Education Knowledge

What Is Data Visualization?

In today’s world, the success of any business largely depends on data and the ability to work with it. Therefore, digital tools that allow company executives and their managers not only to obtain the data they need quickly but also to interpret it visually are becoming increasingly important. Data visualization is now an important tool that helps companies analyze, present and share information. When choosing a visual data development company, you should consider its expertise in a particular niche. For example, the company is a professional in data visualization in the pharmaceutical industry.

What is data visualization, and why do you need it?

Data visualization is the presentation of information using visual elements. Visualization allows you to graphically display text or numeric data of any size, making it easier to interpret and process.

Today’s digital tools are capable of processing large amounts of data from a variety of sources, including:

  • internal and external web sites;
  • smart devices;
  • internal data collection systems;
  • social media.

Raw data can be challenging to interpret and use, unlike data presented in visual form. However, data visualization allows decision-makers to see relationships between data and notice certain patterns or trends. At the same time, the perception of such data does not depend on the technical background of the audience. Thus, the graphical representation of information promotes informed decision-making based on data and strategic planning.

Advantages and disadvantages of data visualization

The main advantage of this way of presenting information is the ease of its perception and interpretation. One glance at a graph or chart is enough to identify trends or patterns, which is not the case with massive spreadsheets. In this case, it does not matter how well one understands the numbers and the issue being studied. Other advantages include compactness and interactivity.

The disadvantages of the graphical presentation of information can be noted only by the unfortunate choice of visual elements to display data. For example, a point chart will not best show the data of a multi-page numeric table.

Components of data visualization

Data visualization has 3 main components:

  1. History, or the purpose of data visualization. Any analysis is done for a specific purpose (performance study, sales forecast, etc.). Before getting started, the data scientist must agree with the business user on the story they want to learn from the analysis and visualization.
  2. Data Visualization has a set of data that the analyst obtains from sources available to them and structures it in the form of tables.
  3. Visual elements. The choice of visualization method depends on the amount and type of data and the task set for the analyst.

Types of visual elements

There are many methods of data visualization which allow you to display the obtained information in the best way. Some of the leading visualization methods include:

A line chart, line chart, or timeline

They are used to visualize temporal data. For example, a line chart can be used to show changes that occurred continuously over some time. Multiple lines on a line chart will show fluctuations in different factors over the same period.

Tree charts (“data trees”).

Used to display hierarchically structured data that relates to a group or set of elements associated with a parent element.

Network data visualization

Network data visualization represents complex relationships between different types of interrelated data. Such elements include:

  • scatter plots – present data as points on a graph;
  • pie charts – add a third data factor to a scatter plot;
  • word clouds – display the frequency of word usage using words of different sizes.

Visualizing multidimensional data sets

Multidimensional data set visualization presents two or more data variables as a single two- or three-dimensional image. They can be represented using:

  • histograms – compare two or more data factors and show changes in one variable over a period of time;
  • pie and composite bar charts.

Geospatial data visualization techniques

Geospatial data visualization presents data in relation to a real-world location. This can be done using:

  • heat maps;
  • density maps;

Interactive visualization techniques

With interactive visualization, users can interact with graphs and charts. Viewers can change variables in visualization parameters to find new insights or access detailed information. Data visualization software typically includes a dashboard for user interaction with the system.

Choosing a digital tool for data visualization

Developers today offer many tools for analyzing and graphically presenting information, including both paid and free ones. What to look for when choosing a visualization tool?

Integration capabilities

The chosen software should integrate with your existing IT infrastructure, databases, and third-party data sources. This is necessary so that you can access the data from the analysis and can update it if necessary.

Interactive reports

Interactive reports know how to handle variable data independently – filter, sort, or move them around in an interactive chart as data values are plotted. So you don’t need the help of a technical team every time you need to make a change to an array of data.


Data visualization tools should not create additional vulnerabilities in your business intelligence system. Instead, consider strong security features that limit data access to unauthorized users.

The ability to scale and automate data processing

The best choice is visualization tools that can handle large data sets with machine learning and artificial intelligence capabilities.

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