Here’s Why Data Visualization is Important in Academic Research!

Educational institutes evaluate the academic growth of students by assigning them with such scholarly work and research papers that broaden their understanding. There are so many rules that must be followed while writing an academic research paper. Each academic paper is divided into multiple sections varying from introduction to literature review to methodology.  Similarly; data analysis is the most crucial part of research paper writing in which the gathered data is analyzed through logical reasoning.

In data analysis; data is presented either in the form of text, in a list, in a table, or in figures. This data helps the writer, as well as, the reader to understand the topic with proof or data visualization examples. Data visualization is the techniques used in most research paper writing to make it easier for the reader to understand the whole concept of the research work. This post is going to be all about data visualization, its importance, its types, and so on.

What is data visualization?

In simplest words; data visualization is the conversion of information into a visual form like a map, a graph, or a table. It is the kind of data visualization in research that is done by offering raw numbers and figures. After the data has been collected, processed, and modeled, it should be visualized for the conclusion to be evaluated. Data visualization is also known as information graphics, information visualization, and statistical graphics.

Techniques used for data visualization:

Data visualization techniques are the ways that are used to represent data visually in any form varying from infographics to time series charts. Following are some of the data visualization examples or techniques that students can use in their research projects;

Pie chart:

It is one of the most basic and common data visualization techniques in which proportions are illustrated as part-to-whole comparisons. They are simple and easy to read and best for key elements.


Histograms showcase the distribution of data, at continuous intervals or defined periods. They help in the identification of the concentration of the values plus it identifies the gaps in the values as well.

Bar chart:

A bar chart is a classic form of data visualization in research in which one axis of the chart shows the categories being compared and the other axis shows a measured value. The length of the bar shows how each group measures according to the given value.

Waterfall chart:

It is the type of data visualization used in Qualitative research writing in which it is illustrated how value changes as it is influenced by different factors like time, and so on. Its focus is to describe how a specific value grows or declines over a specific period of time.

Area chart:

This is yet another technique used for the purpose of data visualization by the students in their research projects.  It is the kind of chart in which the area underneath the line is shaded to represent the total value of each data point. This method is useful in showing changes in one or more quantities over time.


The usage of this technique is the most effective way of visualizing data through a sequence of events in chronological order. It allows to shed light on the most important events that occurred or that will occur in the future and makes it easier for the reader to identify the patterns within the given time span.

Besides these; there are many other similar data visualization examples or techniques that are used to illustrate data including; Gantt charts, Heat maps, Box and Whisker plots, Scatter plots, Highlight Tables, Bullet graphs, and so on.

Stages of data visualization:

The following are the four main stages of data visualization; which will help them in carrying out the process of data visualization:


It is the beginning stage of data visualization in which data is investigated so as to discover patterns, see the anomalies, test hypotheses, and spot assumptions with the assistance of graphical representations and summary statistics.


As the name implies; this is the stage of data visualization in which the explored data is interpreted, feature selection is done, and the data is transformed which ultimately helps in the process of data visualization.


This is the stage where interpreted and collected data is prepared and gathered to finally present it in the form of data visualization.


After the data is approved through the previous stages then a suitable technique of data visualization is used to present the accumulated data in the form of charts, graphs, or tables.

What is the importance of data visualization in academic research and other areas of life?

The importance of data visualization can be determined through the fact that it is applicable to every career. Following are some of the points that shed light on the importance & purpose of data visualization:

  • Data visualization makes it easier to identify patterns, trends, and outliers in large data sets.
  • Teachers can use data visualization to display the results of the students.
  • Computer scientists can explore advancements in artificial intelligence by sharing information through data visualization.
  • Businesses and companies get an overview of their yearly business report quickly.
  • It is through data visualization that the information can be understood quickly and it improves the insight of the reader in the particular matter.
  • Qualitative data visualization examples maintain the interest of the readers because they can easily be understood.
  • It easily distributes the information that enhances the opportunity o share insight with everyone involved in the project.

The effective way of data visualization:

Students buy Dissertation help UK-based services because they know that the professional writers won’t only write the research project proficiently but their data visualization techniques will also be at par excellence. The following steps will help in carrying out the process of data visualization effectively;

Generate a research question:

The first thing is to understand what the goal of your research work is. If you have a clear understating of the core concept of your research project then the remaining steps will be easier to carry out. This is why students must generate and understand the research question properly before deciding on it. This will clarify the kind of data that will be needed and the process of exploring the data.

Gather the data:

You can gather data for your research work by the following steps;

  • Collection of numerical, statical, and geospatial data from online platforms or libraries.
  • Technical assistance with the design and creation of the surveys using Qualtrics web survey or other such platforms.
  • Usage of methods such as digitizing imagery/documents, scraping data from web sources, and collecting it from APIs.

Clean the data:

The cleanliness of data means getting rid of any anomalies or gaps found in the collected data so as to prepare the chart without any mistakes. It allows the student to create effective data visualization rather than wasting time on fixing issues while visualizing the data. The following steps are included in the cleanliness of the data;

  • Addressing invalid data.
  • Eliminating unnecessary variables.
  • Getting rid of duplicate rows.
  • Fixing typographical errors.
  • Categorizing values.

Pick a technique:

While choosing a data visualization technique or chart type; it is very important to consider the sort of data that you have and how you want to project it.  While choosing a technique; a student must stick to the following steps;

  • Comparison between variables.
  • Relationship between variables.
  • The patterns required for data illustration.

Select a tool:

Data visualization tools prove to be really effective while creating a data chart. These tools are the kind of software that helps in the visual and pictorial formatting of the data. There are multiple kinds of data tools and students can choose the one which seems to be the most suitable for them.

It is these tools that support a variety of visual styles varying from simple ones to complex ones. Some of the best types of data visualization tools that can help you in creating effective charts are as follows;

  • Google charts.
  • Tableau.
  • Grafana.
  • Chartist.
  • Fusion charts.
  • Datawrapper.
  • Infogram.
  • ChartBlocks, etc

Prepare the data:

Now; the data will be prepared according to the chosen technique or type of chart for the purpose of data visualization. This step of data visualization includes the following stages;

  • Formation of the columns.
  • Conversion of the values in the units.
  • Filtration of the data to focus on only the data that will be illustrated in the chart.
  • Creating aggregate values for groups.
  • Extraction of the values from complex columns.
  • Combination of the variables to create new columns.

Make a chart:

Now; comes the final step of data visualization which is the making of the chart. The below-mentioned steps will help you in the process;

  • Import the data into the chart you have selected with the help of the chosen software.
  • Select the chart type of your preference.
  • Evaluate the effectiveness of the chart type.
  • Refine it by applying different designs and principles.

Besides the above-mentioned steps; students must keep in mind while preparing data visualization to make it more effective;

  • Accurate: It must accurately represent the data and its trends.
  • Clear: It must be easily understood.
  • Empowering: It must be empowering enough so to convince the reader to know to take action accordingly.
  • Succinct: It must not take long to connect and resonate.

What is storytelling with data visualization?

Storytelling with data is the creation of a narrative using the data and the analytics that the student has collected to support the hypothesis of the story. When done well, data visualization can be an incredibly powerful tool for storytelling, allowing audiences to see the data in a new light and gain insights that they may have otherwise missed. The following points must be considered for data storytelling purposes;

  • What you want the readers to know.
  • What do you want viewers to do with your illustrated data?
  • What narrative will help to drive the action?
  • How your data will help in driving those decisions.

Final note

There are various ways through which students can project their accumulated data in the form of charts or different data visualization techniques. These data visualization techniques prove to be really helpful in different sectors including the area of academic research. Hopefully; the above-mentioned post will help the readers in understanding the basics of data visualization and its importance in the world of academic research.


Is data visualization in data analysis required?

Even though; data visualization is not a must requirement, it proves to be really helpful for students to illustrate what they have gathered and interpreted when it comes to data analysis.

Can I use data visualization tools in research writing?

Yes; data visualization tools can absolutely be used for research projects. It is the data visualization tools that render information in a visual format like a graph, a chart, a table, etc. It makes it easier for students to import a massive amount of data in a visual format.

Can I replicate visualization from online examples?

Many such online tools have been introduced that can help users in the process of data visualization. Students can make help of these tools, can take online examples as their source of reference, or can even take assistance from professionals in the field through online platforms.

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