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Big data visualization: tools, uses and techniques for data-driven decisions

In our data-driven society, the complexity and volume of data sets keep growing. We are constantly overflowed with data in our daily routines, and it gets harder and harder to grasp the meaning of it. 

In the workplace, this is especially challenging. Data readability and analysis are critical for strategic decision-making. But, at the same time, large lists of figures and data pile up and get lost in the chaos of illegible documentation. This is when big data visualization comes to the rescue.

What is big data visualization?

Big data visualization is a group of visualization techniques that illustrates the relationships within large sets of data. It goes way beyond traditional graph resources such as pies or bars, rows and columns, colors, and symbols. Big Data Visualization software includes interactivity, personalization, and animation, among other real-time features. 

Uses of big data visualization

It supports enormous amounts of raw data

Data visualization tools provide fact analysis, identify correlations and spot unexpected patterns within huge sets of unstructured data. 

It processes a wide variety of data types

Numbers, trigonometric functions, geometric algorithms, you name it. Big data visualization can make almost any type of data easily understandable.  

It operates in real-time 

With data visualization tools, the displayed information is constantly updated. Any user can access the most current data available at any time. 

It tells a comprehensive story

As data is presented in a visually-centered manner, data visualization techniques line up causes, effects, and resolutions. They connect daily processes with performance outcomes, discovering hidden relationships within metrics. 

Now, it’s crystal clear why big data visualization is so important in today’s data landscape, where the internet and social media keep spreading at an extraordinary rate. As information can be gathered in no time and our brains have reduced their attention span to about 8 seconds, it’s vital to find a way to consume and comprehend it quickly and efficiently. 

big data visualization

Big data visualization takes data literacy to a whole new level. It brings complex data to life in real time and allows analysts to extract valuable insights from it. And it’s not only useful for specialists. The idea is to make data (and its relationships) appealing and understandable for all stakeholders. This way, organizations can streamline their decision process better and achieve success faster. 

Tips for big data visualization mastery

If you want to excel at big data visualization, here are some tips to level your game up. 

Identify the correct audience

With the aid of big data visualization, it’s possible to capture a lot of insights, but they are only impactful in context. Define the audience and requirements of the report properly to make the best out of it. 

Pick the right technique

As we are going to see further in this article, there are many different methods to visualize big data. It’s really important to outweigh the cons and pros of each option to find the one that works best for your needs. 

Use labels for further explanation

With labels, you can add a little bit of word explanation to visuals. Legends and titles help keep readers engaged and, most importantly, contribute to the report’s overall understanding. 

Top data visualization techniques

Luckily, there are a lot of different big data visualization methods to choose from, depending on the amount of data and type of data storytelling.


Charts are one of the most commonly used data visualization techniques. They match fixed and variable values to compare components. Depending on the visual and geometric elements used to represent the link between data points, you can use the following:

  • Line charts
  • Pie and donut charts
  • Bar charts 
  • Funnel charts


This type of diagram represents values of two or more numeric variables on an X and Y axis. They are great to correlate and compare data sets in 2D or 3D. Scatter plots use points, while bubble plots add a third variable represented by the size of the bubbles.  Histogram plots use bars to represent elements and frequencies over a specific period of time. 


Maps are visual representations that place data points on specific objects or areas. In general, they emphasize hierarchy and location. Heat maps use different intensities of color, while treemaps use color-coded rectangles to represent data weight.

Network diagrams

This big data visualization technique establishes relationships with nodes and ties to map and analyze social networks’ architectures. They help to visualize interactions between components.  

Let’s get it started: big data visualization tools

After exploring the basics of big data visualization, we leave you with a useful list of the most known big data visualization tools. 

  • Tableau
  • Google Chart
  • Microsoft Power BI
  • Sisense
  • Looker
  • Datawrapper
  • D3.js
  • Data box

And remember: data has no value in itself; it’s the decisions that drive value. Get help from these powerful tools to make data-driven meaningful decisions.

If you need any help with navigating the big data visualization universe, don’t hesitate to contact us and check out our big data analytics services.

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