization?

Data analytics refers to the processes that data analysts and scientists use to explore datasets to draw meaningful insights and help shape future outcomes. There are a number of different types of analysis, but four types everyone should be familiar with are:

  • Descriptive
  • Predictive
  • Inferential
  • Causal

Data visualization, on the other hand, is the art of presenting datasets in pictorial or visual forms such as charts, maps, or graphs.

Differences between Data Analytics and Data Visualization

Even though both concepts work towards making data more meaningful to the human mind, they have significant differences as shown below

 1. Data Visualization Deals with Graphics

Based on the definition of visualization and analytics above, the considerable difference between the two is that analytics expresses patterns and trends from unstructured and structured data in a text-based form. On the other hand, visualization conveys these patterns in visual formats. These visual forms range from simple graphs, tables, and charts to advanced 3D dashboards. Check out our guide to choosing the right graph for your data here.

 2. Visualization Makes it Easy to Understand Data

Humans are visual beings. Thus, they’re more likely to understand and remember the information they saw in an image than what they read or heard somewhere. Therefore, while both concepts work towards making data more meaningful, visualization makes it easier for the average brain to digest large data sets comprehensively. This goes a long way in enabling a business owner to confidently make data-driven decisions that help them grow their business.

 3. Visualization Works With Raw Data

To draw meaningful conclusions from unstructured data, a data analyst employs complex data mining algorithms to cleanse and evaluate the data. In visualization, however, an expert uses the outcomes derived from analysis in combination with computational and theory-based tools to create visuals that enable the end-user to understand data.

 4. Visualization Adds Value to Data Analytics

Data analytics deals with data at a much deeper level. As noted, it employs complex algorithms to create data models that accurately predict how different variables will impact future outcomes. In other words, it gives a complete picture of how past business trends and patterns impact its future success in text-based forms. To the average mind, understanding this information in text format can prove to be a little confusing. Queue ‘modern day hieroglyphics’ here.

Data visualization makes understanding complex models easier as it displays how trends and patterns emerge and change over time through graphs, maps, and other 3D visualizations. As a result, it becomes easier for business users to find the connection between data points that would have remained unnoticeable when represented in a text-based analytical format.  This, in turn, enhances data-driven decision making and boosting business productivity. 

 5. Visualization is communicative  

As seen on all the points above, the goal of visualization is to make data easy to comprehend and action on. Analytics, on the other hand, goes much deeper to identify root causes, trends and potential future outcomes for better decision making.

Thus, data visualization is more communicative, while analytics leans towards data-driven decision making. For instance,  a visualization tool only shows you that sales are declining.  An analytical tool goes deeper by explaining when they began declining, how far have they’ve reduced and why they’re dropping.

 6. Analytics Comes Before Visualization

While visualization is equally essential, data analytics come before the latter. For a business to gain critical insights from its data, it must break it down first using analytical tools. This means that visualization tools come in later stages, once the insights have been derived from unstructured data sets. If visualization is the sail of a ship, analysis is the rudder and keel that ensures it stays upright and guided in the right direction.

Bottom Line

In a nutshell, data analytics and data visualization are two sides of the same coin, both geared to helping business users make better decisions. Merging the two concepts in your business will help you make decisions that positively impact your operations, thereby boosting your success. Get in touch with us today to learn how you can drive innovation into your business using the two innovative data tools.



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