Big data visualization: what it is, techniques and best tools

According to IBM, every day, 2.5 quintillion bytes of data are created from social media, sensors, webpages, and all kinds of management systems are using it to control the business processes.

By helping correlations between thousands of variables available in the big data world, technologies could present massive amounts of data in an understanding way, which means Big Data visualization initiatives combine IT and management projects.

In this article, we will address data data visualization:  and how jamaica phone number data its visual representation should move together to ensure it is effectively employed.

You will see the following topics:

A defining characteristic of Big Data is volume.

Today’s companies collect and store data visualization: v vast amounts brevo constant communication alternatives brevo key of information that would take years for a china numbers human to read and understand.

Visualization resources rely on powerful tools to interpret raw data and process it to generate visual representations that allow humans to take in and understand enormous amounts of data in a few minutes.

Big Data visualization describes data of almost any type — numbers, trigonometric function, linear algebra, geometric, basic, or statistical algorithms — in a visual basis format — coding, reports analytics, graphical interaction — that makes it easy to understand and interpret.

Thus, it goes far beyond typical graphs, bubble plots, histograms, pie, and donut charts to more complex representations like heat maps and box and whisker plots, enabling decision-makers to explore data sets to identify correlations or unexpected patterns.

Why is it important to have a good method of visualization?

The amount of data is growing every year thanks to the Internet and innovations such as operational systems, sensors, and the Internet of Things.

The problem for companies is that data is only useful if valuable insights can be extracted from large amounts of raw data and read by who can analyze them — data literacy in near real-time.

Big Data visualization techniques are data visualization:  important because they:

• Enable decision-makers to understand what the amount of data means very quickly;
• Capture trends — the use of appropriate techniques can make it easy to recognize this information;
• Reveal patterns — identify correlations and unexpected connections that could not be found with specific questions; and
• Provide a highly effective way to communicate any insights that surfaces to others.

What are the types of Big Data visualization?

Big Data visualization provides a relevant suite of techniques for gaining a qualitative understanding.

We described the basic types below.

Charts

Charts use elements to match the values of variables and compare multiple components, showing the relationship between data points.

• Line chart — the comparable elements are lines that could help to analyze peak and fall moments at an axis variant, such as sales volume over a period.
• Pie and donut charts — they are used to compare parts of the whole, such as components of one category. The angle and the arc of each sector correspond to the illustrated value, and the distance from the center evaluates their importance.
• Bar chart — each value is displayed by a bar, either vertical or horizontal. It is not indicated when values are very close to each other.

Source

Plots

Plots help to visualize data sets in 2D or 3D. It can be:

• Scatter (X-Y) plot — shows the mutual variation of two data items (axis X and Y).
• Bubble plot — it has the same scatter plot concept, but the markers are bubbles. The main difference is the bubble size, the third measure that represents another variable.
• stogram plot — represents the element variable over a specific period.by observing the arrangement of the data points.

  How about an example to illustrate? The figure below displays a scatter plot showing the correlation between American population growth and the increase in housing prices between 2010 and 2017.

  Bubble charts

  This is another one of the types of data visualization that you can use to indicate correlations and variations. The difference to the previous format is that the bubble charts allow a slightly richer analysis, precisely because of the use of bubbles instead of points.

  Bubbles can vary in size to represent third data sets. The use of different colors is also encouraged to make the material richer and more intuitive.

  To create a clear and compelling bubble chart, you should be careful not to use too many bubbles and make it difficult to visualize. Also, make sure that the bubbles are scale according to the area, not their diameter.

  In this example, the graph uses bubbles of different sizes and colors to indicate the amount of time users spend on the internet, according to age and gender.