Top Data Visualization Techniques: Types, Tips

This blog explores the top data visualization methods in 2023 and their benefits for businesses.

What is Data Visualization?
Types of Data Visualization Techniques
Tips for Creating Effective Data Visualizations
Advantages and Disadvantages of Data Visualization
Data Visualization Examples
Factors that Influence Data Visualization
Data Visualization and Big Data
Conclusion

To know how to visualize data in Tableau, watch Data Visualization using Tableau

What is Data Visualization?

Data visualization involves presenting data in a visual format, such as graphs, charts, and maps, to enhance understanding and analysis. Its primary goal is to make complex data sets more accessible and intuitive to understand. The use of data visualization has become increasingly important in recent years due to the explosion of data and the need for businesses to make better decisions based on data-driven insights. By employing data visualization techniques, organizations can uncover hidden patterns, trends, and insights within raw data that may not be readily observable.

Businesses may communicate more effectively and make better decisions using data visualization, which can offer a more thorough knowledge of data sets. Organizations may analyze performance against goals, identify areas for improvement, and highlight possible opportunities or areas of concern by presenting data in a simple format.

Types of Data Visualization Techniques

Businesses can use numerous data visualization techniques to represent their data. Some of the most common types include line charts, bar charts, scatter plots, heatmaps, and pie charts. Each type has its strengths and weaknesses, and choosing the right visualization technique depends on the type of data analyzed and the insights that need to be generated.

Line Charts and Area Charts

Line charts and area charts are data visualization tools commonly used to display trends and patterns in data over time or across different categories. While they share similarities, they have distinct characteristics that make them suitable for different purposes.

Line Charts:

Line charts are primarily used to represent the relationship between two continuous variables, usually with one variable plotted on the horizontal x-axis and the other on the vertical y-axis.
They effectively illustrate trends, patterns, and fluctuations over time or across a sequence of data points.
Line charts consist of individual data points connected by straight lines, which allows for a clear visualization of the progression of values.
Line charts are useful in displaying time series data, such as stock prices, temperature fluctuations, or population growth over time.
They may be used to compare numerous lines on the same graph, which makes it simple to spot trends across many datasets or factors.

Area Charts:

Similar to line charts, area charts are suitable for displaying trends and patterns over time or across categories.
In addition to showing the progression of values, area charts also represent the magnitude of the data between the line and the x-axis, filled with a color or pattern.
The area between the line and the x-axis is typically filled with a solid color or pattern, making it easy to visualize the cumulative total or the proportion of the whole.
Area charts are effective in illustrating the composition or distribution of data across different categories or variables, especially when the focus is on the magnitude or proportion of the values.
They can be useful in visualizing stacked data, such as market share by various product categories or the distribution of income by different income groups over time.

Overall, line charts are best suited for displaying trends and fluctuations over time, while area charts are more suitable for illustrating the cumulative or relative values between a line and the x-axis. The choice between these chart types depends on the specific data and the insights you want to convey to the audience.

Bar Charts and Column Charts

Rectangular bars or columns are used in bar charts and column charts, which are two commonly employed data visualization tools for displaying categorical data. Despite their similarities, they vary in orientation and how they depict the data.

Bar Charts:

Bar charts display data using rectangular bars that are horizontally oriented.
Each bar represents a specific category, and the length or height of the bar corresponds to the value or frequency of that category.
Bar charts are effective in comparing values or frequencies between different categories.
They are commonly used to illustrate categorical data, such as comparing sales figures of different products, population sizes of other cities, or survey responses by various age groups.
The horizontal orientation of the bars allows for easy reading of the labels on the y-axis, making them suitable when the category names are long or have complex labels.

Column Charts:

Column charts, also known as vertical bar charts, display data employing rectangular columns that are vertically oriented.
Similar to bar charts, each column represents a specific category, and the column’s height or length corresponds to that category’s value or frequency.
Column charts are also effective in comparing values or frequencies between different categories.
They are frequently employed in circumstances where the category labels are concise and amenable to convenient presentation along the x-axis.
Column charts are often used to visualize data such as monthly sales figures for different regions, comparisons of test scores for different subjects, or the market share of different companies.

Both bar charts and column charts are useful for displaying categorical data and comparing values or frequencies across different categories. The choice between them depends on the orientation that best suits the data and the readability of the category labels.

Scatter Plots and Bubble Charts

Scatter plots and bubble charts are data visualization tools to illustrate the correlation between two numerical variables. Although they share similarities, they differ in presenting the information and incorporating additional data.

Scatter Plots:

Scatter plots are graphical representations that display individual data points on a graph, with one variable represented on the horizontal x-axis and the other variable on the vertical y-axis.
Each data point represents a unique combination of values for the two variables being compared.
Scatter plots effectively visualize the correlation or relationship between the variables, as the pattern or trend of the data points can indicate whether there is a positive, negative, or no correlation.
In multiple fields of study, such as the social sciences, economics, and scientific research, these methods are prevalent for the examination and presentation of data.
Scatter plots are useful for recognizing outliers or clusters of data points and determining the strength and direction of the relationship between the variables.

Bubble Charts:

Bubble charts are similar to scatter plots, but they incorporate an additional dimension of information by representing a third variable through the size or color of the data points (bubbles).
The x-axis and y-axis still represent the two numerical variables, while the size or color of the bubbles indicates the magnitude or category of the third variable.
Bubble charts allow the visualization of three variables simultaneously, making them useful for displaying complex data patterns or trends.
They are commonly used when the magnitude or significance of the third variable needs to be emphasized, as the size or color variation helps distinguish different data points.
Bubble charts can be used in various domains, such as marketing research, environmental studies, and finance, to analyze and present data involving multiple dimensions.

Heatmaps and Tree Maps

Heatmaps and treemaps are data visualization techniques employed for representing and analyzing data. While their structures and purposes differ, both offer valuable insights into patterns and distributions within datasets.

Heatmaps:

Heatmaps visualize data using a color-coded matrix, where each cell represents a data point or a combination of variables.
The intensity of the color in each cell represents the magnitude, value, or frequency of the data point.
Heatmaps are particularly effective in displaying patterns and correlations within large datasets.
They are frequently employed for the depiction of quantitative data, encompassing financial data, population density, or fluctuations in temperature.
Heatmaps can reveal clusters, trends, or outliers in the data by providing a visual summary of values across different categories or times.
They are also used in genomics and bioinformatics to analyze gene expression data and identify patterns in DNA sequences.

Treemaps:

Treemaps visualize hierarchical data by dividing a rectangular space into nested rectangles, representing different categories or subcategories.
The dimensions of each rectangle exhibit a direct correlation with a distinct metric, such as revenue, population, or market share.
Treemaps allow for the visualization of the overall structure and the relative sizes of the categories within a hierarchy.
They are commonly used to represent categorical or hierarchical data, such as organizational structures, file sizes on a computer, or market segmentation.
Treemaps help understand the distribution of values across different levels of a hierarchy and identify the most significant or dominant categories.
They can be interactive, allowing users to explore the data by zooming in or expanding specific categories to reveal more detailed information.

Heatmaps are suitable for revealing patterns and correlations within datasets, while treemaps are effective in displaying hierarchical structures and the relative sizes of categories. The choice between them depends on the nature of the data and the specific insights you want to convey to the audience.

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Pie Charts and Donut Charts

Circular data visualizations, such as pie charts and donut charts, depict a whole’s distribution or composition. While these two types share similarities, they differ in visual appearance and the extra features they provide.

Pie Charts:

Pie charts display data as a circle divided into slices, each representing a category or component of the whole.
Pie charts help illustrate the relative contribution or distribution of different categories.
The size of each slice corresponds to the proportion or percentage of the whole that each category represents.
They are commonly used when there are a few distinct categories to represent and emphasize the proportionality between them.
Pie charts allow for quick and easy comparisons of the relative sizes of the categories at a glance.
They are often used in business reports, market research, and presentations to display market share, survey responses, or budget allocations.

Donut Charts:

Donut charts are similar to pie charts but have a hollow center, creating a ring-like appearance.
Like pie charts, donut charts represent the distribution or composition of a whole through slices, with each slice representing a category.
The size of each slice in a donut chart also corresponds to the proportion or percentage it represents.
The hollow center of a donut chart provides an additional space that can be used to display additional information or data.
Donut charts are beneficial when you want to emphasize the overall composition while also providing additional details or comparisons within the chart.
They are often used to present data with subcategories or to exhibit multiple datasets within the same chart, making it easy to compare different rings or categories.

Both pie charts and donut charts are effective for displaying the distribution or composition of a whole, with pie charts offering a straightforward representation. In contrast donut charts provide additional space for more detailed information or comparisons. The choice between them depends on the specific data and the level of detail you want to convey to the audience.

Gauge Charts and Funnel Charts

Gauge charts and funnel charts are data visualization tools used to represent specific data types and offer insights into a process or progression. Despite having distinct structures and purposes, both effectively communicate information visually.

Gauge Charts:

Gauge charts, also known as dial charts or speedometer charts, resemble the dashboard gauges found in vehicles.
Gauge charts visualize a single value within a predefined range, typically representing a performance metric, progress towards a goal, or a level of achievement.
They consist of a circular or semicircular shape divided into sections or arcs, with a pointer or a needle indicating the value on a scale.
Gauge charts effectively provide a quick and intuitive visual representation of a value about a target or reference point.
They are commonly used in key performance indicators (KPIs), customer satisfaction ratings, or progress tracking.
Gauge charts allow users to easily interpret whether the value falls within an acceptable range or needs attention based on pointer’s position.

Funnel Charts:

Funnel charts represent the stages or steps in a process, showing the progression or conversion rates from one stage to the next.
Funnel charts have a trapezoidal shape, with each section representing a stage of the process and the width of each section indicating the volume or proportion of data at that stage.
The width of the funnel decreases progressively from top to bottom, illustrating the reduction or conversion of data or participants at each stage.
Funnel charts are commonly used in marketing and sales to track the conversion rates from leads to customers, website visits to conversions, or any other sequential process.
They provide a visual understanding of the drop-off or attrition rates at each process stage, helping identify areas for improvement or optimization.
Funnel charts can also incorporate additional metrics or annotations to provide further insights into the process.

Gauge charts are effective for displaying a single value about a target or reference point. In contrast, funnel charts are helpful in visualizing the progression or conversion rates of a process. The choice between them depends on the type of data and the specific insights you want to communicate to the audience.

Tips for Creating Effective Data Visualizations

Below we will point out some effective data visualization best practices. By following these tips and continuously refining your approach, you can create compelling and informative visual representations of your data.

Determine your objective: Clearly define the purpose of your data visualization. Understanding your objective will help guide your design choices.
Choose the right chart type: Select a chart or graph that best represents your data and effectively communicates your message. Bar charts, line graphs, pie charts, and scatter plots are commonly used. Consider the variables you want to compare, the nature of your data, and the insights you want to convey.
Simplify and declutter: Keep your visualization clean and uncluttered. Avoid excessive decorations or unnecessary elements that distract from the main message. Focus on the essential information and remove any non-essential components.
Use appropriate colors: Select a color palette that enhances the readability and comprehension of your data visualization. Choose colors that provide contrast and make distinguishing between different data points or categories easy. Ensure color choices are accessible for individuals with color vision deficiencies.
Provide clear labels and titles: Clearly label your axes, data points, and any relevant units of measurement. Use descriptive titles and captions to provide context and help viewers understand the key takeaways from your visualization.
Use appropriate scales: Ensure your scales accurately represent the data being visualized. Misleading scaling can distort the interpretation of the information. Use consistent scales and indicate any breaks or discontinuities in the axis.
Incorporate appropriate data markers: Use data markers, such as dots, bars, or symbols, to represent individual data points or categories. Ensure these markers are easily distinguishable and not too small or too large.
Provide annotations and context: Include annotations or callouts to highlight important observations or provide additional context. It can help viewers understand key findings or trends and provide a deeper understanding of the data.
Test and iterate: Review your visualization and seek feedback from others. Test it with different audiences to ensure it effectively communicates the intended message. Make necessary adjustments and iterate on your design to improve clarity and comprehension.

Advantages and Disadvantages of Data Visualization

Data visualization has emerged as a crucial instrument for enterprises to comprehend and assess substantial quantities of data. Although data visualization techniques offer numerous benefits, businesses must acknowledge the potential drawbacks associated with their utilization.

Advantages of Data Visualization

Improved Understanding – Data visualization techniques can facilitate understanding complex data sets, benefiting businesses. Businesses can identify patterns and trends by presenting data visually and communicating insights effectively.
Enhanced Communication – Communicating data to a broader audience is a key advantage of data visualization for businesses. Visual representations of data can communicate complex information more quickly and intuitively than raw data tables.
Better Decision-Making – Making better-informed decisions based on data-driven insights is one of the benefits that businesses can derive from data visualization. By presenting data in a way that is easy to understand, businesses can identify potential opportunities or areas of concern, track performance against goals, and identify areas for improvement.
Time-Saving – Visual representations of data can help businesses identify patterns and outliers more quickly than raw data.
Increased Engagement – Data visualization possesses a greater potential for engagement when compared to conventional data analysis methods. Using interactive dashboards and infographics enhances the appeal and interactivity of data analysis, leading to heightened engagement and comprehension among users.

Disadvantages of Data Visualization

Misinterpretation – Accurate presentation and appropriate selection of data visualization techniques are imperative to prevent misinterpretation. Failing to adhere to these principles can result in erroneous conclusions or decisions based on flawed data analysis.
Bias – The creation of data visualization can introduce bias when individuals harbor preconceived notions or biases. Consequently, data may be presented in a manner that aligns with a specific agenda or viewpoint, compromising the objectivity and integrity of the visualization.
Overcomplication – When too much information is included, or the wrong visualization technique is used, data visualization can become overly complex. This intricacy may make it difficult for users to interpret the data, which could lead to misunderstanding and poor data comprehension.
Technical Barriers – Technical know-how and the use of specialized tools are required for the creation of data visualization. Businesses or individuals lacking the necessary resources or skills may find this requirement a substantial barrier to their ability to engage in data visualization.
Limited Data – The quantity and quality of the accessible data will determine how effective data visualization is. Incomplete or wrong data might limit the accuracy of data analysis, perhaps leading to defective findings and incorrect deductions.

Data Visualization Examples

Below is an example of data visualization in the form of Histogram:

We took a dataset of Open food facts from Kaggle. Open Food Facts is a free, open, collaborative database of food products from around the world, with ingredients, allergens, nutrition facts, and other information.

You can download the dataset from Kaggle and import it to Google Colab using the following steps:

• Go to your Google Drive and create a folder named openfoodfacts.
• Upload the en.openfoodfacts.org.products.tsv file to that folder. This file contains the data for over a million products in tab-separated format.
• Open a new notebook in Google Colab and mount your Google Drive by running this code:

from google.colab import drive
drive.mount('/drive')

• You will be asked to enter an authorization code. Follow the link provided and copy the code. Paste it in the box and press enter.
• Now you can access your Google Drive files from Colab. To read the Open Food Facts data, you can use the pandas library. Run this code:

import pandas as pd
data = pd.read_tsv('/drive/MyDrive/openfoodfacts/en.openfoodfacts.org.products.tsv', sep='\t')
data.head()

• This will show you the first five rows of the data. You can explore the data further using pandas methods and attributes, such as data.shape, data.columns, data.describe(), etc.

• To visualize the data, you can use libraries such as matplotlib, seaborn, plotly, etc.
For example, to plot a histogram of the energy values of the products, you can run this code:

import matplotlib.pyplot as plt
plt.hist(data['nutrition-score-fr_100g'].dropna(), bins=50)
plt.xlabel('Nutri-Score (kcal/100g)')
plt.ylabel('Frequency')
plt.title('Histogram of Nutrition values')
plt.show()

data visualization in the form of Histogram

Factors that Influence Data Visualization

Below we have highlighted the factors that influence data visualization:

Data characteristics: The nature of the data itself plays a significant role in determining the appropriate visualization techniques. Consider the data type (e.g., categorical, numerical, time-series), the measurement scale, the range of values, and any relationships or patterns within the data.
Audience and purpose: Understanding your audience and the purpose of the visualization is crucial. Consider the knowledge level, interests, and information needs of your audience. The purpose of the visualization, whether it is to inform, persuade, or explore insights, will also influence the design choices.
Context and domain: The context in which the data visualization will be used is important. Consider the specific domain or industry and any contextual factors that may impact the interpretation of the data. Different domains may require different visualization techniques or conventions.
Design principles: Effective data visualization relies on visual perception and cognition principles. Consider principles such as simplicity, clarity, consistency, and aesthetics. Use appropriate colors, fonts, layouts, and visual cues to guide the viewer’s attention and enhance comprehension.
Available tools and technology: The choice of visualization techniques may be influenced by the tools and technologies available. Consider the capabilities of the data visualization software or programming languages you are using and explore different charting libraries or visualization platforms.
Data quality and accuracy: The quality and accuracy of the data will impact the visual representation. Ensure the data is clean, complete, and reliable. Handle any missing or erroneous values appropriately, and provide transparency about the data sources and any potential limitations.
Ethical considerations: Data visualizations should adhere to ethical principles. Ensure the visualizations accurately represent the data and avoid distorting or misleading interpretations. Consider privacy and confidentiality concerns, especially when dealing with sensitive or personal data.
Iterative process: Creating effective data visualizations requires an iterative design, feedback, and refinement process. Test the visualizations with representative users, gather feedback, and make necessary adjustments to improve the clarity and impact of the visualization.

Data Visualization and Big Data

Big data visualization is a useful tool for making sense of massive and complicated data sets that are difficult to process and analyze using traditional approaches. Data visualization can assist in identifying patterns, trends, and relationships that may not be immediately obvious in raw data by portraying data using visual elements such as charts, graphs, and maps.

However, data visualization also has limitations regarding big data, such as the potential for cluttered and difficult-to-interpret visualizations. Specialized software, such as Tableau or Power BI, can process and analyze large and complex data sets to visualize big data. Data visualization techniques offer various customizable visualization options to cater to specific requirements. Additionally, data preparation and cleaning, as well as data governance and privacy considerations, must be considered to ensure that the data is accurate and consistent.

Conclusion

Data visualization techniques are significant for businesses to make sense of the massive amounts of data they collect daily. Businesses can transform raw data into meaningful insights that drive decision-making and enhance performance using the appropriate visualization technique. In 2023, we are seeing a surge in sophisticated and visually appealing data visualization techniques, such as interactive dashboards, AR and VR visualizations, and more. By staying up-to-date with the latest data visualization trends, businesses can gain a competitive advantage and make better decisions based on data-driven insights.