12 Useful Ways to Visualize Your Data (with Examples)

In today’s data-driven world, Data visualization is a powerful tool for getting insights and trends that are effectively communicated by businesses, researchers, and individuals alike. The sheer volume of data generated every day makes it increasingly important for ways to be found to make sense of it all.

Hence, Data visualization is not just about creating pretty charts and graphs; it’s about telling a story with your data. When done correctly, it can help you to:

• Identify areas for improvement
• Optimize processes
• Make predictions
• Inform strategic decisions
• Communicate complex ideas simply

In this article, we’ll explore 12 useful ways to visualize your data, along with examples.

A table is a structured format for displaying data , it is a kind of flat structure and it can display two dimension of data at a time.
For example The below table (Figure 1) shows the country and its repective total profit and cost. This visual helps when the client wants to summarize the data.

A matrix is similar to the table visual but makes it easier to display data meaningfully across two or more dimensions. This can be useful for complex data sets that have multiple categories or groupings. Matrix visuals can be used to perform calculations on the data, such as calculating sums, averages, and percentages. This can be useful for summarizing data and identifying trends.

For example : The below matrix (Figure 2) shows the profit of each year and its respective country

What is the difference between table and matrix visual?

A table is a two dimensional visual to represent the irregular data whereas Matrix is multi dimension visual like an excel pivot table.

Stacked barchart is the best choice when you want to show the contribution of individual parts to the total part. It is used when you have limited number of category as too many category make it difficult to understand.The primarygoal of this chart is to identify each contribution to overall part.

For example : The below chart (Figure 3) contains 3 categories and it visualized according to their profit by year.It clearly shows the total profit value of each category.

Clustered barchart is great when you want to show the multiple categories and sub-categories side by side. The primary goal of this chart is to compare the size of each category.

For example : The Figure 4 illustrates the total profit of each category in their respective year. Here it compares the total profit value of each category.

Stacked column chart is similar to the stacked bar chart which shows composition and comparison of different categories and sub-categories but within vertical columns.

For example : The Figure 5 represents the total profit of each category as per their year in a vertical axis.

Columnar barchart is an ideal choice when comparing individual values across various categories. It is straightforward comparison of values which will be suitable for large number of categories as it avoid clusters unlike Stacked column chart.It is similar to the clustered bar chart.

For example : The Figure 6 illustrates the total profit of multiple categories side by side using vertical columns as per their year.

Card visuals helps to highlight critical data points such as key performance indicators (KPIs), totals, averages, or other single values that need to be prominently displayed. It typically display one key value or metric prominently. The data in card visuals can be dynamic, updating in real-time or according to the latest data available.

For example : The Figure 7 indicate the card visual that displays the total profit, which is £46.88 million.

Map visualization plays a role to display the geographically related data in the form of maps. It helps to analyze and to represent data expression with more clarity.

For example : The Figure 8 illustrates the total profit and average profit of their respective countries through visual repreentation of map.

KPI are specialized to track the amount of progress towards the measurable goal which can be critical business metrics and performance indicators.It is very effective for the organizations to make data driven decisions.

For example : The Figure 9 KPI visual indicates that current level of revenue (The bold green font) towards the goal.

Multirow card is a versatile tool to display multiple values which corresponds to different data points or scenarios.

For example : The Figure 10 indicates the multirow card which contains the value of Revenue , Order Quantity, Return Rate, Return Quantity , Products and customers.

Linechart help to present the sequential values to identify the trends. The horizontal axis (x-axis) typically represents time (days, months, years) in a continuous scale.If you need to highlight trends and changes over time, the Linechart is the best choice.

For example : The line graph(Figure 11) showing revenue by month of a company. The x-axis of the graph shows time, with labels for every six months starting from January 2015 and ending in July 2017. The y-axis shows revenue in millions of dollars (m Revenue).

A Treemap diplays a hierarchical view of your data in nested rectangles. The branches are in rectangles and its sub-branch is shown in a smaller rectangle. The size of each rectangle is proportional to a specific data value, and the color can be used to represent another dimension, such as category or magnitude.

For example : The given treemap (Figure 12) illustrates revenue sizes across various categories within the Education sector, with each rectangle’s size representing the revenue amount for its respective category.

There are 7 principles of Effective visualization design such as

• Identify the suitable visuals to your data.
• The design should be balanced (i.e., color, texture , shape and negative space).
• Highlight information according to your audience.
• Ensure that your visuals are simple and easy-to-understand.
• Add Interactivity to clarify doubts or queries.
• Establish pattern by using similar color and chart types.
• Compare the asects by aligning the data vertically or horizontally.

There are few aspects to be aware of and to be dealt with while in the process of data visualization, so that there would be effective communication and correct interpretation of the transmitted information.

1. Chart Junk

• Chart junk describes the unnecessary or distracting elements in a visualization that do not add value or convey meaningful information.
• Challenges include locating and cutting off chart junk, thus ensuring that visualizations are crystal-clear, easy to comprehend, and productive in terms of answering questions.
  

  Figure 15. Chart Junk

2. Compressing the Vertical Axis

• Vertical compression of a chart can cause data distortion and lead to misinterpretation of the data.
• It is necessary to be careful when picking the vertical axe scaling in order to correctly represent the data without any data compression which may exaggerate or hide the differences in it.

3. No zero point on the vertical axis

• There is no such a thing as zero point on a chart both on the horizontal and vertical axes of a graph that may lead to bigger gradients and thus, the result is the possibility of disillusionment of an observer.
• Actually, to make the graph more appropriate, the zero point must be positioned completely parallel to the z axis.

In conclusion, There are dozens of tools for visualizing the data. Not every tool is right for every person it maybe hard or easy according to their perspective. The good data visualization skills focus on best practices and the core concepts of visuals. So Explore your own personal style when it comes to visualizations and dashboards.

Do I need to Know programming to make visuals?

No, programming skills are not always necessary to create visuals. While proficiency in programming languages like Python or R can be beneficial for advanced customization and interactivity

What tools do typically use for creating visuals from rawdata?

The professionals often use a variety of tools depending on their needs, preferences, and skill level.
Some of the commonly used tools are Excel, SQL, Python, R, PowerBI and Tableau

Is datavisualization is a part of data analytics?

Yes, data visualization is a crucial part of data analytics. It helps in exploring, analyzing, and communicating insights from data effectively.