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Data Visualization Techniques – Definition, Factors, and Types

What is Data Visualization?

Data visualization is the graphic representation of information and data. Using visual elements such as charts, graphs, and maps, data visualization tools provide an accessible way to visualize and understand trends, outliers, and patterns in data. Additionally, it provides an excellent way for employees or business owners to present data to a non-technical audience without mistakes.

Data visualization and techniques are essential for analyzing vast amounts of information and making data-driven decisions.

Data Visualization Advantages

Our eyes are drawn to colors and designs, and we can quickly recognize squares of red and circles of blue. Our culture is visible and includes everything from art and advertising to television and film. Data visualization is another form of graphic art that grabs our attention and keeps us on the message. When we look at charts, we quickly see trends and outliers. If we can see something, we absorb it quickly. Tell stories with a purpose. If you’ve ever looked at a massive spreadsheet couldn’t see a trend, you know how effective visualization can be.

Some other Benefits of  Include:

  • Share information easily
  • Explore opportunities interactively
  • Visualize patterns and relationships

Data Visualization Disadvantages

While there are many advantages, some disadvantages may seem less obvious. For example, viewing visualizations with many different data points makes it easy to give the wrong impression. Or sometimes, the notion is poorly formulated, making it biased or confusing.

Some other Disadvantages Include:

  • Biased or false information.
  • Correlation does not always imply causation.
  • Key messages can get lost in translation.

Data Visualization Techniques

Data Visualization Techniques

The type of techniques you take advantage of will vary depending on the kind of data you’re working with and the story you’re telling with your data.

Here are Some Essential  Techniques you Should know:

  • Pie chart
  • Bar graph
  • graph
  • Gantt chart
  • Heat map
  • Square and Whisker Plot
  • Waterfall outline
  • Area chart
  • Scatter plot
  • Schematic diagram
  • Chronology
  • Table highlight
  • Bullet outline
  • Choropleth map
  • Word cloud
  • Network diagram
  • Correlation matrix

5 Factors Influencing Data Visualization Choices:


It is essential to optimize the data representation for the specific target audience. For example, mobile fitness app users who are on the go can quickly work out with hassle-free visualizations. On the other hand, if data insights are meant for experienced researchers and decision-makers who work with data regularly, simple schemes can often be circumvented.


Therefore, it will determine what type of data the strategy deals with. For example, if you are working with time series metrics, you will use line charts to show dynamics in many cases. To establish the relationship between two components, scatter plots are often used. Conversely, bar charts work well for comparative analysis.


Depending on the context, you can use different methods to visualize and read the data. To emphasize a specific number, for example, a significant profit increase, you can use shades of one color on the chart and highlight the highest value with a bright one. Conversely, to separate the elements, you can use contrasting colors.


There are many data types, each with a different exchange rate. For example, financial results may be measured monthly or annually, while time series and tracking data constantly change. Depending on the rate of change, you can consider dynamic rendering (evaporation) or static data visualization techniques in data mining.


The purpose of data visualization affects how it implement. To perform complex analysis, views are grouped in dynamic and controllable dashboards equipped with various tools for visual data analysis (comparison, formatting, filtering, etc.). However, dashboards are not necessary to show a single or tangential data perspective.

Different Types of Analysis for Data Visualization

There are Three Different Types of Research:

Univariate Analysis: data visualization In univariate analysis, you can use only one attribute to analyze all its characteristics.
Binary Analysis: Data Visualization In Binary Analysis, the data to be visualized is compared between exactly two features (BI).
Multivariate Analysis: data visualization In a multivariate analysis, data on more than two variables compare.


Therefore, data visualization define as a graphical representation that holds information and data.

Using visual elements such as charts, graphs, and maps, data visualization techniques provide an accessible way to visualize and understand trends, outliers, and patterns in data.

Nowadays, we have a vast amount of data, i.e. in the world of big data, tools and techniques are essential for analyzing the enormous amount of information and making decisions based on data.


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