graphical representation of data and information is in eti

Politics and Elections

Ethics and ethical data visualization: a complete guide.

Discover the significance of ethical data visualization in our world. This comprehensive guide covers key ethical principles in data visualization, real-life examples of unethical practices, and strategies to ensure accuracy, fairness, and responsible design in your visual representations of data.

Introduction

We create and use huge amounts of data in all areas of life. It’s important to display and understand this information in an easy-to-grasp and meaningful way. Data visualization plays an important role in bridging the gap between raw data and human understanding. It is enabling us to gain insights, make informed decisions, and communicate complex ideas more effectively. That’s why the question of ethics in data visualization is vital!

From tracking global pandemics to understanding economic trends and social patterns, data visualization has become an essential tool in various fields.

However, with great power comes great responsibility. Data visualization impacts our views and choices, so it’s crucial to make sure visuals are truthful, precise, and ethical.

In this detailed blog post, we’ll explore the importance of ethics in data visualization. We will also cover five ethical principles of data visualization and examine examples of ethical and unethical practices. Finally, we’ll share tips and resources for creating effective, ethical visualizations.

The power of visuals in shaping views and decisions

Visuals greatly impact our perceptions and decision-making, as we naturally process images faster than text. Data visualization effectively grabs attention and communicates complex ideas or patterns due to our visual instincts.

Studies reveal that visuals boost our memory and understanding of information. They help us identify patterns, trends, and anomalies, simplifying conclusions and making informed decisions. Visuals also evoke emotions and empathy, deepening our connection to data and its consequences.

Given this influence, we must recognize the potential outcomes of their work. How data is displayed can affect perception and interpretation, and influence decisions based on that data. By carefully choosing charts, colors, scales, and other elements, we can clarify or confuse the message, guiding or misleading viewers.

This highlights the need for ethical principles in data visualization. The idea here is to ensure visuals are not just eye-catching but also honest, accurate, and unbiased. By embracing ethical practices, we can establish audience trust, foster a well-informed society, and encourage responsible decision-making.

The need for ethical considerations in dataviz efforts

As data visualization gains prominence in our data-driven world, the need for ethical considerations becomes increasingly important. Ethics in data visualization and its considerations are crucial in ensuring that visual representations of data are accurate, fair, and unbiased. As such, they are fostering trust and responsible decision-making.

When data visualizations are created without regard for ethical principles, they can lead to misinterpretations, misinformation, and potentially harmful situations.

There are several reasons why ethical considerations are essential in data visualization.

Why do we need ethical principles in data visualization?

They are summarized in the following five ethical principles in data visualization.

• Accuracy and honesty – Data visualizations should correctly represent the underlying data and not deliberately mislead or deceive the audience. Manipulating visuals to create a false impression or support a specific agenda, erodes trust and leads to poor decision-making.
• Clarity and simplicity – Visualizations should be designed to make the data easier to understand, avoid unnecessary complexity or clutter. Striking a balance between aesthetics and functionality is key to ensuring that the message is clear.
• Fairness and objectivity – Data visualizers should strive to present data objectively, without introducing personal bias or promoting stereotypes. Being transparent about data sources, methodology, and limitations can help establish credibility and promote fair interpretation.
• Privacy and trust – Respecting the privacy of individuals and organizations is critical when visualizing data. We should be mindful of potential privacy concerns and adhere to relevant laws, regulations, and ethical guidelines to protect sensitive information.
• Inclusiveness and accessibility – Ensuring that data visualizations are accessible and inclusive to diverse audiences is an important ethical consideration. This includes using color schemes readable by individuals with color vision deficiencies. It also can mean providing alternative text descriptions (alt text) for visually impaired users. It also means considering cultural sensitivities when designing visuals.

Each of these principles, within the context of dataviz ethics, will be discussed in greater detail later in this article.

Understanding ethics in data visualization

The role of ethics in data viz discipline.

Ethics play a vital role in data visualization. It guides the principles and practices that ensure visual representations of data are truthful, fair, and responsible. Since visuals shape our views and guide decisions, data visualizers need to follow ethical standards for clear and unbiased data communication.

Ethical factors in data visualization cover many issues like data honesty, visual clarity, fair representation, and privacy respect. By sticking to these rules, data visualizers build trust and credibility while effectively sharing complex ideas.

Ethics also matter during the whole data process, from gathering and analyzing data, creating graphics, and the final visual. Ethical aspects should be considered at each step, as biases or errors can harm the visual’s integrity and effectiveness.

Furthermore, ethical obligations extend to visualization consumers, who should remain vigilant against potential manipulation and assess information accuracy judiciously.

Finally, the role of ethics in dataviz is ensuring that we create and use visuals that are honest, accurate, and fair. As such, they will be promoting responsible decision-making and contributing to a more informed and ethical society.

The three basic steps in data visualization and their ethical considerations

Data collection.

Data collection is the first stage in the data visualization process, where relevant and accurate data is gathered from various sources. This essential step forms the foundation for subsequent analysis and visualization.

Data can be collected from primary sources, such as surveys, interviews, or experiments. Also, it can be gathered from secondary sources, like databases, published research, or government reports. In some cases, data may also be obtained through real-time sources, such as sensors or social media platforms.

The two main ethical considerations in this phase are:

• Ensuring data accuracy and completeness – Data collectors must strive to gather high-quality, reliable data to avoid misrepresentations or misleading conclusions. This may involve cross-checking sources, verifying data authenticity, or addressing potential gaps in the dataset.
• Respecting data privacy and consent – When collecting personal or sensitive information, we must privacy regulations and get informed consent from data subjects. This involves being transparent about data usage intentions and safeguarding collected data to prevent unauthorized access or misuse.

Data analysis

Data analysis involves examining and processing collected data to uncover trends, patterns, or insights that will inform the visualization. This step is essential to transforming raw data into meaningful information.

Data visualizers (designers, developers, journalists…) may use various techniques in their work. Those techniques are descriptive statistics, data cleaning, data aggregation, or even more advanced analytics methods like machine learning. They help reveal meaningful patterns or relationships within the data.

Ethical considerations during data analysis include:

• Minimizing biases and errors – Experts should use appropriate methods, tools, and techniques to reduce biases and errors during data processing. This involves critically evaluating data quality, being aware of potential pitfalls, and validating analytical results.
• Transparency in data processing – Analysts need to be transparent about the steps, assumptions, and methodologies used during data analysis. This enables others to verify, replicate, or challenge the results, promoting accountability and trust in the findings.

Data visualization (design)

Data visualization is the final step in this journey. It involves creating visual representations of data that effectively communicate insights, patterns, or trends to an audience. Designers utilize various forms, such as charts, graphs, maps, or interactive visualizations, to make complex data more accessible and understandable.

Good design choices, including colors, scales, and layout, play a significant role in conveying the intended message and influencing viewer interpretation.

The main ethical considerations during data visualization design include:

• Honest data presentation – Designers must avoid manipulating or distorting data to mislead or misinform viewers. This involves choosing appropriate chart types, scales, and data transformations that accurately show the underlying data.
• Accessible and inclusive design – Designers should create visuals suitable to diverse audience needs and preferences. By doing so, it is ensured that the information is accessible to as many people as possible. This may involve considering color blindness, screen reader compatibility, or providing alt descriptions for visual elements

Five ethical principles in data visualization

We mentioned them earlier, and in this section we’ll go deeper into each of the five ethical principles in data visualization.

The five ethical principles in data visualization are:

• Honesty and accuracy
• Clarity and simplicity
• Fairness and objectivity
• Respect for privacy and confidentiality
• Cultural sensitivity and inclusivity

#1 Honesty and accuracy

Being honest and accurate in data visualization means presenting data truthfully, without distortion or manipulation.

This involves using the correct data, avoiding cherry-picking or misrepresenting information to support a specific viewpoint. Honesty and accuracy build trust with the audience, ensuring that the visualizations effectively convey the intended message. To maintain honesty and accuracy, designers should verify data sources, cross-check information, and be transparent about data limitations or uncertainties.

#2 Clarity and simplicity

Clear and simple visualizations make complex data more understandable and accessible. Clarity and simplicity involve choosing appropriate chart types, using consistent and readable fonts, and organizing the layout for easy navigation.

Simplifying visuals helps the audience to quickly grasp key insights, patterns, or trends without confusion. To achieve clarity and simplicity, designers should prioritize essential information, minimize visual clutter, and use colors, labels, and legends effectively.

#3 Fairness and objectivity

Fair and objective data visualizations avoid biases and present data impartially. This principle ensures that visualizations don’t favor a particular viewpoint or mislead the audience with biased interpretations.

Fairness and objectivity involve selecting unbiased data sources, acknowledging data limitations, and presenting alternative perspectives when appropriate.

To achieve fairness and objectivity, we must be aware of biases in data at every step. That includes collection, analysis, and the actual design and presentation work, and take steps to minimize them.

#4 Respect for privacy and confidentiality

Respecting privacy and confidentiality in data visualization means protecting sensitive or personal information.

This principle ensures that visualizations don’t violate privacy rights or expose confidential data. Respecting privacy and confidentiality involves anonymizing data, aggregating information to a safe level, and obtaining consent when necessary.

To maintain privacy and confidentiality, designers should follow relevant data protection regulations, guidelines, and best practices throughout the visualization process.

#5 Cultural sensitivity and inclusivity

Cultural sensitivity and inclusivity in data visualization involve considering diverse audience needs, preferences, and backgrounds.

This principle ensures that visualizations are accessible and respectful to people from various cultural, linguistic, or ability backgrounds. Cultural sensitivity and inclusivity may involve using appropriate colors, symbols, and language to avoid offending or excluding viewers.

To promote cultural sensitivity and inclusivity, we need to research our audience and seek feedback. And not just that – we needt o be open to adjusting our work to accommodate diverse perspectives.

The Hippocratic Oath for dataviz: A commitment to ethical practices

Jason Moore, who’s now a Senior Computer Scientist at the Air Force Research Lab, proposed a version of the Hippocratic Oath for data visualization. It was first introduced at VisWeek in 2011 and then published on Robert Kosara’s blog .

With some modifications, here’s the Hippocratic Oath for data visualization :

“ I shall not use visualization to intentionally hide or confuse the truth which it is intended to portray. I will respect the great power visualization has in garnering wisdom and misleading the uninformed. I accept this responsibility willfully and without reservation, and promise to defend this oath against all enemies, both domestic and foreign. ”

Real-life examples of unethical data visualization practices

Ethical practices are essential to ensure that information is presented honestly, accurately, and without bias.

However, not all visualizations adhere to these principles. That results in misleading or deceptive representations that can distort the audience’s understanding and lead to uninformed decision-making.

In this section, we will explore real-life examples of unethical data visualization practices. We’ll highlighting instances where misleading visuals, data manipulation, and inappropriate visual elements have been used to mislead and confuse.

Learning from these cases, we can raise awareness of the potential pitfalls in data visualization. Moreover, we can emphasize the importance of adhering to ethical principles in creating and consuming visual representations of data.

Misleading or deceptive visualizations

A classic example of misleading visualization is the use of truncated or manipulated y-axes in bar charts or line charts. By adjusting the axis scale, a designer can make small differences in data appear more significant, leading to incorrect conclusions. For example, a company may use a truncated y-axis to exaggerate its growth. Or, to downplay a competitor’s performance, misleading investors or customers.

Gun deaths in Florida

Initially, this chart might make you think that with the introduction of the “Stand Your Ground” law, the number of gun deaths in Florida dropped significantly.

But take another look at the Y-axis. Do you see it? For some reason, the values are ordered in an unusual way – with the highest values at the bottom of the axis and the lowest values at the top.

Most people’s first reaction to this would be that the introduction of this law caused a drop in the number of gun deaths in Florida, not the other way around.

The author, Christine Chan, has tried justifying her decision in a tweet that has since been removed:

@john_self Thanks for the feedback. I prefer to show deaths in negative terms (inverted). It’s a preference really, can be shown either way.

And while it’s likely that she didn’t have an intention to mislead, the impression left made a damage.

People on welfare vs full time jobs

A truncated (or completely missing) Y-axis is the main reason for confusion here. By omitting baseline values and the scale, the difference between the number of people on welfare and those with a full time job seems more drastic than it really is. Bar graph is an appropriate choice for this type of data but it’s missing correct Y-axis.

Adding a meaningful Y-axis with values and a proper scale would help paint a more objective picture here.

CNN’s bar chart shows presidential approval rates

Again, truncated Y-axes give the feeling that the differences between different presidents are even more apparent in this poorly designed piece by CNN. The source is subreddit /dataisugly .

Something doesn’t add up – biggest COVID worries

The sum of all percentages in this case is well over 100%. One doesn’t need to be a data scientist to see that something is off here. Using a pie chart to display percentages is meaningful in many situations, but not every dataset is suitable for it.

In this instance, respondents had the option to choose multiple answers. For example, one might be concerned not only about getting infected themselves but also about their family members becoming infected.

Additionally, it is common practice for the legend to follow the order of pie slices and to have the largest pie slice start at 12 o’clock, proceeding clockwise. In this example, “Getting it” is the first label in the legend section but appears second, regardless of whether we go clockwise or counterclockwise.

Other unethical approaches to data visualization

Manipulation of data to support a specific agenda (cherry picking).

Selectively presenting data points or time periods that support a specific narrative, while ignoring contradictory or less favorable information, can distort the audience’s understanding of an issue.

An example of data manipulation can be seen in the selective presentation of data to support a political or social agenda.

A political group might cherry-pick data points that show a positive trend for their policies. At the same time they would be ignoring negative trends or omitting relevant context. This manipulation can mislead the public and distort their understanding of the issue, promoting a biased perspective.

Inappropriate use of visual elements that trigger bias or stereotype

In 2015, the New York Times published an infographic titled “Murder in America” that aimed to show the relationship between race and homicide victims. However, the visualization faced criticism for perpetuating racial stereotypes. The chart used red dots to represent black victims and blue dots for white victims, with red dots appearing more prominent and alarming. Critics argued that the choice of colors could reinforce negative stereotypes about African Americans and crime. Furthermore, the chart lacked context and socio-economic factors that contribute to crime rates, providing a simplified and potentially misleading view of a complex issue.

It has been removed since.

Ignoring data uncertainty

Ignoring data uncertainty in visualizations can lead to misleading representations of information, causing audiences to develop a false sense of precision and reliability. It’s one of the most overlooked situations and it can happen in a number of cases.

• Election polls and forecasts – During election seasons, various organizations provide polling data and forecast models to predict election outcomes. Failing to include margins of error, confidence intervals, or uncertainty ranges in these visualizations can give the impression that the predictions are more precise and certain than they actually are. This might lead audiences to be overconfident in the forecasted results, which can affect their voting behavior or expectations.
• Scientific research – In fields like medicine or climate science, research findings often include inherent uncertainty due to factors like sample size, measurement errors, or model assumptions. When visualizing these findings, it’s crucial to include error bars, confidence intervals, or other visual cues that represent the uncertainty. Omitting these elements can create a misleading impression of the findings’ certainty, leading to misinformed decisions by policymakers, healthcare professionals, or the public.

Confusing area and radius in scatter plots

An unethical approach in data visualization can involve the inappropriate use of bubble size in scatter plots, causing confusion and misinterpretation of the data.

In scatter plots with bubbles, the size of the bubbles often represents a third variable in addition to the two variables plotted on the x and y axes. However, when the bubble sizes are not scaled properly, it can create misleading impressions about the relationships between variables.

For example, let’s consider a scatter plot that compares a country’s GDP per capita (x-axis) with life expectancy (y-axis), using bubble size to represent the total population. If the bubble size is scaled by radius, rather than area, it can create a distorted perception of the population sizes. Since the area of a circle increases with the square of its radius, a bubble with twice the radius would represent a population four times larger, not twice as large as one might assume. This can lead audiences to overestimate the differences in population between countries.

Strategies for ethical data visualization

Data visualizers must use particular techniques and strategies that encourage ethical decisions throughout the design process to produce visuals that uphold ethical principles.

In this section, we’ll go over four essential strategies for creating ethical dataviz:

• exercising critical thinking and challenging the data source
• selecting the best type of visualization for the intended audience
• achieving a balance between form and function
• ensuring accessibility and inclusivity

Critical thinking and questioning the data source

To ensure the accuracy and honesty of a visualization, it is crucial to critically examine the data source.

Start by assessing the credibility and reliability of the source, and consider potential biases or errors that may be present. Be transparent about the limitations of the data and any assumptions made during the analysis. When working with multiple sources, cross-reference and verify the information to minimize the risk of inaccuracies.

Additionally, be open to feedback and willing to revise the visualization if new or contradictory information emerges.

Choosing the right visualization type for the message

Selecting the appropriate visualization type is essential for effectively communicating the message and avoiding misinterpretation.

Begin by understanding the data and identifying the key insights you wish to convey.

Then, choose a visualization type that best represents these insights.

Do it by considering factors such as:

• the nature of the data (categorical, numerical, or time-based)
• relationships between variables
• the audience’s familiarity with different chart types

Avoid using misleading or overly complex visualizations that may distort the message or confuse the audience.

Balancing aesthetics and functionality

While visually appealing designs can capture attention, it is important not to prioritize aesthetics over functionality.

Strive for a balance between the two by creating visuals that are both engaging and easy to understand. Use color, typography, and layout to enhance the clarity of the message. However, avoid unnecessary embellishments or visual clutter that may distract from the data.

Ensure that the design choices support an accurate interpretation of the information and do not introduce biases or misconceptions.

Ensuring accessibility and inclusivity

An ethical visualization should be accessible and inclusive, taking into account the diverse needs and perspectives of the audience.

To achieve this, consider factors such as color contrast, font size, and alternative text for visually impaired users. Use culturally sensitive and neutral language. Also, be mindful of potential stereotypes or biases that may be reinforced through visual elements.

Additionally, test the visualization with different user groups and gather feedback to identify and address any barriers to accessibility or inclusivity.

By implementing these practices, data visualizers can create visuals that are not only effective but also respectful of their audience’s diverse needs and experiences.

Finally, ethical data visualization is critical in conveying information accurately and responsibly, shaping perceptions, and influencing decision-making.

Data visualizers and consumers can foster trust, promote responsible practices, and contribute to a more informed and fair society. We can do that by following good ethics in data visualization.

This section reiterates the importance of ethical data visualization. It’s a tool helping us foster trust, enable the shared responsibility, and encourage ethical practices for a better society.

Ethics in data visualization and fostering trust

Ethical data visualization helps build trust between data visualizers and their audience. It does that by ensuring that information is presented honestly, accurately, and without bias.

Trustworthy visuals enable audiences to make informed decisions and develop a deeper understanding of complex issues.

By upholding ethical principles, we can establish credibility and foster a trusting relationship with our audiences. And that is vital for effective communication.

The responsibility of data visualizers and consumers

Both data visualizers and consumers have a shared responsibility in promoting ethical practices.

Data designers should follow ethical principles throughout the entire data lifecycle, from collection and analysis to visualization. Consumers should be aware of potential biases and manipulation in the visuals they encounter. They should be able to critically assess the accuracy and reliability of the information presented.

Together, data visualizers and consumers can drive the adoption of ethical practices and create a more responsible data visualization community.

Let’s do it the right way!

Engaging in ethical data visualization not only benefits the individual data visualizer and their audience. It also contributes to a more informed and fair society as a whole.

By committing to ethical practices, we can empower audiences to make better decisions. Also, we can help them develop a deeper understanding of the world around them.

Therefore, with all of this in our minds, I encourage all data visualizers and consumers to actively engage in ethical practices. This will help us in creating a positive impact on society and promoting responsible decision-making.

Additional reading about ethics in data visualization

Here’s some good additional reading that might help you grasp these concepts of ethics in data visualization even more.

• Ethical data viz by Joe Hardin
• The ethics of data visualization by Peter Haferl
• Ethical Dimensions of Visualization Research by Michael Correll
• Practicing good ethics i dataviz by UC Davis
• Ethical infographics In data visualization, journalism meets engineering by Alberto Cairo

You also check out the dataviz fail on a map of Europe done by the European Commission – one of the first posts on this blog.

ABOUT THE AUTHOR

Vibor Cipan

Don't stop reading

Inflation data visualization tutorial and a case study

Inflation rates in Europe 2023 by country (Latest data and maps)

Incredible story of John Nash and his short PhD thesis

2024 Europe gas storage reserves – by country, updated daily

Top 200 most common last names in the USA: A full list and a racial distribution

List and data visualization of the top 20 countries polluting the oceans the most

List of Europe’s most dangerous and worst roads

Seven principles in art and design that’ll improve your data visualizations

Comparison of Slovenia, Croatia, and Western Balkans in 2023 – economy and politics

Interactive map of Pangaea / Pangea with present-day borders and a globe

October in different languages of Europe, maps, and etymology

Unemployment rates in Europe, by country, latest data for 2022

Older articles, september in different languages of europe, maps, and etymology, road quality in europe: the best and worst roads in europe, here’s how heat waves are affecting your mental health and well-being, august in different languages of europe, maps, and etymology, the uk got its new national highest temperature record of 40.3 °c, how many healthy life years do europeans live.

• School Guide
• Mathematics
• Number System and Arithmetic
• Trigonometry
• Probability
• Mensuration
• Maths Formulas
• Class 8 Maths Notes
• Class 9 Maths Notes
• Class 10 Maths Notes
• Class 11 Maths Notes
• Class 12 Maths Notes

Graphical Representation of Data

Graphical Representation of Data: Graphical Representation of Data,” where numbers and facts become lively pictures and colorful diagrams . Instead of staring at boring lists of numbers, we use fun charts, cool graphs, and interesting visuals to understand information better. In this exciting concept of data visualization, we’ll learn about different kinds of graphs, charts, and pictures that help us see patterns and stories hidden in data.

There is an entire branch in mathematics dedicated to dealing with collecting, analyzing, interpreting, and presenting numerical data in visual form in such a way that it becomes easy to understand and the data becomes easy to compare as well, the branch is known as Statistics .

The branch is widely spread and has a plethora of real-life applications such as Business Analytics, demography, Astro statistics, and so on . In this article, we have provided everything about the graphical representation of data, including its types, rules, advantages, etc.

Table of Content

What is Graphical Representation

Types of graphical representations, line graphs, histograms , stem and leaf plot , box and whisker plot .

• Graphical Representations used in Maths

Value-Based or Time Series Graphs 

Frequency based, principles of graphical representations, advantages and disadvantages of using graphical system, general rules for graphical representation of data, frequency polygon, solved examples on graphical representation of data.

Graphics Representation is a way of representing any data in picturized form . It helps a reader to understand the large set of data very easily as it gives us various data patterns in visualized form.

There are two ways of representing data,

• Pictorial Representation through graphs.

They say, “A picture is worth a thousand words”.  It’s always better to represent data in a graphical format. Even in Practical Evidence and Surveys, scientists have found that the restoration and understanding of any information is better when it is available in the form of visuals as Human beings process data better in visual form than any other form.

Does it increase the ability 2 times or 3 times? The answer is it increases the Power of understanding 60,000 times for a normal Human being, the fact is amusing and true at the same time.

Check: Graph and its representations

Comparison between different items is best shown with graphs, it becomes easier to compare the crux of the data about different items. Let’s look at all the different types of graphical representations briefly: 

A line graph is used to show how the value of a particular variable changes with time. We plot this graph by connecting the points at different values of the variable. It can be useful for analyzing the trends in the data and predicting further trends. 

A bar graph is a type of graphical representation of the data in which bars of uniform width are drawn with equal spacing between them on one axis (x-axis usually), depicting the variable. The values of the variables are represented by the height of the bars. 

This is similar to bar graphs, but it is based frequency of numerical values rather than their actual values. The data is organized into intervals and the bars represent the frequency of the values in that range. That is, it counts how many values of the data lie in a particular range. 

It is a plot that displays data as points and checkmarks above a number line, showing the frequency of the point.  

This is a type of plot in which each value is split into a “leaf”(in most cases, it is the last digit) and “stem”(the other remaining digits). For example: the number 42 is split into leaf (2) and stem (4).  

These plots divide the data into four parts to show their summary. They are more concerned about the spread, average, and median of the data. 

It is a type of graph which represents the data in form of a circular graph. The circle is divided such that each portion represents a proportion of the whole. 

Graphical Representations used in Math’s

Graphs in Math are used to study the relationships between two or more variables that are changing. Statistical data can be summarized in a better way using graphs. There are basically two lines of thoughts of making graphs in maths: 

• Value-Based or Time Series Graphs

These graphs allow us to study the change of a variable with respect to another variable within a given interval of time. The variables can be anything. Time Series graphs study the change of variable with time. They study the trends, periodic behavior, and patterns in the series. We are more concerned with the values of the variables here rather than the frequency of those values. 

Example: Line Graph

These kinds of graphs are more concerned with the distribution of data. How many values lie between a particular range of the variables, and which range has the maximum frequency of the values. They are used to judge a spread and average and sometimes median of a variable under study.

Also read: Types of Statistical Data

• All types of graphical representations follow algebraic principles.
• When plotting a graph, there’s an origin and two axes.
• The x-axis is horizontal, and the y-axis is vertical.
• The axes divide the plane into four quadrants.
• The origin is where the axes intersect.
• Positive x-values are to the right of the origin; negative x-values are to the left.
• Positive y-values are above the x-axis; negative y-values are below.

• It gives us a summary of the data which is easier to look at and analyze.
• It saves time.
• We can compare and study more than one variable at a time.

Disadvantages

• It usually takes only one aspect of the data and ignores the other. For example, A bar graph does not represent the mean, median, and other statistics of the data. 
• Interpretation of graphs can vary based on individual perspectives, leading to subjective conclusions.
• Poorly constructed or misleading visuals can distort data interpretation and lead to incorrect conclusions.

Check : Diagrammatic and Graphic Presentation of Data

We should keep in mind some things while plotting and designing these graphs. The goal should be a better and clear picture of the data. Following things should be kept in mind while plotting the above graphs: 

• Whenever possible, the data source must be mentioned for the viewer.
• Always choose the proper colors and font sizes. They should be chosen to keep in mind that the graphs should look neat.
• The measurement Unit should be mentioned in the top right corner of the graph.
• The proper scale should be chosen while making the graph, it should be chosen such that the graph looks accurate.
• Last but not the least, a suitable title should be chosen.

A frequency polygon is a graph that is constructed by joining the midpoint of the intervals. The height of the interval or the bin represents the frequency of the values that lie in that interval. 

Question 1: What are different types of frequency-based plots? 

Types of frequency-based plots:  Histogram Frequency Polygon Box Plots

Question 2: A company with an advertising budget of Rs 10,00,00,000 has planned the following expenditure in the different advertising channels such as TV Advertisement, Radio, Facebook, Instagram, and Printed media. The table represents the money spent on different channels. 

Draw a bar graph for the following data. 

• Put each of the channels on the x-axis
• The height of the bars is decided by the value of each channel.

Question 3: Draw a line plot for the following data 

• Put each of the x-axis row value on the x-axis
• joint the value corresponding to the each value of the x-axis.

Question 4: Make a frequency plot of the following data: 

• Draw the class intervals on the x-axis and frequencies on the y-axis.
• Calculate the midpoint of each class interval.

Now join the mid points of the intervals and their corresponding frequencies on the graph. 

This graph shows both the histogram and frequency polygon for the given distribution.

Related Article:

Graphical Representation of Data| Practical Work in Geography Class 12 What are the different ways of Data Representation What are the different ways of Data Representation? Charts and Graphs for Data Visualization

Conclusion of Graphical Representation

Graphical representation is a powerful tool for understanding data, but it’s essential to be aware of its limitations. While graphs and charts can make information easier to grasp, they can also be subjective, complex, and potentially misleading . By using graphical representations wisely and critically, we can extract valuable insights from data, empowering us to make informed decisions with confidence.

Graphical Representation of Data – FAQs

What are the advantages of using graphs to represent data.

Graphs offer visualization, clarity, and easy comparison of data, aiding in outlier identification and predictive analysis.

What are the common types of graphs used for data representation?

Common graph types include bar, line, pie, histogram, and scatter plots , each suited for different data representations and analysis purposes.

How do you choose the most appropriate type of graph for your data?

Select a graph type based on data type, analysis objective, and audience familiarity to effectively convey information and insights.

How do you create effective labels and titles for graphs?

Use descriptive titles, clear axis labels with units, and legends to ensure the graph communicates information clearly and concisely.

How do you interpret graphs to extract meaningful insights from data?

Interpret graphs by examining trends, identifying outliers, comparing data across categories, and considering the broader context to draw meaningful insights and conclusions.

Please Login to comment...

Similar reads.

• School Learning
• Maths-Class-9

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

• Math Article

Graphical Representation

Graphical Representation is a way of analysing numerical data. It exhibits the relation between data, ideas, information and concepts in a diagram. It is easy to understand and it is one of the most important learning strategies. It always depends on the type of information in a particular domain. There are different types of graphical representation. Some of them are as follows:

• Line Graphs – Line graph or the linear graph is used to display the continuous data and it is useful for predicting future events over time.
• Bar Graphs – Bar Graph is used to display the category of data and it compares the data using solid bars to represent the quantities.
• Histograms – The graph that uses bars to represent the frequency of numerical data that are organised into intervals. Since all the intervals are equal and continuous, all the bars have the same width.
• Line Plot – It shows the frequency of data on a given number line. ‘ x ‘ is placed above a number line each time when that data occurs again.
• Frequency Table – The table shows the number of pieces of data that falls within the given interval.
• Circle Graph – Also known as the pie chart that shows the relationships of the parts of the whole. The circle is considered with 100% and the categories occupied is represented with that specific percentage like 15%, 56%, etc.
• Stem and Leaf Plot – In the stem and leaf plot, the data are organised from least value to the greatest value. The digits of the least place values from the leaves and the next place value digit forms the stems.
• Box and Whisker Plot – The plot diagram summarises the data by dividing into four parts. Box and whisker show the range (spread) and the middle ( median) of the data.

General Rules for Graphical Representation of Data

There are certain rules to effectively present the information in the graphical representation. They are:

• Suitable Title: Make sure that the appropriate title is given to the graph which indicates the subject of the presentation.
• Measurement Unit: Mention the measurement unit in the graph.
• Proper Scale: To represent the data in an accurate manner, choose a proper scale.
• Index: Index the appropriate colours, shades, lines, design in the graphs for better understanding.
• Data Sources: Include the source of information wherever it is necessary at the bottom of the graph.
• Keep it Simple: Construct a graph in an easy way that everyone can understand.
• Neat: Choose the correct size, fonts, colours etc in such a way that the graph should be a visual aid for the presentation of information.

Graphical Representation in Maths

In Mathematics, a graph is defined as a chart with statistical data, which are represented in the form of curves or lines drawn across the coordinate point plotted on its surface. It helps to study the relationship between two variables where it helps to measure the change in the variable amount with respect to another variable within a given interval of time. It helps to study the series distribution and frequency distribution for a given problem.  There are two types of graphs to visually depict the information. They are:

• Time Series Graphs – Example: Line Graph
• Frequency Distribution Graphs – Example: Frequency Polygon Graph

Principles of Graphical Representation

Algebraic principles are applied to all types of graphical representation of data. In graphs, it is represented using two lines called coordinate axes. The horizontal axis is denoted as the x-axis and the vertical axis is denoted as the y-axis. The point at which two lines intersect is called an origin ‘O’. Consider x-axis, the distance from the origin to the right side will take a positive value and the distance from the origin to the left side will take a negative value. Similarly, for the y-axis, the points above the origin will take a positive value, and the points below the origin will a negative value.

Generally, the frequency distribution is represented in four methods, namely

• Smoothed frequency graph
• Pie diagram
• Cumulative or ogive frequency graph
• Frequency Polygon

Merits of Using Graphs

Some of the merits of using graphs are as follows:

• The graph is easily understood by everyone without any prior knowledge.
• It saves time
• It allows us to relate and compare the data for different time periods
• It is used in statistics to determine the mean, median and mode for different data, as well as in the interpolation and the extrapolation of data.

Example for Frequency polygonGraph

Here are the steps to follow to find the frequency distribution of a frequency polygon and it is represented in a graphical way.

• Obtain the frequency distribution and find the midpoints of each class interval.
• Represent the midpoints along x-axis and frequencies along the y-axis.
• Plot the points corresponding to the frequency at each midpoint.
• Join these points, using lines in order.
• To complete the polygon, join the point at each end immediately to the lower or higher class marks on the x-axis.

Draw the frequency polygon for the following data

Mark the class interval along x-axis and frequencies along the y-axis.

Let assume that class interval 0-10 with frequency zero and 90-100 with frequency zero.

Now calculate the midpoint of the class interval.

Using the midpoint and the frequency value from the above table, plot the points A (5, 0), B (15, 4), C (25, 6), D (35, 8), E (45, 10), F (55, 12), G (65, 14), H (75, 7), I (85, 5) and J (95, 0).

To obtain the frequency polygon ABCDEFGHIJ, draw the line segments AB, BC, CD, DE, EF, FG, GH, HI, IJ, and connect all the points.

Frequently Asked Questions

What are the different types of graphical representation.

Some of the various types of graphical representation include:

• Line Graphs
• Frequency Table
• Circle Graph, etc.

Read More:  Types of Graphs

What are the Advantages of Graphical Method?

Some of the advantages of graphical representation are:

• It makes data more easily understandable.
• It saves time.
• It makes the comparison of data more efficient.

Leave a Comment Cancel reply

Your Mobile number and Email id will not be published. Required fields are marked *

Request OTP on Voice Call

Post My Comment

Very useful for understand the basic concepts in simple and easy way. Its very useful to all students whether they are school students or college sudents

Thanks very much for the information

Register with BYJU'S & Download Free PDFs

Register with byju's & watch live videos.

Ethical Data Viz

Arguably, data have the broadest impact in engaging readers, changing minds, and determining policy when they are presented graphically. It is the potential for enormous impact that requires a data scientist to think most carefully about how their visualizations are created and then subsequently consumed.

Many of us already teach data visualization in our statistics and data science classes. Therefore, introducing an ethical framework and a theory on valid graphics will be a natural fit into many classes.

Michael Correll writes about the “Ethical Dimensions of Visualization Research” and suggests that we:

have obligations in that we have a great deal of power over how people ultimately make use of data, both in the patterns they see and the conclusions they draw.

He further suggests that in every data visualization it is important to:

Make the Invisible Visible Collect Data with Empathy Challenge Structures of Power

Along the same lines, Nolan and Perrett describe three important ways that graphics can be used to convey information from data.

• Make the data stand out
• Facilitate comparisons
• Add information

The Basic Tenets of Graphing

As a first foray into understanding how graphical information is utilized, it is important to discuss basic ideas of graph structure and visual perception. In order to present data well, one must understand how to read a chart and how others read a chart.

Since Huff’s well-known “How to Lie with Statistics” was published in 1954, statistics educators have long taught students about the importance of scale, including zero as a baseline (usually), labeling axes, and pruning superfluous chart junk.

Less well known, however, are more nuanced considerations of how a graph is interpreted. For example, Cleveland and McGill were able to rank different visual cues, summarized and expanded on by Yau.

Additionally, if the observer cannot see the graphic clearly, the information cannot be conveyed. Considering font size, labels, and colors are of utmost importance and part of the ethical considerations for all data scientists. For example, the R package RColorBrewer provides different color schemes for optimal visual perception. 1 A discussion of using color in R (including color blindness) is provided on Jenny Bryan’s STAT545 course website https://stat545.com/colors.html .

Compelling Examples

Because educating data scientists is about engagement as well as information, it is often of utmost importance to provide examples of why and how visualizations can go wrong. Unfortunately, it is not difficult to find graphics that misrepresent information.

• The first example is astonishingly problematic, although for most people, it really takes a second glance to focus in on the y-axis.

• Or another plot that has gotten a lot of press is the following. What is wrong with this plot? (Hint: again, look at the y-axis… apologies if the image is hard to see!)

• Recently, the Georgia Department of Health came out with a grouped barplot showing the number of COVID-19 cases by day in 5 populous counties in GA. The bars were arranged in some kind of decreasing order, but at first glance, the typical reader will think that the bars are sorted according to time increasing along the x-axis.

• In his book How Charts Lie , Albert Cairo discusses the importance of graphs to communicate uncertainty. Consider the hurricane graph below that describes a “cone of uncertainty”. The increasing path is meant to indicate that the path becomes less certain, but the widening is commonly interpreted to indicate that the hurricane is getting larger or stronger. (The graphical issues are described in detail from an excellent NY Times interactive article “Those Hurricane Maps Don’t Mean What You Think They Mean”, https://www.nytimes.com/interactive/2019/08/29/opinion/hurricane-dorian-forecast-map.html .)

Decision Making

As mentioned above as a first step, students should understand the structure of graphics and they should be exposed to as many problematic examples as possible. Many of the examples are self-explanatory (especially after the issue has been pointed out), and the more such patterns are presented, the easier it will be for students to identify problematic graphs and create non-problematic graphs.

But along with practice identifying graphics themselves, it is valuable for students to really understand why it is so important to create graphics that convey real information.

Edward Tufte, known for his work in visual communication of information, provides an example of a poor visualization having a catastrophic outcome. The example is based on the Space Shuttle Challenger disaster from January 28, 1986 when the space craft took off from Cape Canaveral, FL and immediate exploded, killing all seven astronauts aboard. We now know that the reason for the explosion was due to the failure of two rubber O-rings which malfunctioned due to the cold temperature of the day ( \(\sim 29^\circ\) F).

It is now understood that the risks due to the cold weather were known by many of the engineers, but they were not able to convince the powers that be to postpone the launch. The evidence was clear but it was poorly communicated. Indeed, there was no intent to mislead or fool the reader. Just the opposite, the engineers were trying to use their graphics to argue the truth; unfortunately, the graphics were not up to the task.

Tufte created the graphic below which should have been used before the launch to convince others to postpone. The basic scatterplot is extremely convincing.

Classroom Activities

Of course, a part of teaching any topic is structuring classroom activities. In 2019, we wrote a series of blog posts, and we point you to the post on Teaching Data Visualization which includes ideas for assignments as well as a large number of resources for books, articles, and courses that include classroom activities and assessment.

Modern Data Science with R devotes an entire chapter to Ethics (free online), including data visualization ethics, and includes homework problems to use.

But at the end of the day, we believe that the conversations you have with your students and the ideas generated from many examples will have the most powerful impact on helping students be good consumers and producers of data visualizations. Keep asking your students good questions:

• What did you learn from the chart someone else made?
• What did someone else learn from the chart you made?

Stephanie Andalde. 2014. “Misleading Graphs: Real Life Examples.” http://www.statisticshowto.com/misleading-graphs/ .

Modern Data Science with R by Baumer, Kaplan, and Horton devotes a chapters to ethics, including the ethics associated with visualizations. (As a textbook, MDSR provides many great examples and end of chapter exercises.)

Alberto Cairo. 2019. “How Charts Lie.”

Cleveland and McGill. 1984. “Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods”.

Alberto Cairo & Tala Schlossberg, 8/29/2019, “Those Hurricane Maps Don’t Mean What You Think They Mean”, New York Times , https://www.nytimes.com/interactive/2019/08/29/opinion/hurricane-dorian-forecast-map.html

Michael Correll. 2019. “Ethical Dimensions of Visualization Research.” Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. https://dl.acm.org/doi/10.1145/3290605.3300418 ; https://arxiv.org/pdf/1811.07271.pdf .

“Data Visualization”, MSIS 2696 at Santa Clara University, A Reader on Data Visualization, Chapter 5 on Ethics

Deb Nolan and Jamis Perrett Teaching and Learning Data Visualization: Ideas and Assignments , TAS 2016.

Edward Tufte, Visual and Statistical Thinking: Displays of Evidence for Making decisions

Nathan Yau, Data Points: Visualization That Means Something

Don’t know the best way to present your work visually? Try from Data to Viz

At RStudio::conf 2020, The Glamour of Graphics , Will Chase makes some very important points about how and why making good graphics matters. The talk might be summarized by the plot below: fonts matter.

About this blog

Each day during the summer of 2019 we blogged on a given topic of interest to educators teaching data science and statistics courses. This summer we are focusing on data ethics. Each entry is intended to provide a short overview of why it is interesting and how it can be applied to teaching. We anticipate that these introductory pieces can be digested daily in 20 or 30 minute chunks that will leave you in a position to decide whether to explore more or integrate the material into your own classes. By following along for the summer, we hope that you will develop a clearer sense for the fast moving landscape of data science. Sign up for emails at https://groups.google.com/forum/#!forum/teach-data-science (you must be logged into Google to sign up).

We always welcome comments on entries and suggestions for new ones.

The original colorbrewer was designed by Cynthia Brewer as described at http://colorbrewer2.org . ↩

• Closing 2020: A summer of ethics in data science education
• Data Sources
• Integrating ethics training into any quantitative course
• A preview of the JSM
• Social Justice & Data Science
• Engaging data science students with COVID-19 data
• Philosophical Ethics for Data Science
• Hippocratic Oath
• Data Feminism
• Bookclub on Data Science Ethics
• communication
• visualization
• collaboration
• data-wrangling

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Patterns (N Y)
• v.1(9); 2020 Dec 11

Principles of Effective Data Visualization

Stephen r. midway.

1 Department of Oceanography and Coastal Sciences, Louisiana State University, Baton Rouge, LA 70803, USA

We live in a contemporary society surrounded by visuals, which, along with software options and electronic distribution, has created an increased importance on effective scientific visuals. Unfortunately, across scientific disciplines, many figures incorrectly present information or, when not incorrect, still use suboptimal data visualization practices. Presented here are ten principles that serve as guidance for authors who seek to improve their visual message. Some principles are less technical, such as determining the message before starting the visual, while other principles are more technical, such as how different color combinations imply different information. Because figure making is often not formally taught and figure standards are not readily enforced in science, it is incumbent upon scientists to be aware of best practices in order to most effectively tell the story of their data.

The Bigger Picture

Visuals are an increasingly important form of science communication, yet many scientists are not well trained in design principles for effective messaging. Despite challenges, many visuals can be improved by taking some simple steps before, during, and after their creation. This article presents some sequential principles that are designed to improve visual messages created by scientists.

Many scientific visuals are not as effective as they could be because scientists often lack basic design principles. This article reviews the importance of effective data visualization and presents ten principles that scientists can use as guidance in developing effective visual messages.

Introduction

Visual learning is one of the primary forms of interpreting information, which has historically combined images such as charts and graphs (see Box 1 ) with reading text. 1 However, developments on learning styles have suggested splitting up the visual learning modality in order to recognize the distinction between text and images. 2 Technology has also enhanced visual presentation, in terms of the ability to quickly create complex visual information while also cheaply distributing it via digital means (compared with paper, ink, and physical distribution). Visual information has also increased in scientific literature. In addition to the fact that figures are commonplace in scientific publications, many journals now require graphical abstracts 3 or might tweet figures to advertise an article. Dating back to the 1970s when computer-generated graphics began, 4 papers represented by an image on the journal cover have been cited more frequently than papers without a cover image. 5

Regarding terminology, the terms graph , plot , chart , image , figure , and data visual(ization) are often used interchangeably, although they may have different meanings in different instances. Graph , plot , and chart often refer to the display of data, data summaries, and models, while image suggests a picture. Figure is a general term but is commonly used to refer to visual elements, such as plots, in a scientific work. A visual , or data visualization , is a newer and ostensibly more inclusive term to describe everything from figures to infographics. Here, I adopt common terminology, such as bar plot, while also attempting to use the terms figure and data visualization for general reference.

There are numerous advantages to quickly and effectively conveying scientific information; however, scientists often lack the design principles or technical skills to generate effective visuals. Going back several decades, Cleveland 6 found that 30% of graphs in the journal Science had at least one type of error. Several other studies have documented widespread errors or inefficiencies in scientific figures. 7 , 8 , 9 In fact, the increasing menu of visualization options can sometimes lead to poor fits between information and its presentation. These poor fits can even have the unintended consequence of confusing the readers and setting them back in their understanding of the material. While objective errors in graphs are hopefully in the minority of scientific works, what might be more common is suboptimal figure design, which takes place when a design element may not be objectively wrong but is ineffective to the point of limiting information transfer.

Effective figures suggest an understanding and interpretation of data; ineffective figures suggest the opposite. Although the field of data visualization has grown in recent years, the process of displaying information cannot—and perhaps should not—be fully mechanized. Much like statistical analyses often require expert opinions on top of best practices, figures also require choice despite well-documented recommendations. In other words, there may not be a singular best version of a given figure. Rather, there may be multiple effective versions of displaying a single piece of information, and it is the figure maker's job to weigh the advantages and disadvantages of each. Fortunately, there are numerous principles from which decisions can be made, and ultimately design is choice. 7

The data visualization literature includes many great resources. While several resources are targeted at developing design proficiency, such as the series of columns run by Nature Communications , 10 Wilkinson's The Grammar of Graphics 11 presents a unique technical interpretation of the structure of graphics. Wilkinson breaks down the notion of a graphic into its constituent parts—e.g., the data, scales, coordinates, geometries, aesthetics—much like conventional grammar breaks down a sentence into nouns, verbs, punctuation, and other elements of writing. The popularity and utility of this approach has been implemented in a number of software packages, including the popular ggplot2 package 12 currently available in R. 13 (Although the grammar of graphics approach is not explicitly adopted here, the term geometry is used consistently with Wilkinson to refer to different geometrical representations, whereas the term aesthetics is not used consistently with the grammar of graphics and is used simply to describe something that is visually appealing and effective.) By understanding basic visual design principles and their implementation, many figure authors may find new ways to emphasize and convey their information.

The Ten Principles

Principle #1 diagram first.

The first principle is perhaps the least technical but very important: before you make a visual, prioritize the information you want to share, envision it, and design it. Although this seems obvious, the larger point here is to focus on the information and message first, before you engage with software that in some way starts to limit or bias your visual tools. In other words, don't necessarily think of the geometries (dots, lines) you will eventually use, but think about the core information that needs to be conveyed and what about that information is going to make your point(s). Is your visual objective to show a comparison? A ranking? A composition? This step can be done mentally, or with a pen and paper for maximum freedom of thought. In parallel to this approach, it can be a good idea to save figures you come across in scientific literature that you identify as particularly effective. These are not just inspiration and evidence of what is possible, but will help you develop an eye for detail and technical skills that can be applied to your own figures.

Principle #2 Use the Right Software

Effective visuals typically require good command of one or more software. In other words, it might be unrealistic to expect complex, technical, and effective figures if you are using a simple spreadsheet program or some other software that is not designed to make complex, technical, and effective figures. Recognize that you might need to learn a new software—or expand your knowledge of a software you already know. While highly effective and aesthetically pleasing figures can be made quickly and simply, this may still represent a challenge to some. However, figure making is a method like anything else, and in order to do it, new methodologies may need to be learned. You would not expect to improve a field or lab method without changing something or learning something new. Data visualization is the same, with the added benefit that most software is readily available, inexpensive, or free, and many come with large online help resources. This article does not promote any specific software, and readers are encouraged to reference other work 14 for an overview of software resources.

Principle #3 Use an Effective Geometry and Show Data

Geometries are the shapes and features that are often synonymous with a type of figure; for example, the bar geometry creates a bar plot. While geometries might be the defining visual element of a figure, it can be tempting to jump directly from a dataset to pairing it with one of a small number of well-known geometries. Some of this thinking is likely to naturally happen. However, geometries are representations of the data in different forms, and often there may be more than one geometry to consider. Underlying all your decisions about geometries should be the data-ink ratio, 7 which is the ratio of ink used on data compared with overall ink used in a figure. High data-ink ratios are the best, and you might be surprised to find how much non-data-ink you use and how much of that can be removed.

Most geometries fall into categories: amounts (or comparisons), compositions (or proportions), distributions , or relationships . Although seemingly straightforward, one geometry may work in more than one category, in addition to the fact that one dataset may be visualized with more than one geometry (sometimes even in the same figure). Excellent resources exist on detailed approaches to selecting your geometry, 15 and this article only highlights some of the more common geometries and their applications.

Amounts or comparisons are often displayed with a bar plot ( Figure 1 A), although numerous other options exist, including Cleveland dot plots and even heatmaps ( Figure 1 F). Bar plots are among the most common geometry, along with lines, 9 although bar plots are noted for their very low data density 16 (i.e., low data-ink ratio). Geometries for amounts should only be used when the data do not have distributional information or uncertainty associated with them. A good use of a bar plot might be to show counts of something, while poor use of a bar plot might be to show group means. Numerous studies have discussed inappropriate uses of bar plots, 9 , 17 noting that “because the bars always start at zero, they can be misleading: for example, part of the range covered by the bar might have never been observed in the sample.” 17 Despite the numerous reports on incorrect usage, bar plots remain one of the most common problems in data visualization.

Examples of Visual Designs

(A) Clustered bar plots are effective at showing units within a group (A–C) when the data are amounts.

(B) Histograms are effective at showing the distribution of data, which in this case is a random draw of values from a Poisson distribution and which use a sequential color scheme that emphasizes the mean as red and values farther from the mean as yellow.

(C) Scatterplot where the black circles represent the data.

(D) Logistic regression where the blue line represents the fitted model, the gray shaded region represents the confidence interval for the fitted model, and the dark-gray dots represent the jittered data.

(E) Box plot showing (simulated) ages of respondents grouped by their answer to a question, with gray dots representing the raw data used in the box plot. The divergent colors emphasize the differences in values. For each box plot, the box represents the interquartile range (IQR), the thick black line represents the median value, and the whiskers extend to 1.5 times the IQR. Outliers are represented by the data.

(F) Heatmap of simulated visibility readings in four lakes over 5 months. The green colors represent lower visibility and the blue colors represent greater visibility. The white numbers in the cells are the average visibility measures (in meters).

(G) Density plot of simulated temperatures by season, where each season is presented as a small multiple within the larger figure.

For all figures the data were simulated, and any examples are fictitious.

Compositions or proportions may take a wide range of geometries. Although the traditional pie chart is one option, the pie geometry has fallen out of favor among some 18 due to the inherent difficulties in making visual comparisons. Although there may be some applications for a pie chart, stacked or clustered bar plots ( Figure 1 A), stacked density plots, mosaic plots, and treemaps offer alternatives.

Geometries for distributions are an often underused class of visuals that demonstrate high data density. The most common geometry for distributional information is the box plot 19 ( Figure 1 E), which shows five types of information in one object. Although more common in exploratory analyses than in final reports, the histogram ( Figure 1 B) is another robust geometry that can reveal information about data. Violin plots and density plots ( Figure 1 G) are other common distributional geometries, although many less-common options exist.

Relationships are the final category of visuals covered here, and they are often the workhorse of geometries because they include the popular scatterplot ( Figures 1 C and 1D) and other presentations of x - and y -coordinate data. The basic scatterplot remains very effective, and layering information by modifying point symbols, size, and color are good ways to highlight additional messages without taking away from the scatterplot. It is worth mentioning here that scatterplots often develop into line geometries ( Figure 1 D), and while this can be a good thing, presenting raw data and inferential statistical models are two different messages that need to be distinguished (see Data and Models Are Different Things ).

Finally, it is almost always recommended to show the data. 7 Even if a geometry might be the focus of the figure, data can usually be added and displayed in a way that does not detract from the geometry but instead provides the context for the geometry (e.g., Figures 1 D and 1E). The data are often at the core of the message, yet in figures the data are often ignored on account of their simplicity.

Principle #4 Colors Always Mean Something

The use of color in visualization can be incredibly powerful, and there is rarely a reason not to use color. Even if authors do not wish to pay for color figures in print, most journals still permit free color figures in digital formats. In a large study 20 of what makes visualizations memorable, colorful visualizations were reported as having a higher memorability score, and that seven or more colors are best. Although some of the visuals in this study were photographs, other studies 21 also document the effectiveness of colors.

In today's digital environment, color is cheap. This is overwhelmingly a good thing, but also comes with the risk of colors being applied without intention. Black-and-white visuals were more accepted decades ago when hard copies of papers were more common and color printing represented a large cost. Now, however, the vast majority of readers view scientific papers on an electronic screen where color is free. For those who still print documents, color printing can be done relatively cheaply in comparison with some years ago.

Color represents information, whether in a direct and obvious way, or in an indirect and subtle way. A direct example of using color may be in maps where water is blue and land is green or brown. However, the vast majority of (non-mapping) visualizations use color in one of three schemes: sequential , diverging , or qualitative . Sequential color schemes are those that range from light to dark typically in one or two (related) hues and are often applied to convey increasing values for increasing darkness ( Figures 1 B and 1F). Diverging color schemes are those that have two sequential schemes that represent two extremes, often with a white or neutral color in the middle ( Figure 1 E). A classic example of a diverging color scheme is the red to blue hues applied to jurisdictions in order to show voting preference in a two-party political system. Finally, qualitative color schemes are found when the intensity of the color is not of primary importance, but rather the objective is to use different and otherwise unrelated colors to convey qualitative group differences ( Figures 1 A and 1G).

While it is recommended to use color and capture the power that colors convey, there exist some technical recommendations. First, it is always recommended to design color figures that work effectively in both color and black-and-white formats ( Figures 1 B and 1F). In other words, whenever possible, use color that can be converted to an effective grayscale such that no information is lost in the conversion. Along with this approach, colors can be combined with symbols, line types, and other design elements to share the same information that the color was sharing. It is also good practice to use color schemes that are effective for colorblind readers ( Figures 1 A and 1E). Excellent resources, such as ColorBrewer, 22 exist to help in selecting color schemes based on colorblind criteria. Finally, color transparency is another powerful tool, much like a volume knob for color ( Figures 1 D and 1E). Not all colors have to be used at full value, and when not part of a sequential or diverging color scheme—and especially when a figure has more than one colored geometry—it can be very effective to increase the transparency such that the information of the color is retained but it is not visually overwhelming or outcompeting other design elements. Color will often be the first visual information a reader gets, and with this knowledge color should be strategically used to amplify your visual message.

Principle #5 Include Uncertainty

Not only is uncertainty an inherent part of understanding most systems, failure to include uncertainty in a visual can be misleading. There exist two primary challenges with including uncertainty in visuals: failure to include uncertainty and misrepresentation (or misinterpretation) of uncertainty.

Uncertainty is often not included in figures and, therefore, part of the statistical message is left out—possibly calling into question other parts of the statistical message, such as inference on the mean. Including uncertainty is typically easy in most software programs, and can take the form of common geometries such as error bars and shaded intervals (polygons), among other features. 15 Another way to approach visualizing uncertainty is whether it is included implicitly into the existing geometries, such as in a box plot ( Figure 1 E) or distribution ( Figures 1 B and 1G), or whether it is included explicitly as an additional geometry, such as an error bar or shaded region ( Figure 1 D).

Representing uncertainty is often a challenge. 23 Standard deviation, standard error, confidence intervals, and credible intervals are all common metrics of uncertainty, but each represents a different measure. Expressing uncertainty requires that readers be familiar with metrics of uncertainty and their interpretation; however, it is also the responsibility of the figure author to adopt the most appropriate measure of uncertainty. For instance, standard deviation is based on the spread of the data and therefore shares information about the entire population, including the range in which we might expect new values. On the other hand, standard error is a measure of the uncertainty in the mean (or some other estimate) and is strongly influenced by sample size—namely, standard error decreases with increasing sample size. Confidence intervals are primarily for displaying the reliability of a measurement. Credible intervals, almost exclusively associated with Bayesian methods, are typically built off distributions and have probabilistic interpretations.

Expressing uncertainty is important, but it is also important to interpret the correct message. Krzywinski and Altman 23 directly address a common misconception: “a gap between (error) bars does not ensure significance, nor does overlap rule it out—it depends on the type of bar.” This is a good reminder to be very clear not only in stating what type of uncertainty you are sharing, but what the interpretation is. Others 16 even go so far as to recommend that standard error not be used because it does not provide clear information about standard errors of differences among means. One recommendation to go along with expressing uncertainty is, if possible, to show the data (see Use an Effective Geometry and Show Data ). Particularly when the sample size is low, showing a reader where the data occur can help avoid misinterpretations of uncertainty.

Principle #6 Panel, when Possible (Small Multiples)

A particularly effective visual approach is to repeat a figure to highlight differences. This approach is often called small multiples , 7 and the technique may be referred to as paneling or faceting ( Figure 1 G). The strategy behind small multiples is that because many of the design elements are the same—for example, the axes, axes scales, and geometry are often the same—the differences in the data are easier to show. In other words, each panel represents a change in one variable, which is commonly a time step, a group, or some other factor. The objective of small multiples is to make the data inevitably comparable, 7 and effective small multiples always accomplish these comparisons.

Principle #7 Data and Models Are Different Things

Plotted information typically takes the form of raw data (e.g., scatterplot), summarized data (e.g., box plot), or an inferential statistic (e.g., fitted regression line; Figure 1 D). Raw data and summarized data are often relatively straightforward; however, a plotted model may require more explanation for a reader to be able to fully reproduce the work. Certainly any model in a study should be reported in a complete way that ensures reproducibility. However, any visual of a model should be explained in the figure caption or referenced elsewhere in the document so that a reader can find the complete details on what the model visual is representing. Although it happens, it is not acceptable practice to show a fitted model or other model results in a figure if the reader cannot backtrack the model details. Simply because a model geometry can be added to a figure does not mean that it should be.

Principle #8 Simple Visuals, Detailed Captions

As important as it is to use high data-ink ratios, it is equally important to have detailed captions that fully explain everything in the figure. A study of figures in the Journal of American Medicine 8 found that more than one-third of graphs were not self-explanatory. Captions should be standalone, which means that if the figure and caption were looked at independent from the rest of the study, the major point(s) could still be understood. Obviously not all figures can be completely standalone, as some statistical models and other procedures require more than a caption as explanation. However, the principle remains that captions should do all they can to explain the visualization and representations used. Captions should explain any geometries used; for instance, even in a simple scatterplot it should be stated that the black dots represent the data ( Figures 1 C–1E). Box plots also require descriptions of their geometry—it might be assumed what the features of a box plot are, yet not all box plot symbols are universal.

Principle #9 Consider an Infographic

It is unclear where a figure ends and an infographic begins; however, it is fair to say that figures tend to be focused on representing data and models, whereas infographics typically incorporate text, images, and other diagrammatic elements. Although it is not recommended to convert all figures to infographics, infographics were found 20 to have the highest memorability score and that diagrams outperformed points, bars, lines, and tables in terms of memorability. Scientists might improve their overall information transfer if they consider an infographic where blending different pieces of information could be effective. Also, an infographic of a study might be more effective outside of a peer-reviewed publication and in an oral or poster presentation where a visual needs to include more elements of the study but with less technical information.

Even if infographics are not adopted in most cases, technical visuals often still benefit from some text or other annotations. 16 Tufte's works 7 , 24 provide great examples of bringing together textual, visual, and quantitative information into effective visualizations. However, as figures move in the direction of infographics, it remains important to keep chart junk and other non-essential visual elements out of the design.

Principle #10 Get an Opinion

Although there may be principles and theories about effective data visualization, the reality is that the most effective visuals are the ones with which readers connect. Therefore, figure authors are encouraged to seek external reviews of their figures. So often when writing a study, the figures are quickly made, and even if thoughtfully made they are not subject to objective, outside review. Having one or more colleagues or people external to the study review figures will often provide useful feedback on what readers perceive, and therefore what is effective or ineffective in a visual. It is also recommended to have outside colleagues review only the figures. Not only might this please your colleague reviewers (because figure reviews require substantially less time than full document reviews), but it also allows them to provide feedback purely on the figures as they will not have the document text to fill in any uncertainties left by the visuals.

What About Tables?

Although often not included as data visualization, tables can be a powerful and effective way to show data. Like other visuals, tables are a type of hybrid visual—they typically only include alphanumeric information and no geometries (or other visual elements), so they are not classically a visual. However, tables are also not text in the same way a paragraph or description is text. Rather, tables are often summarized values or information, and are effective if the goal is to reference exact numbers. However, the interest in numerical results in the form of a study typically lies in comparisons and not absolute numbers. Gelman et al. 25 suggested that well-designed graphs were superior to tables. Similarly, Spence and Lewandowsky 26 compared pie charts, bar graphs, and tables and found a clear advantage for graphical displays over tabulations. Because tables are best suited for looking up specific information while graphs are better for perceiving trends and making comparisons and predictions, it is recommended that visuals are used before tables. Despite the reluctance to recommend tables, tables may benefit from digital formats. In other words, while tables may be less effective than figures in many cases, this does not mean tables are ineffective or do not share specific information that cannot always be displayed in a visual. Therefore, it is recommended to consider creating tables as supplementary or appendix information that does not go into the main document (alongside the figures), but which is still very easily accessed electronically for those interested in numerical specifics.

Conclusions

While many of the elements of peer-reviewed literature have remained constant over time, some elements are changing. For example, most articles now have more authors than in previous decades, and a much larger menu of journals creates a diversity of article lengths and other requirements. Despite these changes, the demand for visual representations of data and results remains high, as exemplified by graphical abstracts, overview figures, and infographics. Similarly, we now operate with more software than ever before, creating many choices and opportunities to customize scientific visualizations. However, as the demand for, and software to create, visualizations have both increased, there is not always adequate training among scientists and authors in terms of optimizing the visual for the message.

Figures are not just a scientific side dish but can be a critical point along the scientific process—a point at which the figure maker demonstrates their knowledge and communication of the data and results, and often one of the first stopping points for new readers of the information. The reality for the vast majority of figures is that you need to make your point in a few seconds. The longer someone looks at a figure and doesn't understand the message, the more likely they are to gain nothing from the figure and possibly even lose some understanding of your larger work. Following a set of guidelines and recommendations—summarized here and building on others—can help to build robust visuals that avoid many common pitfalls of ineffective figures ( Figure 2 ).

Overview of the Principles Presented in This Article

The two principles in yellow (bottom) are those that occur first, during the figure design phase. The six principles in green (middle) are generally considerations and decisions while making a figure. The two principles in blue (top) are final steps often considered after a figure has been drafted. While the general flow of the principles follows from bottom to top, there is no specific or required order, and the development of individual figures may require more or less consideration of different principles in a unique order.

All scientists seek to share their message as effectively as possible, and a better understanding of figure design and representation is undoubtedly a step toward better information dissemination and fewer errors in interpretation. Right now, much of the responsibility for effective figures lies with the authors, and learning best practices from literature, workshops, and other resources should be undertaken. Along with authors, journals play a gatekeeper role in figure quality. Journal editorial teams are in a position to adopt recommendations for more effective figures (and reject ineffective figures) and then translate those recommendations into submission requirements. However, due to the qualitative nature of design elements, it is difficult to imagine strict visual guidelines being enforced across scientific sectors. In the absence of such guidelines and with seemingly endless design choices available to figure authors, it remains important that a set of aesthetic criteria emerge to guide the efficient conveyance of visual information.

Acknowledgments

Thanks go to the numerous students with whom I have had fun, creative, and productive conversations about displaying information. Danielle DiIullo was extremely helpful in technical advice on software. Finally, Ron McKernan provided guidance on several principles.

Author Contributions

S.R.M. conceived the review topic, conducted the review, developed the principles, and wrote the manuscript.

Steve Midway is an assistant professor in the Department of Oceanography and Coastal Sciences at Louisiana State University. His work broadly lies in fisheries ecology and how sound science can be applied to management and conservation issues. He teaches a number of quantitative courses in ecology, all of which include data visualization.

• Business Essentials
• Leadership & Management
• Credential of Leadership, Impact, and Management in Business (CLIMB)
• Entrepreneurship & Innovation
• Digital Transformation
• Finance & Accounting
• Business in Society
• For Organizations
• Support Portal
• Media Coverage
• Founding Donors
• Leadership Team

• Harvard Business School →
• HBS Online →
• Business Insights →

Business Insights

Harvard Business School Online's Business Insights Blog provides the career insights you need to achieve your goals and gain confidence in your business skills.

• Career Development
• Communication
• Decision-Making
• Earning Your MBA
• Negotiation
• News & Events
• Productivity
• Staff Spotlight
• Student Profiles
• Work-Life Balance
• AI Essentials for Business
• Alternative Investments
• Business Analytics
• Business Strategy
• Business and Climate Change
• Creating Brand Value
• Design Thinking and Innovation
• Digital Marketing Strategy
• Disruptive Strategy
• Economics for Managers
• Entrepreneurship Essentials
• Financial Accounting
• Global Business
• Launching Tech Ventures
• Leadership Principles
• Leadership, Ethics, and Corporate Accountability
• Leading Change and Organizational Renewal
• Leading with Finance
• Management Essentials
• Negotiation Mastery
• Organizational Leadership
• Power and Influence for Positive Impact
• Strategy Execution
• Sustainable Business Strategy
• Sustainable Investing
• Winning with Digital Platforms

17 Data Visualization Techniques All Professionals Should Know

• 17 Sep 2019

There’s a growing demand for business analytics and data expertise in the workforce. But you don’t need to be a professional analyst to benefit from data-related skills.

Becoming skilled at common data visualization techniques can help you reap the rewards of data-driven decision-making , including increased confidence and potential cost savings. Learning how to effectively visualize data could be the first step toward using data analytics and data science to your advantage to add value to your organization.

Several data visualization techniques can help you become more effective in your role. Here are 17 essential data visualization techniques all professionals should know, as well as tips to help you effectively present your data.

Access your free e-book today.

What Is Data Visualization?

Data visualization is the process of creating graphical representations of information. This process helps the presenter communicate data in a way that’s easy for the viewer to interpret and draw conclusions.

There are many different techniques and tools you can leverage to visualize data, so you want to know which ones to use and when. Here are some of the most important data visualization techniques all professionals should know.

Data Visualization Techniques

The type of data visualization technique you leverage will vary based on the type of data you’re working with, in addition to the story you’re telling with your data .

Here are some important data visualization techniques to know:

• Gantt Chart
• Box and Whisker Plot
• Waterfall Chart
• Scatter Plot
• Pictogram Chart
• Highlight Table
• Bullet Graph
• Choropleth Map
• Network Diagram
• Correlation Matrices

1. Pie Chart

Pie charts are one of the most common and basic data visualization techniques, used across a wide range of applications. Pie charts are ideal for illustrating proportions, or part-to-whole comparisons.

Because pie charts are relatively simple and easy to read, they’re best suited for audiences who might be unfamiliar with the information or are only interested in the key takeaways. For viewers who require a more thorough explanation of the data, pie charts fall short in their ability to display complex information.

2. Bar Chart

The classic bar chart , or bar graph, is another common and easy-to-use method of data visualization. In this type of visualization, one axis of the chart shows the categories being compared, and the other, a measured value. The length of the bar indicates how each group measures according to the value.

One drawback is that labeling and clarity can become problematic when there are too many categories included. Like pie charts, they can also be too simple for more complex data sets.

3. Histogram

Unlike bar charts, histograms illustrate the distribution of data over a continuous interval or defined period. These visualizations are helpful in identifying where values are concentrated, as well as where there are gaps or unusual values.

Histograms are especially useful for showing the frequency of a particular occurrence. For instance, if you’d like to show how many clicks your website received each day over the last week, you can use a histogram. From this visualization, you can quickly determine which days your website saw the greatest and fewest number of clicks.

4. Gantt Chart

Gantt charts are particularly common in project management, as they’re useful in illustrating a project timeline or progression of tasks. In this type of chart, tasks to be performed are listed on the vertical axis and time intervals on the horizontal axis. Horizontal bars in the body of the chart represent the duration of each activity.

Utilizing Gantt charts to display timelines can be incredibly helpful, and enable team members to keep track of every aspect of a project. Even if you’re not a project management professional, familiarizing yourself with Gantt charts can help you stay organized.

5. Heat Map

A heat map is a type of visualization used to show differences in data through variations in color. These charts use color to communicate values in a way that makes it easy for the viewer to quickly identify trends. Having a clear legend is necessary in order for a user to successfully read and interpret a heatmap.

There are many possible applications of heat maps. For example, if you want to analyze which time of day a retail store makes the most sales, you can use a heat map that shows the day of the week on the vertical axis and time of day on the horizontal axis. Then, by shading in the matrix with colors that correspond to the number of sales at each time of day, you can identify trends in the data that allow you to determine the exact times your store experiences the most sales.

6. A Box and Whisker Plot

A box and whisker plot , or box plot, provides a visual summary of data through its quartiles. First, a box is drawn from the first quartile to the third of the data set. A line within the box represents the median. “Whiskers,” or lines, are then drawn extending from the box to the minimum (lower extreme) and maximum (upper extreme). Outliers are represented by individual points that are in-line with the whiskers.

This type of chart is helpful in quickly identifying whether or not the data is symmetrical or skewed, as well as providing a visual summary of the data set that can be easily interpreted.

7. Waterfall Chart

A waterfall chart is a visual representation that illustrates how a value changes as it’s influenced by different factors, such as time. The main goal of this chart is to show the viewer how a value has grown or declined over a defined period. For example, waterfall charts are popular for showing spending or earnings over time.

8. Area Chart

An area chart , or area graph, is a variation on a basic line graph in which the area underneath the line is shaded to represent the total value of each data point. When several data series must be compared on the same graph, stacked area charts are used.

This method of data visualization is useful for showing changes in one or more quantities over time, as well as showing how each quantity combines to make up the whole. Stacked area charts are effective in showing part-to-whole comparisons.

9. Scatter Plot

Another technique commonly used to display data is a scatter plot . A scatter plot displays data for two variables as represented by points plotted against the horizontal and vertical axis. This type of data visualization is useful in illustrating the relationships that exist between variables and can be used to identify trends or correlations in data.

Scatter plots are most effective for fairly large data sets, since it’s often easier to identify trends when there are more data points present. Additionally, the closer the data points are grouped together, the stronger the correlation or trend tends to be.

10. Pictogram Chart

Pictogram charts , or pictograph charts, are particularly useful for presenting simple data in a more visual and engaging way. These charts use icons to visualize data, with each icon representing a different value or category. For example, data about time might be represented by icons of clocks or watches. Each icon can correspond to either a single unit or a set number of units (for example, each icon represents 100 units).

In addition to making the data more engaging, pictogram charts are helpful in situations where language or cultural differences might be a barrier to the audience’s understanding of the data.

11. Timeline

Timelines are the most effective way to visualize a sequence of events in chronological order. They’re typically linear, with key events outlined along the axis. Timelines are used to communicate time-related information and display historical data.

Timelines allow you to highlight the most important events that occurred, or need to occur in the future, and make it easy for the viewer to identify any patterns appearing within the selected time period. While timelines are often relatively simple linear visualizations, they can be made more visually appealing by adding images, colors, fonts, and decorative shapes.

12. Highlight Table

A highlight table is a more engaging alternative to traditional tables. By highlighting cells in the table with color, you can make it easier for viewers to quickly spot trends and patterns in the data. These visualizations are useful for comparing categorical data.

Depending on the data visualization tool you’re using, you may be able to add conditional formatting rules to the table that automatically color cells that meet specified conditions. For instance, when using a highlight table to visualize a company’s sales data, you may color cells red if the sales data is below the goal, or green if sales were above the goal. Unlike a heat map, the colors in a highlight table are discrete and represent a single meaning or value.

13. Bullet Graph

A bullet graph is a variation of a bar graph that can act as an alternative to dashboard gauges to represent performance data. The main use for a bullet graph is to inform the viewer of how a business is performing in comparison to benchmarks that are in place for key business metrics.

In a bullet graph, the darker horizontal bar in the middle of the chart represents the actual value, while the vertical line represents a comparative value, or target. If the horizontal bar passes the vertical line, the target for that metric has been surpassed. Additionally, the segmented colored sections behind the horizontal bar represent range scores, such as “poor,” “fair,” or “good.”

14. Choropleth Maps

A choropleth map uses color, shading, and other patterns to visualize numerical values across geographic regions. These visualizations use a progression of color (or shading) on a spectrum to distinguish high values from low.

Choropleth maps allow viewers to see how a variable changes from one region to the next. A potential downside to this type of visualization is that the exact numerical values aren’t easily accessible because the colors represent a range of values. Some data visualization tools, however, allow you to add interactivity to your map so the exact values are accessible.

15. Word Cloud

A word cloud , or tag cloud, is a visual representation of text data in which the size of the word is proportional to its frequency. The more often a specific word appears in a dataset, the larger it appears in the visualization. In addition to size, words often appear bolder or follow a specific color scheme depending on their frequency.

Word clouds are often used on websites and blogs to identify significant keywords and compare differences in textual data between two sources. They are also useful when analyzing qualitative datasets, such as the specific words consumers used to describe a product.

16. Network Diagram

Network diagrams are a type of data visualization that represent relationships between qualitative data points. These visualizations are composed of nodes and links, also called edges. Nodes are singular data points that are connected to other nodes through edges, which show the relationship between multiple nodes.

There are many use cases for network diagrams, including depicting social networks, highlighting the relationships between employees at an organization, or visualizing product sales across geographic regions.

17. Correlation Matrix

A correlation matrix is a table that shows correlation coefficients between variables. Each cell represents the relationship between two variables, and a color scale is used to communicate whether the variables are correlated and to what extent.

Correlation matrices are useful to summarize and find patterns in large data sets. In business, a correlation matrix might be used to analyze how different data points about a specific product might be related, such as price, advertising spend, launch date, etc.

Other Data Visualization Options

While the examples listed above are some of the most commonly used techniques, there are many other ways you can visualize data to become a more effective communicator. Some other data visualization options include:

• Bubble clouds
• Circle views
• Dendrograms
• Dot distribution maps
• Open-high-low-close charts
• Polar areas
• Radial trees
• Ring Charts
• Sankey diagram
• Span charts
• Streamgraphs
• Wedge stack graphs
• Violin plots

Tips For Creating Effective Visualizations

Creating effective data visualizations requires more than just knowing how to choose the best technique for your needs. There are several considerations you should take into account to maximize your effectiveness when it comes to presenting data.

Related : What to Keep in Mind When Creating Data Visualizations in Excel

One of the most important steps is to evaluate your audience. For example, if you’re presenting financial data to a team that works in an unrelated department, you’ll want to choose a fairly simple illustration. On the other hand, if you’re presenting financial data to a team of finance experts, it’s likely you can safely include more complex information.

Another helpful tip is to avoid unnecessary distractions. Although visual elements like animation can be a great way to add interest, they can also distract from the key points the illustration is trying to convey and hinder the viewer’s ability to quickly understand the information.

Finally, be mindful of the colors you utilize, as well as your overall design. While it’s important that your graphs or charts are visually appealing, there are more practical reasons you might choose one color palette over another. For instance, using low contrast colors can make it difficult for your audience to discern differences between data points. Using colors that are too bold, however, can make the illustration overwhelming or distracting for the viewer.

Related : Bad Data Visualization: 5 Examples of Misleading Data

Visuals to Interpret and Share Information

No matter your role or title within an organization, data visualization is a skill that’s important for all professionals. Being able to effectively present complex data through easy-to-understand visual representations is invaluable when it comes to communicating information with members both inside and outside your business.

There’s no shortage in how data visualization can be applied in the real world. Data is playing an increasingly important role in the marketplace today, and data literacy is the first step in understanding how analytics can be used in business.

Are you interested in improving your analytical skills? Learn more about Business Analytics , our eight-week online course that can help you use data to generate insights and tackle business decisions.

This post was updated on January 20, 2022. It was originally published on September 17, 2019.

About the Author

• school Campus Bookshelves
• menu_book Bookshelves
• perm_media Learning Objects
• login Login
• how_to_reg Request Instructor Account
• hub Instructor Commons

Margin Size

• Download Page (PDF)
• Download Full Book (PDF)
• Periodic Table
• Physics Constants
• Scientific Calculator
• Reference & Cite
• Tools expand_more
• Readability

selected template will load here

This action is not available.

2: Graphical Descriptions of Data

• Last updated
• Save as PDF
• Page ID 5170

• Kathryn Kozak
• Coconino Community College

\( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

\( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

\( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

\( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

\( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

\( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

\( \newcommand{\Span}{\mathrm{span}}\)

\( \newcommand{\id}{\mathrm{id}}\)

\( \newcommand{\kernel}{\mathrm{null}\,}\)

\( \newcommand{\range}{\mathrm{range}\,}\)

\( \newcommand{\RealPart}{\mathrm{Re}}\)

\( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

\( \newcommand{\Argument}{\mathrm{Arg}}\)

\( \newcommand{\norm}[1]{\| #1 \|}\)

\( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

\( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

\( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

\( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

\( \newcommand{\vectorC}[1]{\textbf{#1}} \)

\( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

\( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

\( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

In chapter 1, you were introduced to the concepts of population, which again is a collection of all the measurements from the individuals of interest. Remember, in most cases you can’t collect the entire population, so you have to take a sample. Thus, you collect data either through a sample or a census. Now you have a large number of data values. What can you do with them? No one likes to look at just a set of numbers. One thing is to organize the data into a table or graph. Ultimately though, you want to be able to use that graph to interpret the data, to describe the distribution of the data set, and to explore different characteristics of the data. The characteristics that will be discussed in this chapter and the next chapter are:

• Center: middle of the data set, also known as the average.
• Variation: how much the data varies.
• Distribution: shape of the data (symmetric, uniform, or skewed).
• Qualitative data: analysis of the data
• Outliers: data values that are far from the majority of the data.
• Time: changing characteristics of the data over time.

This chapter will focus mostly on using the graphs to understand aspects of the data, and not as much on how to create the graphs. There is technology that will create most of the graphs, though it is important for you to understand the basics of how to create them.

• 2.1: Qualitative Data Remember, qualitative data are words describing a characteristic of the individual. There are several different graphs that are used for qualitative data. These graphs include bar graphs, Pareto charts, and pie charts.
• 2.2: Quantitative Data
• 2.3: Other Graphical Representations of Data

21 Best Data Visualization Types: Examples of Graphs and Charts Uses

Those who master different data visualization types and techniques (such as graphs, charts, diagrams, and maps) are gaining the most value from data.

Why? Because they can analyze data and make the best-informed decisions.

Whether you work in business, marketing, sales, statistics, or anything else, you need data visualization techniques and skills.

Graphs and charts make data much more understandable for the human brain.

On this page:

• What are data visualization techniques? Definition, benefits, and importance.
• 21 top data visualization types. Examples of graphs and charts with an explanation.
• When to use different data visualization graphs, charts, diagrams, and maps?
• How to create effective data visualization?
• 10 best data visualization tools for creating compelling graphs and charts.

What Are Data V isualization T echniques? Definition And Benefits.

Data visualization techniques are visual elements (like a line graph, bar chart, pie chart, etc.) that are used to represent information and data.

Big data hides a story (like a trend and pattern).

By using different types of graphs and charts, you can easily see and understand trends, outliers, and patterns in data.

They allow you to get the meaning behind figures and numbers and make important decisions or conclusions.

Data visualization techniques can benefit you in several ways to improve decision making.

Key benefits:

• Data is processed faster Visualized data is processed faster than text and table reports. Our brains can easily recognize images and make sense of them.
• Better analysis Help you analyze better reports in sales, marketing, product management, etc. Thus, you can focus on the areas that require attention such as areas for improvement, errors or high-performing spots.
• Faster decision making Businesses who can understand and quickly act on their data will gain more competitive advantages because they can make informed decisions sooner than the competitors.
• You can easily identify relationships, trends, patterns Visuals are especially helpful when you’re trying to find trends, patterns or relationships among hundreds or thousands of variables. Data is presented in ways that are easy to consume while allowing exploration. Therefore, people across all levels in your company can dive deeper into data and use the insights for faster and smarter decisions.
• No need for coding or data science skills There are many advanced tools that allow you to create beautiful charts and graphs without the need for data scientist skills . Thereby, a broad range of business users can create, visually explore, and discover important insights into data.

How Do Data Visualization Techniques work?

Data visualization techniques convert tons of data into meaningful visuals using software tools.

The tools can operate various types of data and present them in visual elements like charts, diagrams, and maps.

They allow you to easily analyze massive amounts of information, discover trends and patterns in data and then make data-driven decisions .

Why data visualization is very important for any job?

Each professional industry benefits from making data easier to understand. Government, marketing, finance, sales, science, consumer goods, education, sports, and so on.

As all types of organizations become more and more data-driven, the ability to work with data isn’t a good plus, it’s essential.

Whether you’re in sales and need to present your products to prospects or a manager trying to optimize employee performance – everything is measurable and needs to be scored against different KPI s.

We need to constantly analyze and share data with our team or customers.

Having data visualization skills will allow you to understand what is happening in your company and to make the right decisions for the good of the organization.

Before start using visuals, you must know…

Data visualization is one of the most important skills for the modern-day worker.

However, it’s not enough to see your data in easily digestible visuals to get real insights and make the right decisions.

• First : to define the information you need to present
• Second: to find the best possible visual to show that information

Don’t start with “I need a bar chart/pie chart/map here. Let’s make one that looks cool” . This is how you can end up with misleading visualizations that, while beautiful, don’t help for smart decision making.

Regardless of the type of data visualization, its purpose is to help you see a pattern or trend in the data being analyzed.

The goal is not to come up with complex descriptions such as: “ A’s sales were more than B by 5.8% in 2018, and despite a sales growth of 30% in 2019, A’s sales became less than B by 6.2% in 2019. ”

A good data visualization summarizes and presents information in a way that enables you to focus on the most important points.

Let’s go through 21 data visualization types with examples, outline their features, and explain how and when to use them for the best results.

21 Best Types Of Data Visualization With Examples And Uses

1. Line Graph

The line graph is the most popular type of graph with many business applications because they show an overall trend clearly and concisely.

What is a line graph?

A line graph (also known as a line chart) is a graph used to visualize the values of something over a specified period of time.

For example, your sales department may plot the change in the number of sales your company has on hand over time.

Data points that display the values are connected by straight lines.

When to use line graphs?

• When you want to display trends.
• When you want to represent trends for different categories over the same period of time and thus to show comparison.

For example, the above line graph shows the total units of a company sales of Product A, Product B, and Product C from 2012 to 2019.

Here, you can see at a glance that the top-performing product over the years is product C, followed by Product B.

2. Bar Chart

At some point or another, you’ve interacted with a bar chart before. Bar charts are very popular data visualization types as they allow you to easily scan them for valuable insights.

And they are great for comparing several different categories of data.

What is a bar chart?

A bar chart (also called bar graph) is a chart that represents data using bars of different heights.

The bars can be two types – vertical or horizontal. It doesn’t matter which type you use.

The bar chart can easily compare the data for each variable at each moment in time.

For example, a bar chart could compare your company’s sales from this year to last year.

When to use a bar chart?

• When you need to compare several different categories.
• When you need to show how large data changes over time.

The above bar graph visualizes revenue by age group for three different product lines – A, B, and C.

You can see more granular differences between revenue for each product within each age group.

As different product lines are groups by age group, you can easily see that the group of 34-45-year-old buyers are the most valuable to your business as they are your biggest customers.

3. Column Chart

If you want to make side-by-side comparisons of different values, the column chart is your answer.

What is a column chart?

A column chart is a type of bar chart that uses vertical bars to show a comparison between categories.

If something can be counted, it can be displayed in a column chart.

Column charts work best for showing the situation at a point in time (for example, the number of products sold on a website).

Their main purpose is to draw attention to total numbers rather than the trend (trends are more suitable for a line chart).

When to use a column chart?

• When you need to show a side-by-side comparison of different values.
• When you want to emphasize the difference between values.
• When you want to highlight the total figures rather than the trends.

For example, the column chart above shows the traffic sources of a website. It illustrates direct traffic vs search traffic vs social media traffic on a series of dates.

The numbers don’t change much from day to day, so a line graph isn’t appropriate as it wouldn’t reveal anything important in terms of trends.

The important information here is the concrete number of visitors coming from different sources to the website each day.

4. Pie Chart

Pie charts are attractive data visualization types. At a high-level, they’re easy to read and used for representing relative sizes.

What is a pie chart?

A Pie Chart is a circular graph that uses “pie slices” to display relative sizes of data.

A pie chart is a perfect choice for visualizing percentages because it shows each element as part of a whole.

The entire pie represents 100 percent of a whole. The pie slices represent portions of the whole.

When to use a pie chart?

• When you want to represent the share each value has of the whole.
• When you want to show how a group is broken down into smaller pieces.

The above pie chart shows which traffic sources bring in the biggest share of total visitors.

You see that Searches is the most effective source, followed by Social Media, and then Links.

At a glance, your marketing team can spot what’s working best, helping them to concentrate their efforts to maximize the number of visitors.

5. Area Chart 

If you need to present data that depicts a time-series relationship, an area chart is a great option.

What is an area chart?

An area chart is a type of chart that represents the change in one or more quantities over time. It is similar to a line graph.

In both area charts and line graphs, data points are connected by a line to show the value of a quantity at different times. They are both good for showing trends.

However, the area chart is different from the line graph, because the area between the x-axis and the line is filled in with color. Thus, area charts give a sense of the overall volume.

Area charts emphasize a trend over time. They aren’t so focused on showing exact values.

Also, area charts are perfect for indicating the change among different data groups.

When to use an area chart?

• When you want to use multiple lines to make a comparison between groups (aka series).
• When you want to track not only the whole value but also want to understand the breakdown of that total by groups.

In the area chart above, you can see how much revenue is overlapped by cost.

Moreover, you see at once where the pink sliver of profit is at its thinnest.

Thus, you can spot where cash flow really is tightest, rather than where in the year your company simply has the most cash.

Area charts can help you with things like resource planning, financial management, defining appropriate storage space, and more.

6. Scatter Plot

The scatter plot is also among the popular data visualization types and has other names such as a scatter diagram, scatter graph, and correlation chart.

Scatter plot helps in many areas of today’s world – business, biology, social statistics, data science and etc.

What is a Scatter plot?

Scatter plot is a graph that represents a relationship between two variables . The purpose is to show how much one variable affects another.

Usually, when there is a relationship between 2 variables, the first one is called independent. The second variable is called dependent because its values depend on the first variable.

But it is also possible to have no relationship between 2 variables at all.

When to use a Scatter plot?

• When you need to observe and show relationships between two numeric variables.
• When just want to visualize the correlation between 2 large datasets without regard to time.

The above scatter plot illustrates the relationship between monthly e-commerce sales and online advertising costs of a company.

At a glance, you can see that online advertising costs affect monthly e-commerce sales.

When online advertising costs increase, e-commerce sales also increase.

Scatter plots also show if there are unexpected gaps in the data or if there are any outlier points.

7. Bubble chart

If you want to display 3 related dimensions of data in one elegant visualization, a bubble chart will help you.

What is a bubble chart?

A bubble chart is like an extension of the scatter plot used to display relationships between three variables.

The variables’ values for each point are shown by horizontal position, vertical position, and dot size.

In a bubble chart, we can make three different pairwise comparisons (X vs. Y, Y vs. Z, X vs. Z).

When to use a bubble chart?

• When you want to depict and show relationships between three variables.

The bubble chart above illustrates the relationship between 3 dimensions of data:

• Cost (X-Axis)
• Profit (Y-Axis)
• Probability of Success (%) (Bubble Size).

Bubbles are proportional to the third dimension – the probability of success. The larger the bubble, the greater the probability of success.

It is obvious that Product A has the highest probability of success.

8. Pyramid Graph

Pyramid graphs are very interesting and visually appealing graphs. Moreover, they are one of the most easy-to-read data visualization types and techniques.

What is a pyramid graph?

It is a graph in the shape of a triangle or pyramid. It is best used when you want to show some kind of hierarchy. The pyramid levels display some kind of progressive order, such as:

• More important to least important. For example, CEOs at the top and temporary employees on the bottom level.
• Specific to least specific. For example, expert fields at the top, general fields at the bottom.
• Older to newer.

When to use a pyramid graph?

• When you need to illustrate some kind of hierarchy or progressive order

Image Source: Conceptdraw

The above is a 5 Level Pyramid of information system types that is based on the hierarchy in an organization.

It shows progressive order from tacit knowledge to more basic knowledge. Executive information system at the top and transaction processing system on the bottom level.

The levels are displayed in different colors. It’s very easy to read and understand.

9. Treemaps

Treemaps also show a hierarchical structure like the pyramid graph, however in a completely different way.

What is a treemap?

Treemap is a type of data visualization technique that is used to display a hierarchical structure using nested rectangles.

Data is organized as branches and sub-branches. Treemaps display quantities for each category and sub-category via a rectangle area size.

Treemaps are a compact and space-efficient option for showing hierarchies.

They are also great at comparing the proportions between categories via their area size. Thus, they provide an instant sense of which data categories are the most important overall.

When to use a treemap?

• When you want to illustrate hierarchies and comparative value between categories and subcategories.

Image source: Power BI

For example, let’s say you work in a company that sells clothing categories: Urban, Rural, Youth, and Mix.

The above treemap depicts the sales of different clothing categories, which are then broken down by clothing manufacturers.

You see at a glance that Urban is your most successful clothing category, but that the Quibus is your most valuable clothing manufacturer, across all categories.

10. Funnel chart

Funnel charts are used to illustrate optimizations, specifically to see which stages most impact drop-off.

Illustrating the drop-offs helps to show the importance of each stage.

What is a funnel chart?

A funnel chart is a popular data visualization type that shows the flow of users through a sales or other business process.

It looks like a funnel that starts from a large head and ends in a smaller neck. The number of users at each step of the process is visualized from the funnel width as it narrows.

A funnel chart is very useful for identifying potential problem areas in the sales process.

When to use a funnel chart?

• When you need to represent stages in a sales or other business process and show the amount of revenue for each stage.

Image Source: DevExpress

This funnel chart shows the conversion rate of a website.

The conversion rate shows what percentage of all visitors completed a specific desired action (such as subscription or purchase).

The chart starts with the people that visited the website and goes through every touchpoint until the final desired action – renewal of the subscription.

You can see easily where visitors are dropping out of the process.

11. Venn Diagram 

Venn diagrams are great data visualization types for representing relationships between items and highlighting how the items are similar and different.

What is a Venn diagram?

A Venn Diagram is an illustration that shows logical relationships between two or more data groups. Typically, the Venn diagram uses circles (both overlapping and nonoverlapping).

Venn diagrams can clearly show how given items are similar and different.

Venn diagram with 2 and 3 circles are the most common types. Diagrams with a larger number of circles (5,6,7,8,10…) become extremely complicated.

When to use a Venn diagram?

• When you want to compare two or more options and see what they have in common.
• When you need to show how given items are similar or different.
• To display logical relationships from various datasets.

The above Venn chart clearly shows the core customers of a product – the people who like eating fast foods but don’t want to gain weight.

The Venn chart gives you an instant understanding of who you will need to sell.

Then, you can plan how to attract the target segment with advertising and promotions.

12. Decision Tree

As graphical representations of complex or simple problems and questions, decision trees have an important role in business, finance, marketing, and in any other areas.

What is a decision tree?

A decision tree is a diagram that shows possible solutions to a decision.

It displays different outcomes from a set of decisions. The diagram is a widely used decision-making tool for analysis and planning.

The diagram starts with a box (or root), which branches off into several solutions. That’s why it is called a decision tree.

Decision trees are helpful for a variety of reasons. Not only they are easy-to-understand diagrams that support you ‘see’ your thoughts, but also because they provide a framework for estimating all possible alternatives.

When to use a decision tree?

• When you need help in making decisions and want to display several possible solutions.

Imagine you are an IT project manager and you need to decide whether to start a particular project or not.

You need to take into account important possible outcomes and consequences.

The decision tree, in this case, might look like the diagram above.

13. Fishbone Diagram

Fishbone diagram is a key tool for root cause analysis that has important uses in almost any business area.

It is recognized as one of the best graphical methods to understand and solve problems because it takes into consideration all the possible causes.

What is a fishbone diagram?

A fishbone diagram (also known as a cause and effect diagram, Ishikawa diagram or herringbone diagram) is a data visualization technique for categorizing the potential causes of a problem.

The main purpose is to find the root cause.

It combines brainstorming with a kind of mind mapping and makes you think about all potential causes of a given problem, rather than just the one or two.

It also helps you see the relationships between the causes in an easy to understand way.

When to use a fishbone diagram?

• When you want to display all the possible causes of a problem in a simple, easy to read graphical way.

Let’s say you are an online marketing specialist working for a company witch experience low website traffic.

You have the task to find the main reasons. Above is a fishbone diagram example that displays the possible reasons and can help you resolve the situation.

14. Process Flow Diagram

If you need to visualize a specific process, the process flow diagram will help you a lot.

What is the process flow diagram?

As the name suggests, it is a graphical way of describing a process, its elements (steps), and their sequence.

Process flow diagrams show how a large complex process is broken down into smaller steps or tasks and how these go together.

As a data visualization technique, it can help your team see the bigger picture while illustrating the stages of a process.

When to use a process flow diagram?

• When you need to display steps in a process and want to show their sequences clearly.

The above process flow diagram shows clearly the relationship between tasks in a customer ordering process.

The large ordering process is broken down into smaller functions and steps.

15. Spider/Radar Chart

Imagine, you need to rank your favorite beer on 8 aspects (Bitterness, Sweetness, Sourness, Saltiness, Hop, Malt, Yeast, and Special Grain) and then show them graphically. You can use a radar chart.

What is a radar chart?

Radar chart (also called spider, web, and polar bar) is a popular data visualization technique that displays multivariate data.

In can compare several items with many metrics of characteristics.

To be effective and clear, the radar chart should have more than 2 but no more than 6 items that are judged.

When to use a radar chart?

• When you need to compare several items with more than 5 metrics of characteristics.

The above radar chart compares employee’s performance with a scale of 1-5 on skills such as Communications, Problem-solving, Meeting deadlines, Technical knowledge, Teamwork.

A point that is closer to the center on an axis shows a lower value and a worse performance.

It is obvious that Mary has a better performance than Linda.

16. Mind Map

Mind maps are beautiful data visuals that represent complex relationships in a very digestible way.

What is a mind map?

A mind map is a popular diagram that represents ideas and concepts.

It can help you structure your information and analyze, recall, and generate new ideas.

It is called a mind map because it is structured in a way that resembles how the human brain works.

And, best of all, it is a fun and artistic data visualization technique that engages your brain in a much richer way.

When to use a mind map?

• When you want to visualize and connect ideas in an easy to digest way.
• When you want to capture your thoughts/ideas and bring them to life in visual form.

Image source: Lucidchart

The above example of a mind map illustrates the key elements for running a successful digital marketing campaign.

It can help you prepare and organize your marketing efforts more effectively.

17. Gantt Chart

A well-structured Gantt chart aids you to manage your project successfully against time.

What is a Gantt chart?

Gantt charts are data visualization types used to schedule projects by splitting them into tasks and subtasks and putting them on a timeline.

Each task is listed on one side of the chart. This task also has a horizontal line opposite it representing the length of the task.

By displaying tasks with the Gantt chart, you can see how long each task will take and which tasks will overlap.

Gantt charts are super useful for scheduling and planning projects.

They help you estimate how long a project should take and determine the resources needed.

They also help you plan the order in which you’ll complete tasks and manage the dependencies between tasks.

When to use a Gantt chart?

• When you need to plan and track the tasks in project schedules.

Image Source: Aha.io

The above example is a portfolio planning Gantt Chart Template that illustrates very well how Gantt Charts work.

It visualizes the release timeline for multiple products for an entire year.

It shows also dependencies between releases.

You can use it to help team members understand the release schedule for the upcoming year, the duration of each release, and the time for delivering.

This helps you in resource planning and allows teams to coordinate implementation plans.

18. Organizational Charts

Organizational charts are data visualization types widely used for management and planning.

What is an organizational chart?

An organizational chart (also called an org chart) is a diagram that illustrates a relationship hierarchy.

The most common application of an org chart is to display the structure of a business or other organization.

Org charts are very useful for showing work responsibilities and reporting relationships.

They help leaders effectively manage growth or change.

Moreover, they show employees how their work fits into the company’s overall structure.

When to use the org chart?

• When you want to display a hierarchical structure of a department, company or other types of organization.

Image Source: Organimi

The above hierarchical org chart illustrates the chain of command that goes from the top (e.g., the CEOs) down (e.g., entry-level and low-level employees) and each person has a supervisor.

It clearly shows levels of authority and responsibility and who each person reports to.

It also shows employees the career paths and chances for promotion.

19. Area Map

Most business data has a location. Revenue, sales, customers, or population are often displayed with a dimensional variable on a map.

What is an area map?

It is a map that visualizes location data.

They allow you to see immediately which geographical locations are most important to your brand and business.

Image Source: Infogram

The map above depicts sales by location and the color indicates the level of sales (the darker the blue, the higher the sales).

These data visualization types are very useful as they show where in the world most of your sales are from and where your most valuable sales are from.

Insights like these illustrate weaknesses in a sales and marketing strategy in seconds.

20. Infographics

In recent years, the use of infographics has exploded in almost every industry.

From sales and marketing to science and healthcare, infographics are applied everywhere to present information in a visually appealing way.

What is an infographic?

Infographics are specific data visualization types that combine images, charts, graphs, and text. The purpose is to represent an easy-to-understand overview of a topic.

However, the main goal of an infographic is not only to provide information but also to make the viewing experience fun and engaging for readers.

It makes data beautiful—and easy to digest.

When you want to represent and share information, there are many data visualization types to do that – spreadsheets, graphs, charts, emails, etc.

But when you need to show data in a visually impactful way, the infographic is the most effective choice.

When to use infographics?

• When you need to present complex data in a concise, highly visually-pleasing way.

Image Source: Venngage

The above statistical infographic represents an overview of Social Buzz’s biggest social platforms by age and geography.

For example, we see that 75% of active Facebook users are 18-29 years old and 48% of active users live in North America.

21. T-Chart

If you want to compare and contrast items in a table form, T-Chart can be your solution.

What is a T-Chart?

A T-Chart is a type of graphic organizer in the shape of the English letter “T”. It is used for comparison by separating information into two or more columns.

You can use T-Chart to compare ideas, concepts or solutions clearly and effectively.

T-Charts are often used for comparison of pros and cons, facts and opinions.

By using T-Chart, you can list points side by side, achieve a quick, at-a-glance overview of the facts, and arrive at conclusions quickly and easily.

When to use a T-Chart?

• When you need to compare and contrast two or more items.
• When you want to evaluate the pros and cons of a decision.

The above T-Chart example clearly outlines the cons and pros of hiring a social media manager in a company.

10 Best Data Visualization Tools

There is a broad range of data visualization tools that allow you to make fascinating graphs, charts, diagrams, maps, and dashboards in no time.

They vary from BI (Business Intelligence) tools with robust features and comprehensive dashboards to more simple software for just creating graphs and charts.

Here we’ve collected some of the most popular solutions. They can help you present your data in a way that facilitates understanding and decision making.

1. Visme is a data presentation and visualization tool that allows you to create stunning data reports. It provides a great variety of presentation tools and templates for a unique design.

2. Infogram is a chart software tool that provides robust diagram-making capabilities. It comes with an intuitive drag-and-drop editor and ready-made templates for reports. You can also add images for your reports, icons, GIFs, photos, etc.

3. Venngage is an infographic maker. But it also is a great chart software for small businesses because of its ease of use, intuitive design, and great templates.

4. SmartDraw is best for those that have someone graphic design skills. It has a slightly more advanced design and complexity than Venngage, Visme, and Infogram, … so having some design skills is an advantage. It’s a drawing tool with a wide range of charts, diagrams, maps, and well-designed templates.

5. Creately is a dynamic diagramming tool that offers the best free version. It can be deployed from the cloud or on the desktop and allows you to create your graphs, charts, diagrams, and maps without any tech skills.

6. Edraw Max is an all-in-one diagramming software tool that allows you to create different data visualization types at a high speed. These include process flow charts, line graphs, org charts, mind maps, infographics, floor plans, network diagrams, and many others. Edraw Max has a wide selection of templates and symbols, letting you to rapidly produce the visuals you need for any purpose.

7. Chartio is an efficient business intelligence tool that can help you make sense of your company data. Chartio is simple to use and allows you to explore all sorts of information in real-time.

8. Sisense – a business intelligence platform with a full range of data visualizations. You can create dashboards and graphical representations with a drag and drop user interface.

9. Tableau – a business intelligence system that lets you quickly create, connect, visualize, and share data seamlessly.

10. Domo is a cloud business intelligence platform that helps you examine data using graphs and charts. You can conduct advanced analysis and create great interactive visualization.

Data visualization techniques are vital components of data analysis, as they can summarize large amounts of data effectively in an easy to understand graphical form.

There are countless data visualization types, each with different pros, cons, and use cases.

The trickiest part is to choose the right visual to represent your data.

Your choice depends on several factors – the kind of conclusion you want to draw, your audience, the key metrics, etc.

I hope the above article helps you understand better the basic graphs and their uses.

When you create your graph or diagram, always remember this:

A good graph is the one reduced to its simplest and most elegant form without sacrificing what matters most – the purpose of the visual.

About The Author

Silvia Valcheva

Silvia Valcheva is a digital marketer with over a decade of experience creating content for the tech industry. She has a strong passion for writing about emerging software and technologies such as big data, AI (Artificial Intelligence), IoT (Internet of Things), process automation, etc.

Leave a Reply Cancel Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed .

11 Data Visualization Techniques for Every Use-Case with Examples

Data visualization is rapidly becoming an essential skill in data science and many other data-driven industries, such as finance, education, and healthcare. This comes with no surprise: as data practitioners are dealing with an ever-growing volume of complex and varied data, data visualization provides a set of techniques to make sense of it and effectively communicate data insights.

Historically considered a minor topic in data science, today, data visualization is a vibrant and fast-paced field, enriched with numerous techniques, tools, theories, and contributions from other disciplines, like psychology and neuroscience. If you’re interested in becoming a data visualization wizard, DataCamp gets you covered. Check out our data visualization course catalog to access more than 30 data visualization courses taught by leading experts and covering a variety of popular technologies.

This article provides an overview of the state of data visualization. We will focus on the most popular data visualization analyses, techniques, and tools. Keep reading!

The Power of Good Data Visualization

Data visualization involves the use of graphical representations of data, such as graphs, charts, and maps. Compared to descriptive statistics or tables, visuals provide a more effective way to analyze data, including identifying patterns, distributions, and correlations and spotting outliers in complex datasets.

Visuals allow data scientists to summarize thousands of rows and columns of complex data and put it in an understandable and accessible format.

By bringing data to life with insightful plots and charts, data visualization is vital in decision-making processes. Whether it’s data analysts breaking down their findings to non-technical stakeholders, data scientists performing A/B tests for marketing purposes, or machine learning engineers explaining potential bias in complex large language models like ChatGPT, data visualization is the key to moving from data insights to decision-making.

Despite the use of data visualization, many thorough and detailed data analyses still end up in the drawer for the simple reason that they didn’t get to captivate the audience, whether decision-makers, stakeholders, or other members of the team.

Thanks to progress in disciplines like neuroscience, today, we know the way a data visualization is depicted can severely affect how people perceive it. The choices you make when designing a graph –for example, the colors, the layout, and the size– can make a big difference. Interested in the theory behind data visualization? Our Understanding Data Visualization Course is a great place to get started.

While data visualization has an important role to play when communicating data insights, the recipe for successful communication is more complex. That’s the idea behind data storytelling, an innovative approach that advocates for using visuals, narrative, and data to turn data insights into action. To know more about data storytelling, check out our DataFramed podcast , where we speak with Brent Dykes, Senior Director of Insights & Data Storytelling at Blast Analytics and author of Effective Data Storytelling.

Types of Data Visualization Analysis

Data visualization is used to analyze visually the behavior of the different variables in a dataset, such as a relationship between data points in a variable or the distribution. Depending on the number of variables you want to study at once, you can distinguish three types of data visualization analysis.

• Univariate analysis . Used to summarize the behavior of only one variable at a time.
• Bivariate analysis . Helps to study the relationship between two variables
• Multivariate analysis . Allows data practitioners to analyze more than two variables at once.

Key Data Visualization Techniques

Let’s now examine the most popular data visualization techniques!

One of the most used visualizations, line plots are excellent at tracking the evolution of a variable over time. They are normally created by putting a time variable on the x-axis and the variable you want to analyze on the y-axis. For example, the line plot below shows the evolution of the DJIA Stock Price during 2022.

Source. DataCamp

To learn about how to create compelling line plots, check out our Line Plots in MatplotLib with Python Tutorial .

A bar chart ranks data according to the value of multiple categories. It consists of rectangles whose lengths are proportional to the value of each category. Bar charts are prevalent because they are easy to read. Businesses commonly use bar charts to make comparisons, like comparing the market share of different brands or the revenue of different regions. There are multiple types of bar charts, each suited for a different purpose.

There are multiple types of bar charts, each suited for a different purpose, including vertical bar plots, horizontal bar plots, and clustered bar plots.

Vertical, horizontal, and clustered bar plots.

Our course, Introduction to Data Science in Python , covers a range of data visualization techniques, including bar plots.

Histograms are one of the most popular visualizations to analyze the distribution of data. They show the numerical variable's distribution with bars.

To build a histogram, the numerical data is first divided into several ranges or bins, and the frequency of occurrence of each range is counted. The horizontal axis shows the range, while the vertical axis represents the frequency or percentage of occurrences of a range.

Histograms immediately showcase how a variable's distribution is skewed or where it peaks. Here are examples from our Data Demystified Series on Data Visualizations that Capture Distributions .

Box and whisker plots

Another great plot to summarize the distribution of a variable is boxplots. Boxplots provide an intuitive and compelling way to spot the following elements:

• Median . The middle value of a dataset where 50% of the data is less than the median and 50% of the data is higher than the median.
• The upper quartile . The 75th percentile of a dataset where 75% of the data is less than the upper quartile, and 25% of the data is higher than the upper quartile.
• The lower quartile . The 25th percentile of a dataset where 25% of the data is less than the lower quartile and 75% is higher than the lower quartile.
• The interquartile range . The upper quartile minus the lower quartile
• The upper adjacent value . Or colloquially, the “maximum.” It represents the upper quartile plus 1.5 times the interquartile range.
• The lower adjacent value . Or colloquially, the “minimum." It represents the lower quartile minus 1.5 times the interquartile range.
• Outliers . Any values above the “maximum” or below the “minimum.”

The anatomy of a box plot. Source: Galarnyk

For example, the following seaborn-based boxplot shows the distribution of sepal length in three varieties of iris plants, drawing on the popular iris dataset. Our Python Seaborn Tutorial For Beginners is a perfect resource to discover how to create boxplots and other graphs using Python’s popular visualization package, Seaborn.

Scatter plots

Scatter plots are used to visualize the relationship between two continuous variables. Each point on the plot represents a single data point, and the position of the point on the x and y-axis represents the values of the two variables. It is often used in data exploration to understand the data and quickly surface potential correlations.

The following example takes again the iris dataset to plot the relationship between sepal width and sepal length.

To have more examples of scatter plots, read our Data Demystified Series on Data Visualizations that Capture Relationships . You can also learn to create a variety of plots, including scatter plots, in our plotting with Matplotlib tutorial .

Bubble plot

Scatter plots can be easily augmented by adding new elements that represent new variables. For example, if we want to plot the relationship between sepal width and sepal length in the different varieties of iris, we could just add colors to the points, as following:

We could also change the size of the points according to another variable. This is what characterizes the so-called bubble plots. For example, this incredible graph shows the relationship between a country's life expectancy and GDP, adding color to represent the country's region, and size to represent the country's population.

Source. Gapminder

We cover bubble plots and how to create them in our course, Intermediate Interactive Data Visualization with plotly in R .

Treemaps are suitable to show part-to-whole relationships in data. They display hierarchical data as a set of rectangles. Each rectangle is a category within a given variable, whereas the area of the rectangle is proportional to the size of that category. Compared to similar visualizations, like pie charts, tree maps are considered more intuitive and preferable.

Below you can find an example.

In our Sentiment Analysis in R course , you’ll learn how to use treemaps to visualize sentiment in groups of documents.

A heatmap is a common and beautiful matrix plot that can be used to graphically summarize the relationship between two variables. The degree of correlation between two variables is represented by a color code.

For example, this heat extracted from our Intermediate Data Visualization with Seaborn Course analyzes the occupation of the guests of the Daily Show during the 1999-2012 period. As expected, guests from the acting and media industries are the most frequent attendants.

To learn more about how to create a heatmap , you can check out our tutorial that explores how to make one using Power BI.

Word clouds

Word clouds are useful for visualizing common words in a text or data set. They're similar to bar plots but are often more visually appealing. However, at times word clouds can be harder to interpret. World clouds are useful in the following scenarios:

• Quickly identify the most important themes or topics in a large body of text.
• Understand the overall sentiment or tone of a piece of writing.
• Explore patterns or trends in data that contain textual information.
• Communicate the key ideas or concepts in a visually engaging way.

Check out our Generating WordClouds in Python Tutorial to discover how to create your own word cloud.

Source. Datacamp

A considerable proportion of the data generated every day is inherently spatial. Spatial data –also known sometimes as geospatial data or geographic information– are data for which a specific location is associated with each record.

Every spatial data point can be located on a map using a certain coordinate reference system. For example, the image below, extracted from our GeoPandas Tutorial , shows the different districts of Barcelona.

Geospatial analysis is a rapidly-evolving field within data science. Maps are at the heart of this discipline. Check out our Working with Geospatial Data in Python Course to start drawing maps today!

Network diagrams

Most data is stored in tables. However, this is not the only format available. The so-called graphs are better suited to analyze data that is organized in networks, such as online social networks, like Facebook and Twitter, to transportation networks, like metro lines. Network analytics is the subdomain of data science that uses graphs to study networks.

Network graphs consist of two main components: nodes and edges, also known as relationships. This is an example of a simple network graph.

Cool right? The possibilities of network graphs are endless. To get a gentle introduction to this field, we highly recommend our Introduction to Network Analysis in Python Course .

Choosing the Right Visualization Technique

We have just presented a small subset of the many data visualization techniques available. Depending on the type of analysis you want to perform, some graphs will be more suitable than others.

For example, if you want to showcase trends and fluctuations in data over time, a line plot is what you’re looking for. By contrast, if you want to analyze the distribution of the data points in a variable, a histogram or a boxplot will be better suited.

When deciding what technique to use, ask yourself the following questions:

• How many variables do you want to analyze at once? Depending on the answer, you will be performing univariate, bivariate, or multivariate analysis.
• What do you want to analyze? Each visualization is suitable for analyzing one of the following phenomena:
• Distribution
• Correlation
• Part-of-Whole

With practice, matching the visualization technique with the type of data and the question being answered will be a straightforward process.

Tools for Data Visualization

Data visualization tools range from no-code business intelligence tools like Power BI and Tableau to online visualization platforms like DataWrapper and Google Charts. There are also specific packages in popular programming languages for data science, such as Python and R . As such, data visualization is often viewed as the entry point, or “gateway drug,” for many aspiring data practitioners.

When deciding on a data visualization tool, you should consider the following factors:

• Learning curve . The ease of use and complexity of data visualization tools range considerably. Generally, the more features and capabilities, the steeper the learning curve. Simpler data visualization tools are better suited for non-technical users, but they come with more constraints and limitations.
• Flexibility . If you want to complete control over every little aspect of your visualizations, you should go for tools with wide flexibility. It will take you more time to get familiar with them, but once you are there, you will be able to produce incredibly aesthetic and customizable visualizations.
• Type of visualization . Data visualization tools can be categorized depending on whether they focus on independent plots or dashboards. The first category of tools is designed to create one visualization at a time. The second category treats applications or dashboards as the basic unit. Tools like Power BI and Tableau fall within this category.
• Price . Price is an important factor to consider when choosing a data visualization tool. Depending on your needs and budget, some tools will function better than others.

In the fast-paced field of data visualization, new tools are launching the ecosystem every day. Choosing the right one for your needs can be daunting. That’s why we have prepared an article with 12 of the Best Data Visualizations Tools that may help you make up your mind.

Best Practices for Effective Data Visualization

The main goal of data visualization is to reduce complexity and provide clarity. Choosing the right data visualization technique is vital for success, but there are many other factors to consider. Here are some of the design best practices to effectively communicate data insights with your audience.

• Consider your audience . As a golden rule, you should always empathize with the audience your visualization is addressing. This means having a good understanding of your audience’s area of expertise, level of technical knowledge, and interests.
• Clear the clutter . To avoid making unreadable, cluttered visualizations, ask yourself if what you’re including is relevant to the audience, and remove unnecessary elements as much as you can.
• Keep an eye on the fonts . Even though it can be tempting to use different fonts and sizes, as a general rule of thumb, stick to one font with no more than three different sizes. You should follow the font hierarchy and keep headings larger than the body, as well as use a bold typeface to highlight key elements and headings.
• Use colors creatively . Color is one of the most eye-catching aspects of any data visualization. As such, put a lot of thought into choosing the color scheme of your data visualization. This means having a consistent color palette across your visualizations and using color systematically to distinguish between groups, levels of importance, and different kinds of information hierarchy.

Making visualization can be fairly considered an art. Intuition and good taste can make a difference, but you should always consider the theory behind it. To know more about the best practices for effective data visualization, we highly recommend you check out our Data Storytelling & Communication Cheat Sheet . Further, if you are working with dashboards, this article on Best Practices for Designing Dashboards is worth reading.

How to Master Data Visualization Techniques

We hope you enjoyed this article. Now that you have an insight into the state of data visualization, it’s time for practice. DataCamp is here to help. You can find more resources to guide you through your data visualization journey below:

• Data Visualization with Python Skill Track
• Data Visualization with R Skill Track
• Data Visualization in Power BI Course
• Data Visualization in Tableau Course

Data Visualization Cheat Sheet

I am a freelance data analyst, collaborating with companies and organisations worldwide in data science projects. I am also a data science instructor with 2+ experience. I regularly write data-science-related articles in English and Spanish, some of which have been published on established websites such as DataCamp, Towards Data Science and Analytics Vidhya As a data scientist with a background in political science and law, my goal is to work at the interplay of public policy, law and technology, leveraging the power of ideas to advance innovative solutions and narratives that can help us address urgent challenges, namely the climate crisis. I consider myself a self-taught person, a constant learner, and a firm supporter of multidisciplinary. It is never too late to learn new things.

Exploring 12 of the Best Data Visualization Tools in 2023 With Examples

Javier Canales Luna

Top 10 Data Visualization Books

What is Data Visualization? A Guide for Data Scientists

Kurtis Pykes

Top 10 Data Science Tools To Use in 2024

Abid Ali Awan

Top 9 Power BI Dashboard Examples

Eugenia Anello

cheat sheet

Richie Cotton

Guide On Graphical Representation of Data – Types, Importance, Rules, Principles And Advantages

What are Graphs and Graphical Representation?

Graphs, in the context of data visualization, are visual representations of data using various graphical elements such as charts, graphs, and diagrams. Graphical representation of data , often referred to as graphical presentation or simply graphs which plays a crucial role in conveying information effectively.

Principles of Graphical Representation

Effective graphical representation follows certain fundamental principles that ensure clarity, accuracy, and usability:Clarity : The primary goal of any graph is to convey information clearly and concisely. Graphs should be designed in a way that allows the audience to quickly grasp the key points without confusion.

• Simplicity: Simplicity is key to effective data visualization. Extraneous details and unnecessary complexity should be avoided to prevent confusion and distraction.
• Relevance: Include only relevant information that contributes to the understanding of the data. Irrelevant or redundant elements can clutter the graph.
• Visualization: Select a graph type that is appropriate for the supplied data. Different graph formats, like bar charts, line graphs, and scatter plots, are appropriate for various sorts of data and relationships.

Rules for Graphical Representation of Data

Creating effective graphical representations of data requires adherence to certain rules:

• Select the Right Graph: Choosing the appropriate type of graph is essential. For example, bar charts are suitable for comparing categories, while line charts are better for showing trends over time.
• Label Axes Clearly: Axis labels should be descriptive and include units of measurement where applicable. Clear labeling ensures the audience understands the data’s context.
• Use Appropriate Colors: Colors can enhance understanding but should be used judiciously. Avoid overly complex color schemes and ensure that color choices are accessible to all viewers.
• Avoid Misleading Scaling: Scale axes appropriately to prevent exaggeration or distortion of data. Misleading scaling can lead to incorrect interpretations.
• Include Data Sources: Always provide the source of your data. This enhances transparency and credibility.

Importance of Graphical Representation of Data

Graphical representation of data in statistics is of paramount importance for several reasons:

• Enhances Understanding: Graphs simplify complex data, making it more accessible and understandable to a broad audience, regardless of their statistical expertise.
• Helps Decision-Making: Visual representations of data enable informed decision-making. Decision-makers can easily grasp trends and insights, leading to better choices.
• Engages the Audience: Graphs capture the audience’s attention more effectively than raw data. This engagement is particularly valuable when presenting findings or reports.
• Universal Language: Graphs serve as a universal language that transcends linguistic barriers. They can convey information to a global audience without the need for translation.

Advantages of Graphical Representation

The advantages of graphical representation of data extend to various aspects of communication and analysis:

• Clarity: Data is presented visually, improving clarity and reducing the likelihood of misinterpretation.
• Efficiency: Graphs enable the quick absorption of information. Key insights can be found in seconds, saving time and effort.
• Memorability: Visuals are more memorable than raw data. Audiences are more likely to retain information presented graphically.
• Problem-Solving: Graphs help in identifying and solving problems by revealing trends, correlations, and outliers that may require further investigation.

Use of Graphical Representations

Graphical representations find applications in a multitude of fields:

• Business: In the business world, graphs are used to illustrate financial data, track performance metrics, and present market trends. They are invaluable tools for strategic decision-making.
• Science: Scientists employ graphs to visualize experimental results, depict scientific phenomena, and communicate research findings to both colleagues and the general public.
• Education: Educators utilize graphs to teach students about data analysis, statistics, and scientific concepts. Graphs make learning more engaging and memorable.
• Journalism: Journalists rely on graphs to support their stories with data-driven evidence. Graphs make news articles more informative and impactful.

Types of Graphical Representation

There exists a diverse array of graphical representations, each suited to different data types and purposes. Common types include:

1.Bar Charts:

Used to compare categories or discrete data points, often side by side.

2. Line Charts:

Ideal for showing trends and changes over time, such as stock market performance or temperature fluctuations.

3. Pie Charts:

Display parts of a whole, useful for illustrating proportions or percentages.

4. Scatter Plots:

Reveal relationships between two variables and help identify correlations.

5. Histograms:

Depict the distribution of data, especially in the context of continuous variables.

In conclusion, the graphical representation of data is an indispensable tool for simplifying complex information, aiding in decision-making, and enhancing communication across diverse fields. By following the principles and rules of effective data visualization, individuals and organizations can harness the power of graphs to convey their messages, support their arguments, and drive informed actions.

Download PPT of Graphical Representation

Video On Graphical Representation

FAQs on Graphical Representation of Data

What is the purpose of graphical representation.

Graphical representation serves the purpose of simplifying complex data, making it more accessible and understandable through visual means.

Why are graphs and diagrams important?

Graphs and diagrams are crucial because they provide visual clarity, aiding in the comprehension and retention of information.

How do graphs help learning?

Graphs engage learners by presenting information visually, which enhances understanding and retention, particularly in educational settings.

Who uses graphs?

Professionals in various fields, including scientists, analysts, educators, and business leaders, use graphs to convey data effectively and support decision-making.

Where are graphs used in real life?

Graphs are used in real-life scenarios such as business reports, scientific research, news articles, and educational materials to make data more accessible and meaningful.

Why are graphs important in business?

In business, graphs are vital for analyzing financial data, tracking performance metrics, and making informed decisions, contributing to success.

Leave a comment

Cancel reply.

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

Related Posts

Best Google AdWords Consultants in India...

What is a Google Ads Consultant? A Google Ads Consultant is an expert who specializes in delivering expertise and advice on Google Ads, which is Google’s online advertising medium. Google Ads permits companies to develop and run ads that are visible on Google’s search engine and other Google platforms. The function of a Google Ads […]

Best PPC Consultants in India –...

What Is a PPC Consultant? A PPC consultant or a pay per click consultant is an expert who specializes in handling and optimizing PPC advertisement drives for companies. PPC is a digital marketing model where advertisers pay a price each time their ad is clicked. Standard PPC mediums include Bing Ads, Google Ads, and social media advertisement platforms like […]

Top 20 Generic Digital Marketing Interview...

1. What is Digital Marketing? Digital marketing is also known as online marketing which means promoting and selling products or services to potential customers using the internet and online platforms. It includes email, social media, and web-based advertising, but also text and multimedia messages as a marketing channel. 2. What are the types of Digital […]

Best Social Media Consultants in India...

What Is a Social Media Consultant? A social media advisor is a specialist who delivers direction, recommendation, and assistance linked to the usage of social media for people, companies, or associations. Their prime objective is to support customers effectively by employing social media platforms to gain specific objectives, such as improving brand awareness, entertaining target […]

Gaurav Mittal

Had a great time spent with some awesome learning at The Digital Education Institute. It really helped me to build my career and i am thankful to the institute for making me what i am today.

Company where our students are working

Enroll Now for 2 Hour Free Digital Marketing Class

Lorem Ipsum is simply dummy text of the printing and typesetting industry

Lorem Ipsum is simply dummy text of the printing and typesetting industry . Lorem Ipsum is simply dummy text of the printing and typesetting industry

If you're seeing this message, it means we're having trouble loading external resources on our website.

If you're behind a web filter, please make sure that the domains *.kastatic.org and *.kasandbox.org are unblocked.

To log in and use all the features of Khan Academy, please enable JavaScript in your browser.

Praxis Core Math

Course: praxis core math   >   unit 1, data representations | lesson.

• Data representations | Worked example
• Center and spread | Lesson
• Center and spread | Worked example
• Random sampling | Lesson
• Random sampling | Worked example
• Scatterplots | Lesson
• Scatterplots | Worked example
• Interpreting linear models | Lesson
• Interpreting linear models | Worked example
• Correlation and Causation | Lesson
• Correlation and causation | Worked example
• Probability | Lesson
• Probability | Worked example

What are data representations?

• How much of the data falls within a specified category or range of values?
• What is a typical value of the data?
• How much spread is in the data?
• Is there a trend in the data over time?
• Is there a relationship between two variables?

What skills are tested?

• Matching a data set to its graphical representation
• Matching a graphical representation to a description
• Using data representations to solve problems

How are qualitative data displayed?

• A vertical bar chart lists the categories of the qualitative variable along a horizontal axis and uses the heights of the bars on the vertical axis to show the values of the quantitative variable. A horizontal bar chart lists the categories along the vertical axis and uses the lengths of the bars on the horizontal axis to show the values of the quantitative variable. This display draws attention to how the categories rank according to the amount of data within each. Example The heights of the bars show the number of students who want to study each language. Using the bar chart, we can conclude that the greatest number of students want to study Mandarin and the least number of students want to study Latin.
• A pictograph is like a horizontal bar chart but uses pictures instead of the lengths of bars to represent the values of the quantitative variable. Each picture represents a certain quantity, and each category can have multiple pictures. Pictographs are visually interesting, but require us to use the legend to convert the number of pictures to quantitative values. Example Each represents 40 ‍   students. The number of pictures shows the number of students who want to study each language. Using the pictograph, we can conclude that twice as many students want to study French as want to study Latin.
• A circle graph (or pie chart) is a circle that is divided into as many sections as there are categories of the qualitative variable. The area of each section represents, for each category, the value of the quantitative data as a fraction of the sum of values. The fractions sum to 1 ‍   . Sometimes the section labels include both the category and the associated value or percent value for that category. Example The area of each section represents the fraction of students who want to study that language. Using the circle graph, we can conclude that just under 1 2 ‍   the students want to study Mandarin and about 1 3 ‍   want to study Spanish.

How are quantitative data displayed?

• Dotplots use one dot for each data point. The dots are plotted above their corresponding values on a number line. The number of dots above each specific value represents the count of that value. Dotplots show the value of each data point and are practical for small data sets. Example Each dot represents the typical travel time to school for one student. Using the dotplot, we can conclude that the most common travel time is 10 ‍   minutes. We can also see that the values for travel time range from 5 ‍   to 35 ‍   minutes.
• Histograms divide the horizontal axis into equal-sized intervals and use the heights of the bars to show the count or percent of data within each interval. By convention, each interval includes the lower boundary but not the upper one. Histograms show only totals for the intervals, not specific data points. Example The height of each bar represents the number of students having a typical travel time within the corresponding interval. Using the histogram, we can conclude that the most common travel time is between 10 ‍   and 15 ‍   minutes and that all typical travel times are between 5 ‍   and 40 ‍   minutes.

How are trends over time displayed?

How are relationships between variables displayed.

• (Choice A)   A
• (Choice B)   B
• (Choice C)   C
• (Choice D)   D
• (Choice E)   E
• Your answer should be
• an integer, like 6 ‍  
• a simplified proper fraction, like 3 / 5 ‍  
• a simplified improper fraction, like 7 / 4 ‍  
• a mixed number, like 1   3 / 4 ‍  
• an exact decimal, like 0.75 ‍  
• a multiple of pi, like 12   pi ‍   or 2 / 3   pi ‍  
• a proper fraction, like 1 / 2 ‍   or 6 / 10 ‍  
• an improper fraction, like 10 / 7 ‍   or 14 / 8 ‍  

Things to remember

• When matching data to a representation, check that the values are graphed accurately for all categories.
• When reporting data counts or fractions, be clear whether a question asks about data within a single category or a comparison between categories.
• When finding the number or fraction of the data meeting a criteria, watch for key words such as or , and , less than , and more than .

Want to join the conversation?

• Upvote Button navigates to signup page
• Downvote Button navigates to signup page
• Flag Button navigates to signup page

• Reviews / Why join our community?
• For companies
• Frequently asked questions

Guidelines for Good Visual Information Representations

Information visualization is not as easy as it might first appear, particularly when you are examining complex data sets. How do you deliver a “good” representation of the information that you bring out of the data that you are working with?

While this may be a subjective area of information visualization and, of course , there are exceptions to the guidelines (as with all areas of design – rules are for breaking if by breaking them you achieve your purpose) it’s best to begin with the four guidelines outlined by Edward Tufte.

About Edward Tufte

Edward Tufte is, perhaps, the world’s leading authority on information design and data visualization . He is an American statistician and a Professor Emeritus at Yale University (for political sciences, computer sciences and statistics).

He has authored several books and papers on analytic design and is a strong proponent for the power of visualizing data. In particular his books, Visual Display of Quantitative Information, Envisioning Information, Visual Explanations and Beautiful Evidence are considered to be definitive works in the field of information visualization. The New York Times called him; “The Leonardo da Vinci of data.”

Within his works you can find four essential guidelines for visual information representation:

Graphical Excellence

Visual integrity, maximizing the data-ink ratio, aesthetic elegance, tufte’s criteria for good visual information representation.

The purpose of “good’ representations is to deliver a visual representation of data to the user of that representation which is “most fit for purpose”. This will enable the user of the information to make the most out of the representation. There is no single hard and fast rule for creating good representations because the nature of the data, the users of that data, etc. are enormously varied.

Thus we find ourselves with a set of criteria which can be applied to most visual representations, as suggested by Tufte, to judge their fitness for purpose. It must be acknowledged, however, that these criteria can be bent or even broken if doing so serves a purpose for the user of the information representation.

There could be hours of debate as to what constitutes graphical excellence but Tufte offers that in data representations at least it should provide the user with; “the greatest number of ideas, in the shortest time, using the least amount of ink, in the smallest space.”

In short as with many other areas of user experience – the focus here is on usability ; it is completely possible to create beautiful graphical representations of data which fail to deliver on these premises. In fact, it might be said that this occurs so often that the power of data visualization is muted because people have come to expect such visualizations to be decorative rather than valuable.

The graphic above, relating to US employment statistics in March 2015, offers many ideas in a very small space and is easy to digest. We’d suggest it meets the criteria of “graphical excellence”.

This is a confusing term. When Tufte refers to “visual integrity” he is invoking an almost moral position in that the representation should neither distort the underlying data nor create a false impression or interpretation of that data.

In practice this means that numerical scales should be properly proportionate (and not fudged to exaggerate the fall or rise of a curve at a particular point, for example). That variations, when they occur, should relate to the data rather than to the artistic interpretation of that data. The dimensions used within an image should be limited to the dimensions within the data and should never exceed them and finally that the keys (or legends) should be undistorted and unambiguous.

This bar graph fails to give us enough information to be useful and thus fails in delivering “visual integrity”.

Tufte recommends that we pay attention to the way that a visualization is compiled; in that all superfluous elements (to the user) should be removed. He offers the idea that borders, backgrounds, use of 3D, etc. may do nothing but serve to distract the user from the information itself. He promotes that you give priority to the data and how it will be used and not to the visual appearance of that representation.

He also provides a mathematical formula for a data-ink ratio:

Data-Ink/Total Ink Used

This is simply a comparison of the ink needed to clearly and unambiguously present the data to the ink actually used (including aesthetic considerations). The closer the ratio is to 1 – the less distracting your representation is likely to be and thus the more useful it is likely to be for your user.

This image of business processes with an ERP environment is quite good at conveying which business functions are affected by the ERP processes but what purpose does the color scheme serve?

Tufte’s interpretation of aesthetic elegance is not based on the “physical beauty” of an information visualization but rather the simplicity of the design evoking the complexity of the data clearly.

He holds up Minard’s visualization (pictured below) of Napoleon’s March in the Russian Campaign as an example of aesthetic elegance.

The Take Away

Tufte’s guidelines are not prescriptive but rather designed to assist the information visualization professional in creating usable and useful information representations. At their core his rules can be boiled down to keeping things as simple and as honest as possible. The rest simply ensure that you adapt to complexity in the most creative and basic way possible.

UX designers will see clear links between their own design work on products and the design of information representations.

References and Resources

You can find all of Edward Tufte’s work via his website .

Find out more about Charles Joseph Minard and his map of Napoleon’s Russian Campaign.

You can also find an interesting analysis of Minard’s map here .

Hero Image: Author/Copyright holder: Kitware Inc. Copyright terms and licence: CC BY-ND 2.0

UI Design Patterns for Successful Software

Get Weekly Design Tips

Topics in this article, what you should read next, user interface design guidelines: 10 rules of thumb.

• 1.4k shares

Information Overload, Why it Matters and How to Combat It

• 1.1k shares
• 4 years ago

The Key Elements & Principles of Visual Design

How to Develop an Empathic Approach in Design Thinking

Simple Guidelines When You Design for Mobile

How to Design an Information Visualization

How to Visualize Your Qualitative User Research Results for Maximum Impact

• 3 years ago

Preattentive Visual Properties and How to Use Them in Information Visualization

• 5 years ago

6 Common Pitfalls in Prototyping and How to Avoid Them

How to Conduct Focus Groups

Open Access—Link to us!

We believe in Open Access and the  democratization of knowledge . Unfortunately, world-class educational materials such as this page are normally hidden behind paywalls or in expensive textbooks.

If you want this to change , cite this article , link to us, or join us to help us democratize design knowledge !

Privacy Settings

Our digital services use necessary tracking technologies, including third-party cookies, for security, functionality, and to uphold user rights. Optional cookies offer enhanced features, and analytics.

Experience the full potential of our site that remembers your preferences and supports secure sign-in.

Governs the storage of data necessary for maintaining website security, user authentication, and fraud prevention mechanisms.

Enhanced Functionality

Saves your settings and preferences, like your location, for a more personalized experience.

Referral Program

We use cookies to enable our referral program, giving you and your friends discounts.

Error Reporting

We share user ID with Bugsnag and NewRelic to help us track errors and fix issues.

Optimize your experience by allowing us to monitor site usage. You’ll enjoy a smoother, more personalized journey without compromising your privacy.

Analytics Storage

Collects anonymous data on how you navigate and interact, helping us make informed improvements.

Differentiates real visitors from automated bots, ensuring accurate usage data and improving your website experience.

Lets us tailor your digital ads to match your interests, making them more relevant and useful to you.

Advertising Storage

Stores information for better-targeted advertising, enhancing your online ad experience.

Personalization Storage

Permits storing data to personalize content and ads across Google services based on user behavior, enhancing overall user experience.

Advertising Personalization

Allows for content and ad personalization across Google services based on user behavior. This consent enhances user experiences.

Enables personalizing ads based on user data and interactions, allowing for more relevant advertising experiences across Google services.

Receive more relevant advertisements by sharing your interests and behavior with our trusted advertising partners.

Enables better ad targeting and measurement on Meta platforms, making ads you see more relevant.

Allows for improved ad effectiveness and measurement through Meta’s Conversions API, ensuring privacy-compliant data sharing.

LinkedIn Insights

Tracks conversions, retargeting, and web analytics for LinkedIn ad campaigns, enhancing ad relevance and performance.

LinkedIn CAPI

Enhances LinkedIn advertising through server-side event tracking, offering more accurate measurement and personalization.

Google Ads Tag

Tracks ad performance and user engagement, helping deliver ads that are most useful to you.

Share Knowledge, Get Respect!

or copy link

Cite according to academic standards

Simply copy and paste the text below into your bibliographic reference list, onto your blog, or anywhere else. You can also just hyperlink to this article.

New to UX Design? We’re giving you a free ebook!

Download our free ebook The Basics of User Experience Design to learn about core concepts of UX design.

In 9 chapters, we’ll cover: conducting user interviews, design thinking, interaction design, mobile UX design, usability, UX research, and many more!

New to UX Design? We’re Giving You a Free ebook!

Introduction to Graphs

Table of Contents

15 December 2020                 

Read time: 6 minutes

Introduction

What are graphs?

What are the different types of data?

What are the different types of graphical representations?

The graph is nothing but an organized representation of data. It helps us to understand the data. Data are the numerical information collected through observation.

The word data came from the Latin word Datum which means “something given”

After a research question is developed, data is being collected continuously through observation. Then it is organized, summarized, classified, and then represented graphically.

Differences between Data and information: Data is the raw fact without any add on but the information is the meaning derived from data.

Introduction to Graphs-PDF

The graph is nothing but an organized representation of data. It helps us to understand the data. Data are the numerical information collected through observation. Here is a downloadable PDF to explore more.

• Line and Bar Graphs Application
• Graphs in Mathematics & Statistics

What are the different Types of Data?

There are two types of Data :

Quantitative

The data which are statistical or numerical are known as Quantitive data. Quantitive data is generated through. Quantitative data is also known as Structured data. Experiments, Tests, Surveys, Market Report.

Quantitive data is again divided into Continuous data and Discrete data.

Continuous Data

Continuous data is the data which can have any value. That means Continuous data can give infinite outcomes so it should be grouped before representing on a graph.

• The speed of a vehicle as it passes a checkpoint
• The mass of a cooking apple
• The time taken by a volunteer to perform a task

Discrete Data

Discrete data can have certain values. That means only a finite number can be categorized as discrete data.

• Numbers of cars sold at a dealership during a given month
• Number of houses in certain block
• Number of fish caught on a fishing trip
• Number of complaints received at the office of airline on a given day
• Number of customers who visit at bank during any given hour
• Number of heads obtained in three tosses of a coin

Differences between Discrete and Continuous data

• Numerical data could be either discrete or continuous
• Continuous data can take any numerical value (within a range); For example, weight, height, etc.
• There can be an infinite number of possible values in continuous data
• Discrete data can take only certain values by finite ‘jumps’, i.e., it ‘jumps’ from one value to another but does not take any intermediate value between them (For example, number of students in the class)

Qualitative

Data that deals with description or quality instead of numbers are known as Quantitative data. Qualitative data is also known as unstructured data. Because this type of data is loosely compact and can’t be analyzed conventionally.

Different Types of Graphical Representations

There are many types of graph we can use to represent data. They are as follows,

A bar graph or chart is a way to represent data by rectangular column or bar. The heights or length of the bar is proportional to the values.

A line graph is a type of graph where the information or data is plotted as some dots which are known as markers and then they are added to each other by a straight line.

The line graph is normally used to represent the data that changes over time.

A histogram graph is a graph where the information is represented along with the height of the rectangular bar. Though it does look like a bar graph, there is a fundamental difference between them. With the histogram, each column represents a range of quantitative data when a bar graph represents categorical variables.

The other name of the pie chart is a circle graph. It is a circular chart where numerical information represents as slices or in fractional form or percentage where the whole circle is 100%.

• Stem and leaf plot

The stem and leaf plot is a way to represents quantitative data according to frequency ranges or frequency distribution.

In the stem and leaf plot, each data is split into stem and leaf, which is 32 will be split into 3 stems and 2 leaves.

Frequency table: Frequency means the number of occurrences of an event. A frequency distribution table is a graph or chart which shows the frequency of events. It is denoted as ‘f’ .

Pictograph or Pictogram is the earliest way to represents data in a pictorial form or by using symbols or images. And each image represents a particular number of things.

According to the above-mentioned Pictograph, the number of Appels sold on Monday is 6x2=12.

• Scatter diagrams

Scatter diagram or scatter plot is a way of graphical representation by using cartesian coordinates of two variables. The plot shows the relationship between two variables. Below there is a data table as well as a Scattergram as per the given data.

What is the meaning of Graphical representation?

Graphical representation is a way to represent and analyze quantitive data. A graph is a kind of a chart where data are plotted as variables across the coordinate. It became easy to analyze the extent of change of one variable based on the change of other variables.

Principles of graphical representation

The principles of graphical representation are algebraic. In a graph, there are two lines known as Axis or Coordinate axis. These are the X-axis and Y-axis. The horizontal axis is the X-axis and the vertical axis is the Y-axis. They are perpendicular to each other and intersect at O or point of Origin.

On the right side of the Origin, the Xaxis has a positive value and on the left side, it has a negative value. In the same way, the upper side of the Origin Y-axis has a positive value where the down one is with a negative value.

When X-axis and y-axis intersected each other at the origin it divides the plane into four parts which are called Quadrant I, Quadrant II, Quadrant III, Quadrant IV.

The location on the coordinate plane is known as the ordered pair and it is written as (x,y). That means the first value will be on the x-axis and the second one is on the y-axis. When we will plot any coordinate, we always have to start counting from the origin and have to move along the x-axis, if it is positive then to the right side, and if it is negative then to the left side. Then from the x-axis, we have to plot the y’s value, which means we have to move up for positive value or down if the value is negative along with the y-axis.

In the following graph, 1st ordered pair (2,3) where both the values of x and y are positive and it is on quadrant I. 2nd ordered pair (-3,1), here the value of x is negative and value of y is positive and it is in quadrant II. 3rd ordered pair (-1.5, -2.5), here the value of x as well as y both are Negative and in quadrant III.

Methods of representing a frequency distribution

There are four methods to represent a frequency distribution graphically. These are,

• Smoothed Frequency graph
• Cumulative frequency graph or Ogive.
• Pie diagram.

Advantages and Disadvantages of Graphical representation of data

• It improves the way of analyzing and learning as the graphical representation makes the data easy to understand.
• It can be used in almost all fields from mathematics to physics to psychology and so on.
• It is easy to understand for its visual impacts.
• It shows the whole and huge data in an instance.

The main disadvantage of graphical representation of data is that it takes a lot of effort as well as resources to find the most appropriate data and then represents it graphically.

You may also like:

• Graphing a Quadratic Function
• Empirical Relationship Between Mean, Median, and Mode

Not only in mathematics but almost in every field the graph is a very important way to store, analyze, and represents information. After any research work or after any survey the next step is to organize the observation or information and plotting them on a graph paper or plane. The visual representation of information makes the understanding of crucial components or trends easier.

A huge amount of data can be store or analyze in a small space.

The graphical representation of data helps to decide by following the trend.

A complete Idea: Graphical representation constitutes a clear and comprehensive idea in the minds of the audience. Reading a large number (say hundreds) of pages may not help to make a decision. Anyone can get a clear idea just by looking into the graph or design.

Graphs are a very conceptual topic, so it is essential to get a complete understanding of the concept. Graphs are great visual aids and help explain numerous things better, they are important in everyday life. Get better at graphs with us, sign up for a free trial . 

About Cuemath

Cuemath, a student-friendly mathematics and coding platform, conducts regular Online Classes for academics and skill-development, and their Mental Math App, on both iOS and Android , is a one-stop solution for kids to develop multiple skills. Understand the Cuemath Fee structure and sign up for a free trial.

Frequently Asked Questions (FAQs)

What is data.

Data are characteristics or information, usually numerical, that are collected through observation.

How do you differentiate between data and information?

Data is the raw fact without any add on but the information is the meaning derived from data.

What are the types of data?

There are two types of Data:

What are the ways to represent data?

Tables, charts and graphs are all ways of representing data , and they can be used for two broad purposes. The first is to support the collection, organisation and analysis of data as part of the process of a scientific study.

- Tables, charts and graphs are all ways of representing data, and they can be used for two broad purposes. The first is to support the collection, organisation and analysis of data as part of the process of a scientific study.

What are the different types of graphs?

Different types of graphs include:

What is an Infographic [Theory, Tips, Examples and Mega Inspiration]

• Share on Facebook
• Share on Twitter

By Al Boicheva

in Insights , Inspiration

4 years ago

Viewed 47,996 times

Spread the word about this article:

Updated: May 13, 2022

In today’s article, we’ll review all you need to know about what is an infographic. We’ll look into the anatomy of infographics, their elements, and what makes an infographic great. Of course, we’ll also include many examples and useful tips that will inspire you to create your own engaging infographics. Below is the overview that includes the main topics of the article, so don’t hesitate to fast-travel to specific sections of interest if you’re looking for something in particular.

Article overview:

1. Definition of Infographics 2. What Makes an Infographic Great? 2.1. Audience 2.2. Title 2.3. Simplicity 2.4. Storytelling 3. Types of Infographics 3.1. Visual Infographics 3.2. Timeline Infographics 3.3. Visual Resumes 3.4. List Infographics 3.5. Comparison Infographics 3.6. Statistic Infographics 3.7. Process Infographics 3.8. Map Infographics

4. The Visual Elements Of Infographics 4.1. Colors 4.2. Fonts 4.3. Icons 5. Tips on How To Make An Engaging Infographic 5.1. Tools 5.2. Inspiration 5.3. Topic 5.4. Other Tips

1. What Is an Infographic?

The very name “infographics” is short for information graphics. It defines the visual representation of data that is easy to scan and comprehend at first glance. It’s a powerful tool for businesses and educational institutions to present concepts and data in a more appealing and engaging way.

There are a few things that define what is an infographic in more detail so let’s list them here:

• Infographics simplify heavy data by providing a high-level view.
• They combine images, text, diagrams, charts, and even videos.
• It takes minimal use of text in favor of visuals.
• It’s an effective tool to present and explain complex data quickly and comprehensively.
• Infographics are a great tool for education and building awareness.
• They are designed to reach a wider audience.

2. What Makes an Infographic Great?

After we understood what is an infographic, let’s jump into what makes one engaging. To organize your data in a simple visual way could prove to be quite challenging. Just like in writing content. You still need to focus on catchy headlines, readability, the proper words and images, and most importantly- who are you making the infographic for.

2.1. Audience

A great infographic has a clear idea of who the target audience is . Depending on the age, gender, and culture of the ideal viewer, you already have the right approach on what tone to set in, what colors to use, and what sort of visuals to include.

The key thing is to create infographics that are geared to the needs of your audience.

For example, the infographic below is specifically made for children. It’s entirely visual with a well-crafted colorful illustration with fun characters that instantly reveal the main concept: what animals live underground. It is a great way to educate small children.

Who does live in the underground? by Polina Ugarova

While on the subject of education, infographics have a designated place in textbooks, encyclopedias, and classroom posters. The following example targets older children and students and organizes big historical events in a simple list infographic.

just some assignments… by Bui Dieu Linh

An infographic can also target a specific group such as office workers. In the next example, we see a simple comprehensive visualization of survey results that resonates with the majority of office workers.

Infographic / The Daily Grind by Holly Herman

Great infographics start with a title that sets the topic and core message right on. In fact, a powerful title can determine the success of your graphic. When people process information, they always start with the headline, and once drawn by the topic, they feel curious to learn more.

Exactly the case with this visualization of the 8 things that can make your home office work easier. The title is catchy and instantly explains the topic: How to keep working from home step by step. It sets the issue first “to keep” and instantly claims to have a solution, which compels the audience to keep reading.

How to keep working from home – Infographic by Juliana Bandeira

In the next example, there are two headlines. “Missions to Mars” gives the topic and is followed by the more powerful “How many man-made objects have been sent to the Red Planet and how many actually arrived?” This instantly draws attention to the graphic that reveals very little few dots on the planet Mars.

Missions to Mars by Paul Button

2.3. Simplicity

Sometimes you really need to explain a very complex concept through an infographic. However, making your audience work hard to understand your presentation will defeat the whole point of making the infographic in the first place. If you keep way too many elements and make your design busy, this will distract the viewer from the main point. Consider making your infographic presentation longer if you need to include more data, but make sure to use simpler sections.

Tip: Use a lot of white space. Let your sections and important areas breathe. A busy infographic can be very overwhelming and hard to read.

In the example, the graphic visualizes the path of the pandemic including infections and symptoms, and how they change over time. It’s a complicated topic with lots of data and statistics that will take time to analyze and read. However,  the overall infographic has simplified the concept in different sections with high-contrast colors and accents to make it easier to scan and understand even at the first glance.

Path to Pandemic / BBC Science Focus by James Round

To go a bit further, let’s have a look at an infographic that is practically impossible to make look any simpler. Yet, the designer has still managed to structure the complex data in a comprehensive way.  The graphic shows historical milestones in space exploration. Furthermore, it also includes future space missions and upcoming astronomical phenomena in and around our solar system. It’s extraordinary to visualize pages and pages worthy of rich data in one graphic.

Bureau Oberhaeuser Calendar 2020

2.4. Storytelling

In a way, an infographic tells a story. Therefore, a great infographic will tell a great story and do it clear and accurate. Since this is your story, you have full control over how it flows and the tools you use. You can create flows with white space, text hierarchy, color contrast, and charts.

Below, the UNHCR tells the story of five years of life and conflicts in Sudan. The infographic shows the numbers behind one of the largest humanitarian crises in the world.

South Sudan – UNHCR by Bianco Tangerine

Another way to tell a story is to reveal important data, steps, or tips. The following example dedicated to starting a successful podcast tells the story of the state of podcasting, what you need to start your own, and what technology you will use.

Podcast related infographic design by Lesia Artymovych

3. Types of Infographics

Infographics can be very diverse but there are a few main categories they can be listed in. There is one type we will mention outside of that list, and that is the Informational Infographic. What makes it different is that it focuses on the text and only enhances it with visual elements and colors. Informational infographics take more time to read and understand so they aren’t entirely a visual representation of data.

Informational Infographic Example (Holistic Therapies by Bárbara Americano)

3.1. Visual Infographics

On the contrary, visual infographics cut the text-based elements in favor of visuals. They let the images tell the story and are ideal for presentations, reports, and educational purposes.

Visual Infographic Example (Florida Wildlife Infographics by Yuliya Shumilina)

3.2. Timeline Infographics

This is a very great way to depict data in chronological order, to follow a trend through a period of time, or to show the evolution of a concept.  They are very pleasant and easy to read and have great use for posters, textbooks, and presentations.

Timeline Infographic Example (Typewriter by Trey Thompson)

3.3. Visual Resumes

This type of infographic has a specific purpose to instantly build a great first impression through a striking resume. The infographic CVs is ideal for illustrators, designers, marketers, and developers. However, this doesn’t narrow it down to just the creative industries. You need to make a memorable CV when you apply for a job no matter the field. So, why not take advantage and impress your future employer with an easy-to-scan and comprehend resume that will stand up from the pile of traditional resumes.

Infographic CV Example (Pinda’s Resume by Penellopy L. Sousa)

3.4. List Infographics

List-based infographics are most commonly used to sort heavy data and order it into a list. Such presentations are a great option when you need to list a series of steps to win an argument or to present claims.

List-Based Infographics Example (Amazon product listing by Lutfun Haque)

3.5. Comparison Infographics

This type, as the name suggests, is a format where you can put two concepts against each other. It’s ideal to compare ideas, point out their differences, or even prove the superiority of one of those ideas.

Comparison Infographics Example (Illustrator Vs Photoshop by M.A.Kather)

3.6. Statistic Infographics

This format is widely known as its own category: data visualization. It serves to illustrate very complex and heavy data via graphics, charts, images, and schematics in order to make it visual and comprehensive.

Statistic Infographics Example (Plastic Waste Pollution by Jamie Kettle)

3.7. Process Infographics

This format is ideal to show the flow of a process no matter how complex. The process infographics explain how certain concepts work step by step.

Process Infographics (How-To: Holiday Cocktails)

3.8. Map Infographics

The point of map infographics is to show information based on location. Topics are usually statistics that incorporate areas. It can show the development of a concept in certain countries, cities, or specific places.

Map Infographics Example (Berlin Breakfast Map by Elena Resko)

4. The Visual Elements Of Infographics

Although there are many formats of infographics, colors, fonts and icons are usually what they all have in common. In fact, these elements are the key to make a high-quality visual representation of your concept and convey your message in the best way possible.

4.1. Colors

Just like in everything related to design, colors are the most important element that can make or break your infographic . The right colors can create contrast, atmosphere, and emotions, and influence your viewers to be mesmerized by your work and wish to stay and examine it in detail.

Colors are also powerful symbols and carry strong associations with concepts. Such concepts can be forces (blue for water, green for land), political powers (red for Republicans, blue for Democrats, green for green parties), brand colors, or anything that has a pre-fixed color. With this in mind, it wouldn’t be wise to change or switch such colors and cause confusion.

In terms of creating contrast and knowing how to combine colors perfectly , you could check out our guide to color theory for non-designers .

Balanced Colors in Infographics (IoT Device & Cloud Computing by Anjum Alam)

Important things to consider when you choose colors for your infographics:

• Highlights: Use high contrast colors to highlight or obscure data based on its importance.
• Contrast: When comparing two concepts, you can create contrast by choosing complementary colors.
• Consistency: Be consistent with colors from start to finish and stick to one palette only.
• Meaning: Consider color associations and symbolism.
• Simple Palette: Avoid using more than 5 colors in one infographic. If you need more diversity, you could use different tints or shades of one color instead.

Aside from knowing how to combine colors, it’s also important to know how to combine fonts. This means considering the best practices, which fonts are legible, how to create emphasis through text hierarchy, and more.

Do’s and Don’ts in Design – Infographics by Digital Herd Agency

Important things to consider when you choose fonts for your infographics:

• Legibility: Choose fonts that are easy to read even in big paragraphs in smaller sizes. Avoid display fonts and focus on simple, minimalistic ones.
• No more than two fonts: If you use a lot of different fonts and typefaces this can ruin the harmony of your infographic and aggravate its readability.
• Same Typeface Combinations: You can combine fonts from the same typeface but avoid combining fonts from different families with similar characteristics.
• Serif and Sans Serif: This is the classic combination that works best with serifs for headlines and highest hierarchy texts and sans-serifs for the body text.
• Text Hierarchy: Especially when you use the same font family for the entire infographic, you can create a hierarchy based on font size and weights.
• Mood: Consider what fonts look elegant, romantic, dramatic, or professional, and use them to your advantage to help you communicate the exact tone and mood you intend.

In the meantime, you could also check out our hand-picked collection of 20 free fonts you can add to your fonts library.

Most infographics use icons to organize the information into sections and specific areas or just to indicate concepts. In fact, just a single icon can easily explain an entire paragraph of text.

Infographic by Aimi Humayra Ahmad Suhaime

Important things to consider when you choose icons for your infographics:

• Replacement: During your infographic design process, see if you can replace items or section titles with icons. If you want to indicate different activities during a workday in the office, title each with an icon.
• Social Media Icons: Everybody knows what the icons for Facebook, Instagram, or Twitter look like so it’s safe to use them instead of writing the name of each platform. This also goes for popular brands.
• Clarity: It’s very subjective to point out what icons are designed well and what isn’t, but always go for icons that clearly and unmistakably visualize the concept you want. Most commonly, the simplest universal icons are much more readable and clear than detailed ones.
• Matching: same as colors and fonts, icons need to be consistent as well. Choose icons from the same bundle based on the same style, colors, and level of simplicity.

There are many sources that offer free icon packs to help you out with your infographics. You could check our picks for the best free icon packs that you can download and use right away .

5. Tips on How To Make An Engaging Infographic

If you’re reading this article, you probably wish to make your own infographic for your next presentation. Of course, making an effective infographic that engages and drives results takes time and practice. However, there are a few tips that can definitely help you go in the right direction. So let’s see what we have.

The first thing you do is decide what tools to use. In case you don’t have an in-house designing team or aren’t a designer yourself, you will look for dedicated software.

In the meantime, if you already use software such as Adobe Illustrator , here’s how to create a simple infographic in less than 5 minutes . If you use Google Slides, Powerpoint, Photoshop, and other popular software for your design, you can also take advantage of this selection of infographic templates that you can customize to fit your project .

An easy-to-use tool that offers a rich template library with the option to search by category. It comes with a Free and Pro account.

Creately comes pre-packed with core support for 50+ diagram types, 1000’s professionally designed shape libraries, and templates. Offers Free, Personal, Team, and Enterprise plans.

A perfect tool for visualizing numbers and data that also offers SQL connectors, data analytics, and engagement analytics. It has Free and Paid plans.

You can create an infographic from scratch or choose to work with a template. The tool offers step-by-step tutorials and comes with Free and Paid options.

A versatile design tool developed specifically for marketers to create presentations and infographics. It also offers to create interactive infographics and popups.

This tool is a video creator that will help you make powerful infographic videos. It offers infographic video templates to work with and also has Free and Paid plans.

5.2. Inspiration

Whether you’ll be using infographics software or not, you will always benefit from searching for inspiration from existing beautifully crafted infographics. Even if you don’t have a specific concept in mind, existing examples will help you build an idea.

You could check our hand-picked collection of engaging infographic examples that we made specifically for inspiration. Additionally, we also featured a gallery of 12 animated video infographics .

There are also websites such as Cool Infographics and Daily Infographics .

The most successful infographics are the most helpful ones. When you select a topic, be as specific as possible and try to offer something that your audience will hardly find anywhere else. Although your concept might center around a popular topic, try to narrow it down or something niche.

For example, you wish to make infographics about color combinations. Sure, there are plenty of those going around. However, how many color combination infographics focus on specific wedding theme pallets. These would be incredibly helpful for designers or people who have a Victorian, Gothic, or Hawaiian-themed wedding and still haven’t selected their colors. Or if you wish to educate children with fun facts, why not make an infographic about fruits, vegetables, and nuts that aren’t actually fruits, vegetables, or nuts.

5.4. Other Tips

With the tools, inspiration, and topic out of the way, let’s get in-depth with more specific tips and advice.

The Biodiversity | Infographic by Mayra Magalhaes

1. Catchy Headline

The best way to complement your topic is to present it in a catchy powerful headline. The title should give an instant clear idea of what the infographic is about and win your viewers’ curiosity. “How to write an effective college essay”, and “How to get your chainsaw cutting fast” are descriptive enough, and not only do they set the exact topic, but offer a solution.

2. Minumum Text

If you can present it in visuals, always choose that option. If a graphic feels text-heavy, this means the images don’t balance the infographic enough. The text should complement the images and reinforce them. After all, it is possible for an infographic to lack text, but not the other way around.

3. Readability

In many cases, your infographic will be downsized and this might lose the readability of your icons, images, and text. When you create infographics, check how their legibility in smaller sizes.

Sometimes you need to include a lot of facts, steps, and data. However, in many cases, a very long infographic is a deal-breaker for the viewer. Ideally, 8 000 pixels in length is more than enough for a great informative detailed graphic.

Intuitive cognitive visual flow is everything. It leads the viewer’s gaze through the story from beginning to end, from one phase to another. If the striking headline and beautiful visuals get your viewer’s attention, the flow is what will keep it.

6. One Topic

Same as having a specific topic, you should dedicate your infographic just to that topic and don’t digress with anything else. If you make an infographic about dolphins that isn’t specifically about comparing them with other mammals, you don’t need to include such comparisons.

An infographic presents data and facts, so make sure you use and cite trustworthy sources. There are a lot of questionable sources out there, so in case you aren’t presenting your own research and data, checking and double-checking will prove essential to the trustworthiness of your infographic. Cite your sources with relevant links.

If you create infographics with original research and data that is relevant to your brand, make sure you use your brand logo , colors, and other elements. This will give you and your brand exposure.

9. Promoting

Making the infographic is only half of the work. To help it go viral, however, you need to promote it by reaching out to influential sources and asking to get featured. Always include social media sharing plugins and ask your viewers to share your infographic.

Infographic by The Design Surgery

Final Words

In conclusion, if designed right, infographics are a powerful tool for communication and presentation. They present data in a condensed and highly-visual manner, that is why they have become the standard visual in content across all fields ever since the infographics boom of 2012. truth is, creating an infographic isn’t that hard, but there are some best practices and understanding you should keep in mind when you start making one. We hope we shed some light on the topic and helped you understand the anatomy of infographics. After all, knowing how something works and why, is the key to creation. That’s all for today’s review on what is an infographic.

In the meantime, just right before you start crafting the infographics for your next presentation, you could make a final stop at our hand-picked engaging infographic examples and get the inspiration and ideas you need. Or you can also check some of our other related articles:

• The Best Free Infographic Templates in 2022 for Every Software
• 30+ Free Comparison Infographic Templates: Amazing Free Collection
• The Top Infographic Design Trends

Add some character to your visuals

Cartoon Characters, Design Bundles, Illustrations, Backgrounds and more...

Like us on Facebook

Subscribe to our newsletter

Be the first to know what’s new in the world of graphic design and illustrations.

• [email protected]

Browse High Quality Vector Graphics

E.g.: businessman, lion, girl…

Related Articles

Hire freelancer or full-time employee: what’s best for your business, 16 cool apps for instagram to change your ‘gram game for the better, how to convey character’s personality through shape, variance and size, 80 illustration based web designs: mega pack, mega inspiration, how to turn yourself into animated cartoon in zoom.us, check out our infographics bundle with 500+ infographic templates:, enjoyed this article.

Don’t forget to share!

• Comments (1)

Al Boicheva

Al is an illustrator at GraphicMama with out-of-the-box thinking and a passion for anything creative. In her free time, you will see her drooling over tattoo art, Manga, and horror movies.

Thousands of vector graphics for your projects.

Hey! You made it all the way to the bottom!

Here are some other articles we think you may like:

Free Vectors

Character clipart: a collection for every taste & every project.

by Iveta Pavlova

The Best Websites to Hire Freelance Designers

by Lyudmil Enchev

Free Oktoberfest Graphics Collection to Make You See Double

by Al Boicheva

Looking for Design Bundles or Cartoon Characters?

A source of high-quality vector graphics offering a huge variety of premade character designs, graphic design bundles, Adobe Character Animator puppets, and more.

Module 3B: Statistics: Describing Data

Introduction to representing data graphically, what you’ll learn to do: identify types of real numbers and use them in algebraic expressions.

In this lesson we will present some of the most common ways data is represented graphically.  We will also discuss some of the ways you can increase the accuracy and effectiveness of graphs of data that you create.

• Learning Objectives and Introduction. Provided by : Lumen Learning. License : CC BY: Attribution
• Occupation data for Peover Inferior in 1881 as reported by the census of population. Authored by : Milesmcleod23. Located at : https://en.wikipedia.org/wiki/File:Peover_Inferior_occupational_data_graph_1881.jpg . License : CC BY-SA: Attribution-ShareAlike

Privacy Policy