the presentation of data in a pictorial or graphical format

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Data Visualization

What it is and why it matters.

Data visualization is the presentation of data in a pictorial or graphical format. It enables decision makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns. With interactive visualization, you can take the concept a step further by using technology to drill down into charts and graphs for more detail, interactively changing what data you see and how it’s processed.

• Today's World
• How It's Used
• How It Works

History of Data Visualization

The concept of using pictures to understand data has been around for centuries, from maps and graphs in the 17th century to the invention of the pie chart in the early 1800s. Several decades later, one of the most cited examples of statistical graphics occurred when Charles Minard mapped Napoleon’s invasion of Russia. The map depicted the size of the army as well as the path of Napoleon’s retreat from Moscow – and tied that information to temperature and time scales for a more in-depth understanding of the event.

It’s technology, however, that truly lit the fire under data visualization. Computers made it possible to process large amounts of data at lightning-fast speeds. Today, data visualization has become a rapidly evolving blend of science and art that is certain to change the corporate landscape over the next few years.

Data visualization: A wise investment in your big data future

With big data there’s potential for great opportunity, but many retail banks are challenged when it comes to finding value in their big data investment. For example, how can they use big data to improve customer relationships? How – and to what extent – should they invest in big data?

In this Q&A with Simon Samuel, Head of Customer Value Modeling for a large bank in the UK, we examine these and other big data issues that confront retail bankers.

Why is data visualization important?

Because of the way the human brain processes information, using charts or graphs to visualize large amounts of complex data is easier than poring over spreadsheets or reports. Data visualization is a quick, easy way to convey concepts in a universal manner – and you can experiment with different scenarios by making slight adjustments.

Data visualization can also:

• Identify areas that need attention or improvement.
• Clarify which factors influence customer behavior.
• Help you understand which products to place where.
• Predict sales volumes.

Data Visualization in Today’s World

What’s the impact that data visualization has had in the corporate world – and what’s in store for the future? Here’s what the experts are saying.

Techniques for data visualization

A picture is worth a thousand words – especially when you’re trying to find relationships and understand your data, which could include thousands or even millions of variables. This white paper provides some basic tips and techniques for creating meaningful visuals of your data.

Explore techniques

Data visualization is going to change the way our analysts work with data. They’re going to be expected to respond to issues more rapidly. And they’ll need to be able to dig for more insights – look at data differently, more imaginatively. Data visualization will promote that creative data exploration. Simon Samuel Head of Customer Value Modeling for a large bank in the UK

SAS ® Visual Analytics

Data visualization technology from sas delivers fast answers to complex questions, regardless of the size of your data., how is it being used.

Regardless of industry or size, all types of businesses are using data visualization to help make sense of their data. Here’s how.

Comprehend information quickly

By using graphical representations of business information, businesses are able to see large amounts of data in clear, cohesive ways – and draw conclusions from that information. And since it’s significantly faster to analyze information in graphical format (as opposed to analyzing information in spreadsheets), businesses can address problems or answer questions in a more timely manner.

Identify relationships and patterns

Even extensive amounts of complicated data start to make sense when presented graphically; businesses can recognize parameters that are highly correlated. Some of the correlations will be obvious, but others won’t. Identifying those relationships helps organizations focus on areas most likely to influence their most important goals.

Pinpoint emerging trends

Using data visualization to discover trends – both in the business and in the market – can give businesses an edge over the competition, and ultimately affect the bottom line. It’s easy to spot outliers that affect product quality or customer churn, and address issues before they become bigger problems.

Communicate the story to others

Once a business has uncovered new insights from visual analytics, the next step is to communicate those insights to others. Using charts, graphs or other visually impactful representations of data is important in this step because it’s engaging and gets the message across quickly.

How it Works

Data visualization in action.

While it may be easy to grasp the concept that data visualization helps you make sense of large amounts of data, it's not as easy to understand what happens next. What type of technology do you need, and how do you use it?

This practical video gives you an overview of SAS Visual Analytics and SAS Visual Statistics, demonstrating how it's possible to explore billions of rows of data in seconds, using different configurations. SAS technology helps you prepare data, create reports and graphs, discover new insights and share those visualizations with others via the Web, PDFs or mobile devices.

Laying the groundwork for data visualization

Before implementing new technology, there are some steps you need to take. Not only do you need to have a solid grasp on your data, you also need to understand your goals, needs and audience. Preparing your organization for data visualization technology requires that you first:

• Understand the data you’re trying to visualize, including its size and cardinality (the uniqueness of data values in a column).
• Determine what you’re trying to visualize and what kind of information you want to communicate.
• Know your audience and understand how it processes visual information.
• Use a visual that conveys the information in the best and simplest form for your audience.

Once you've answered those initial questions about the type of data you have and the audience who'll be consuming the information, you need to prepare for the amount of data you'll be working with. Big data brings new challenges to visualization because large volumes, different varieties and varying velocities must be taken into account. Plus, data is often generated faster that it can be managed and analyzed.

There are factors you should consider, such as the cardinality of columns you’re trying to visualize. High cardinality means there’s a large percentage of unique values (e.g., bank account numbers, because each item should be unique). Low cardinality means a column of data contains a large percentage of repeat values (as might be seen in a “gender” column).

Deciding which visual is best

One of the biggest challenges for business users is deciding which visual should be used to best represent the information. SAS Visual Analytics uses intelligent autocharting to create the best possible visual based on the data that is selected.

When you’re first exploring a new data set, autocharts are especially useful because they provide a quick view of large amounts of data. This data exploration capability is helpful even to experienced statisticians as they seek to speed up the analytics lifecycle process because it eliminates the need for repeated sampling to determine which data is appropriate for each model.

Read More About This Topic

What is Data Visualization? Definition, Examples, Best Practices

This guide provides an introduction to data visualization, including real-world examples, best practices and editable templates.

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Resource Details

June 5, 2020

Data visualization is the visual presentation of data or information. The goal of data visualization is to communicate data or information clearly and effectively to readers. Typically, data is visualized in the form of a chart, infographic, diagram, or map.

The field of data visualization combines both art and data science. While data visualization can be creative and pleasing to look at, it should also be functional in its visual communication of the data.

This resource explains the fundamentals of data visualization, including examples of different types of data visualizations and when and how to use them to illustrate findings and insights.

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Related Guides & Resources

Getting started with data visualization.

Visualizing data is one of the most effective ways of communicating data. This can take many forms from live digital data dashboards to static charts shared in social media channels.

Communicating Results: Design your Visualization

Let’s get visual: nonprofit data visualization.

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Data visualization: about.

• Choosing a Chart Type
• Design Considerations

What is data visualization?

Data visualization is the presentation of data in a pictorial or graphical format. A well-designed figure can have a huge impact on the communication of research results. Data visualizations can include word clouds, bar charts, maps, or even simple tables. 

According to Noah Illinsky at IBM's Center for Advanced Visualization, a successful visualization:

• Has clear purpose (why this visualization)
• Includes only the relevant content (what are you visualizing)
• Uses appropriate structure (how are you visualizing it)
• Has useful formatting  (everything else)

Read more about The Four Pillars of Visualization .

Why Visualize?

• Visualizations reveal patterns in data. See Anscombe's Quartet as an example - the quartet consists of four datasets that have nearly identical statistical properties; however, when graphed in scatter plots, they reveal four distinct patterns.
• They help us make comparisons . Bar charts, grouped bar charts, and histograms are good examples of visualizations that allow for easy comparisons. A well-crafted visualization will enable you to quickly compare one variable (or a set of variables) against another.
• They enable us to discover new information. Data in its raw or even in its cleaned form makes it difficult or nearly impossible to discover new information, trends, or correlations.
• They enable us to comprehend massive amounts of data . See the visualization of flight patterns  in the US by Aaron Koblin, part of the Celestial Mechanics project at UCLA. 

Types of Visualizations

• A Tour through the Visualization Zoo
• Visualization Types
• Data Visualisation Catalogue

Preparing Data for Visualization

Data visualization can reveal problems in your data collection if it hasn't been cleaned properly. You don't want to make assumptions that aren't warranted! Read Hadley Wickham's " Tidy Data " to learn more about cleaning your data.

• Next: Choosing a Chart Type >>
• Last Updated: Mar 12, 2024 11:34 AM
• URL: https://guides.lib.berkeley.edu/data-visualization

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Data Visualization: Resources for Teaching, Learning, and Research

Common tools used for data visualization include R and Python, third-party applications like Tableau , Gephi , and Voyant Tools , JavaScript libraries like D3  and temporal, geospatial, and exhibition tools like Omeka, StoryMap, Timeline.js, WorldMap, and Carto. Resources for data visualization and data science training and assistance are available from several organizations around campus, including (but not limited to) Academic Technology for FAS , the Harvard Library, Research Computing in the Arts and Humanities , and the Institute for Quantitative Social Science .

Access to a suite of visualization applications, and to assistance with data visualization, is available on computers in the Multimedia Lab at Lamont Library, while workshops on approaches and tools used for effective visualization are frequently offered to faculty, staff, teaching fellows, and students by a combination of groups on campus. For more information on the digital toolkit or on visualization resources in general, please contact AT-FAS at [email protected] .

Workshop Example

The following is an excerpt from a  Harvard Gazette article about the two-day workshop "Thinking With Your Eyes: Visualizing the Arts, Humanities, and Sciences," held in February 2014 and sponsored by the interdisciplinary Digital Futures Consortium :

It seems like big data is everywhere you look. And in a way, it is: Maps, medical scans, and weather charts are commonplace forms of data visualization. Each was examined during “Thinking with Your Eyes,” a two-day conference that brought together experts in the arts, sciences, humanities, and technology — as well as academic and computing groups from across Harvard — to investigate how graphic representation brings knowledge to life. “In a technological age where large amounts of data can be captured like never before, how big data is used and portrayed presents significant challenges,” said keynote speaker Martin Wattenberg, who along with Fernanda Viégas leads Google’s “Big Picture” visualization research group. As presenters acknowledged the long and cross-cultural history of visual representation, it was often in the context of seeking new ways to make information more memorable. Read more...

Faculty Research Examples (provided by Research Computing in the Arts and Humanities)

Steven Clancy, Senior Lecturer on Slavic Languages and Literatures / Director of the Slavic Language Program

Russian Modules is a Russian language textbook currently under development. It makes use of the Neo4j graph database to support the visualization of Russian lemmas, both within context and in isolation. Additional functionality comes from D3js, a data visualization library that displays words within the database in the form of a series of force layouts. The goal of the project is to tie the graph database into the entirety of the book in order to create a unique interactive environment for learning. This involves the ability to highlight terms, explore meanings, noun declensions, and verb conjugations. In addition, this will allow for curriculum planning by analysing Russian language texts for their difficulty (as assigned by Steven and his colleagues). Copying and pasting text into the text analysis tool will highlight words based on word difficulty as it appears in the larger Russian language curriculum.

Malika Zeghal, Prince Alwaleed Bin Talal Professor in Contemporary Islamic Thought and Life in the Department of Near Eastern Languages and Civilization

Malika Zeghal is the principal investigator for Afkar, the Arabic word for ideas. Her project endeavors to trace Muslim intellectual networks during the interwar period of the early twentieth century. The research team began by parsing the content of various religious journals in the Middle East, beginning in Cairo, but expanding outward as far as Paris and the Philippines. This information will be used to create a dynamic world map that will display the provenance and movement of fatwa requests included in the journals. The goal is to display the various paths of knowledge contained within each of the journals, and to begin asking questions about the various imaginary networks (e.g. transcontinental intellectual communities) that existed at the time.

Research Visualization Example: Visualisation des Billets Vendus

Based on the research of Pannill Camp, Associate Professor of Drama at Washington University at St. Louis, Juliette Cherbuliez, Associate Professor of French at the University of Minnesota, and Derek Miller, Assistant Professor of English at Harvard University,  Visualisation des Billets Vendus   is a data interactive created by Christophe Schuwey, Lecturer at Université de Fribourg (Switzerland), and Christopher Morse, Senior Research Computing Specialist with Harvard's Research Computing in the Arts and Humanities group that reveals ticket sales at performances during the 1784-1785 season at the Odéon-Théâtre de l’Europe in Paris. 

Visualisation des Billets Vendus, while still in its early stages, has been an interesting thought experiment in theater representation and history, and presents a number of unique challenges. For example, how should one visualize a theater? Does it suffice to abstract a theater into shapes like a seating chart one might see on a website like Ticketmaster? What can be learned (or not) by specificity, that is to say, by attempting to recreate each individual seating area, or even each seat? Moving forward, the visualization seeks to encompass the entirety of the Comédie-Française registers collection, totaling over one hundred years of ticket sales, and various user interface improvements over time will make it easier for users to work with the heat map in more detail.

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What is data visualization? Presenting data for decision-making

Data visualization is the presentation of data in a graphical format to make it easier for decision makers to see and understand trends, outliers, and patterns in data..

Data visualization definition

Data visualization is the presentation of data in a graphical format such as a plot, graph, or map to make it easier for decision makers to see and understand trends, outliers, and patterns in data.

Maps and charts were among the earliest forms of data visualization. One of the most well-known early examples of data visualization was a flow map created by French civil engineer Charles Joseph Minard in 1869 to help understand what Napoleon’s troops suffered in the disastrous Russian campaign of 1812. The map used two dimensions to depict the number of troops, distance, temperature, latitude and longitude, direction of travel, and location relative to specific dates.

Today, data visualization encompasses all manners of presenting data visually, from dashboards to reports, statistical graphs, heat maps, plots, infographics, and more.

What is the business value of data visualization?

Data visualization helps people analyze data, especially large volumes of data, quickly and efficiently.

By providing easy-to-understand visual representations of data, it helps employees make more informed decisions based on that data. Presenting data in visual form can make it easier to comprehend, enable people to obtain insights more quickly. Visualizations can also make it easier to communicate those insights and to see how independent variables relate to one another. This can help you see trends, understand the frequency of events, and track connections between operations and performance, for example.

Key data visualization benefits include:

• Unlocking the value big data by enabling people to absorb vast amounts of data at a glance
• Increasing the speed of decision-making by providing access to real-time and on-demand information
• Identifying errors and inaccuracies in data quickly

What are the types of data visualization?

There are myriad ways of visualizing data, but data design agency The Datalabs Agency breaks data visualization into two basic categories:

• Exploration: Exploration visualizations help you understand what the data is telling you.
• Explanation: Explanation visualizations tell a story to an audience using data .

It is essential to understand which of those two ends a given visualization is intended to achieve. The Data Visualisation Catalogue , a project developed by freelance designer Severino Ribecca, is a library of different information visualization types.

Some of the most common specific types of visualizations include:

2D area: These are typically geospatial visualizations. For example, cartograms use distortions of maps to convey information such as population or travel time. Choropleths use shades or patterns on a map to represent a statistical variable, such as population density by state.

Temporal: These are one-dimensional linear visualizations that have a start and finish time. Examples include a time series, which presents data like website visits by day or month, and Gantt charts, which illustrate project schedules.

Multidimensional: These common visualizations present data with two or more dimensions. Examples include pie charts, histograms, and scatter plots.

Hierarchical: These visualizations show how groups relate to one another. Tree diagrams are an example of a hierarchical visualization that shows how larger groups encompass sets of smaller groups.

Network: Network visualizations show how data sets are related to one another in a network. An example is a node-link diagram, also known as a network graph , which uses nodes and link lines to show how things are interconnected.

What are some data visualization examples?

Tableau has collected what it considers to be 10 of the best data visualization examples . Number one on Tableau’s list is Minard’s map of Napoleon’s march to Moscow, mentioned above. Other prominent examples include:

• A dot map created by English physician John Snow in 1854 to understand the cholera outbreak in London that year. The map used bar graphs on city blocks to indicate cholera deaths at each household in a London neighborhood. The map showed that the worst-affected households were all drawing water from the same well, which eventually led to the insight that wells contaminated by sewage had caused the outbreak.
• An animated age and gender demographic breakdown pyramid created by Pew Research Center as part of its The Next America project , published in 2014. The project is filled with innovative data visualizations. This one shows how population demographics have shifted since the 1950s, with a pyramid of many young people at the bottom and very few older people at the top in the 1950s to a rectangular shape in 2060.
• A collection of four visualizations by Hanah Anderson and Matt Daniels of The Pudding that illustrate gender disparity in pop culture by breaking down the scripts of 2,000 movies and tallying spoken lines of dialogue for male and female characters. The visualizations include a breakdown of Disney movies, the overview of 2,000 scripts, a gradient bar with which users can search for specific movies, and a representation of age biases shown toward male and female roles.

Data visualization tools

Data visualization software encompasses many applications, tools, and scripts. They provide designers with the tools they need to create visual representations of large data sets. Some of the most popular include the following:

Domo: Domo is a cloud software company that specializes in business intelligence tools and data visualization. It focuses on business-user deployed dashboards and ease of use, making it a good choice for small businesses seeking to create custom apps.

Dundas BI: Dundas BI is a BI platform for visualizing data, building and sharing dashboards and reports, and embedding analytics.

Infogram: Infogram is a drag-and-drop visualization tool for creating visualizations for marketing reports, infographics, social media posts, dashboards, and more. Its ease-of-use makes it a good option for non-designers as well.

Klipfolio: Klipfolio is designed to enable users to access and combine data from hundreds of services without writing any code. It leverages pre-built, curated instant metrics and a powerful data modeler, making it a good tool for building custom dashboards.

Looker: Now part of Google Cloud, Looker has a plug-in marketplace with a directory of different types of visualizations and pre-made analytical blocks. It also features a drag-and-drop interface.

Microsoft Power BI: Microsoft Power BI is a business intelligence platform integrated with Microsoft Office. It has an easy-to-use interface for making dashboards and reports. It’s very similar to Excel so Excel skills transfer well. It also has a mobile app.

Qlik: Qlik’s Qlik Sense features an “associative” data engine for investigating data and AI-powered recommendations for visualizations. It is continuing to build out its open architecture and multicloud capabilities.

Sisense: Sisense is an end-to-end analytics platform best known for embedded analytics. Many customers use it in an OEM form.

Tableau: One of the most popular data visualization platforms on the market, Tableau is a platform that supports accessing, preparing, analyzing, and presenting data. It’s available in a variety of options, including a desktop app, server, and hosted online versions, and a free, public version. Tableau has a steep learning curve but is excellent for creating interactive charts.

Data visualization certifications

Data visualization skills are in high demand. Individuals with the right mix of experience and skills can demand high salaries. Certifications can help.

Some of the popular certifications include the following:

• Data Visualization Nanodegree (Udacity)
• Professional Certificate in IBM Data Science (IBM)
• Data Visualization with Python (DataCamp)
• Data Analysis and Visualization with Power BI (Udacity)
• Data Visualization with R (Dataquest)
• Visualize Data with Python (Codecademy)
• Professional Certificate in Data Analytics and Visualization with Excel and R (IBM)
• Data Visualization with Tableau Specialization (UCDavis)
• Data Visualization with R (DataCamp)
• Excel Skills for Data Analytics and Visualization Specialization (Macquarie University)

Data visualization jobs and salaries

Here are some of the most popular job titles related to data visualization and the average salary for each position, according to data from PayScale .

• Data analyst: $64K
• Data scientist: $98K
• Data visualization specialist: $76K
• Senior data analyst: $88K
• Senior data scientist: $112K
• BI analyst: $65K
• Analytics specialist: $71K
• Marketing data analyst: $61K

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Educational resources and simple solutions for your research journey

Data Visualization: How to Present Your Research Data Visually

The academic and scientific community produces large amounts of data through their research, and data visualization is a key skill for researchers across all disciplines. Presenting qualitative data visually from one’s research can transform the final output, and one does not have to spend enormous amount of time to accomplish this. Researchers who need to communicate quantitative data have several options to present it visually through bar graphs, pie charts, histograms and even infographics. However, communicating research findings based on complex datasets is not always easy. This is where effective data visualization can greatly help readers. Data visualization is the representation of information and data in a pictorial or graphical format highlighting the trends and outliers and making it easier to understand. Effective use of data visualization techniques helps to focus readers’ attention on critical information, in a way is both simple and engaging.

Many researchers are often under the false impression that presenting qualitative data visually is a complex exercise, but it is not. Today, there are numerous qualitative data visualization tools and techniques that can be used by both beginners and experts alike. Using data visualization to communicate important data can enhance the impact of the researcher’s work thereby making it more meaningful and engaging to potential readers. Adding visuals like graphs and charts can make it easier for not just for researchers to communicate experimental results and share details of interesting new discoveries but also allows readers to analyze and understand complicated data sets and concepts. A study in The Economist revealed the number of citations jumped 120% when a research paper included infographics. 1

Let us look at some ways to make the best use of data visualization techniques that will help present your qualitative research data and information effectively.

Table of Contents

Pair the right kind of visuals to convey specific data sets

The human brain is wired to quickly analyze and understand visual cues which is why it is important to add visual elements in your research paper. Plotting data using charts, graphs and infographics allows readers to quickly identify underlying patterns of data that would otherwise be difficult to comprehend when reading through paragraphs of text. However, using the wrong kind of visuals can be misleading, and reduce readability and comprehension.

Today, with more and more scientific knowledge being conveyed visually via social media, researchers are experimenting with communicating research through different types of visuals. However, using the right kind of visuals is imperative to ease the public understanding of science.

In fact, an interesting study in the Journal of the American Statistical Association by William Cleveland and Robert McGill sheds light on how human perception affects how we decipher graphic displays of data; this made some kinds of charts easier to understand than others. The statisticians were able to prove that charts based on the lengths of bars or lines, such as in a standard bar chart were the easiest to read – especially, when trying to discern small differences between values. Pie charts on the other hand were acceptable only in limited contexts so were rarely the right choice. 2

Focus on balancing form and function

There are several data visualization software and programs available to researchers today that simplify the process of presenting data visually. What needs to be kept in mind, however, is that quantitative data visualization is not just about beautifying graphs to make them look better. Neither is it about heaping information into an infographic to communicate different aspects of research. Researchers must understand that to be effective, data visualization must be a delicate balance between form and function. While a plain graph could be too boring to attract and hold reader attention a beautiful visual could also fail in its endeavor to communicate the right message if it is not presented properly. The data and the visuals need to work together to create a compelling narrative that combines research analysis with storytelling.

Be aware of the use of colors and shapes

While incorporating quantitative data visualization techniques it is recommended not to rely only on the default templates available within software and programs but instead customize and define the layout according to your specific needs. Choosing the right colors and shapes to indicate various categories is very important. For example, it would be appropriate to use light colored text on a darker background to enhance readability. Researchers can also choose to use different shapes to indicate separate data sets and categories of information and use varying sizes to stress the frequency of data. One can also use similar vectors to add a touch of ingenuity to the visuals being used. It is best to avoid using vivid effects and abstract images for denoting differences in data as they can distract readers from key information points being conveyed.  Most importantly, avoid adding too many visuals or graphics. Researchers must use visuals only where necessary and align them with the information provided.

While many early career researchers and academics might find it challenging to visualize qualitative data and present it in a manner that is easily understood by an audience, this is an art that should be cultivated and can be mastered. This is where Mind The Graph comes in help researchers get more creative with science communication by offering them a simple way to present data visually. A powerful AI tool for scientists, Mind The Graph is home to the world’s largest scientifically accurate illustrations gallery with over 40,000 illustrations across more than 80 research fields. Choose a format to communicate your research and use one of the 200+ pre-made templates to create powerful scientific infographics, posters, graphical abstracts, and presentations for your own research. It’s a quick, effective, and powerful way to communicate your science to the world. And the best part is that you can now get Mind The Graph at a steal by subscribing to Researcher.Life , which unlocks access to this data visualization tool as well as other premium AI tools and services designed to help you succeed.

References:

• Graphic Details, The Economist, June 2016. Available at https://www.economist.com/science-and-technology/2016/06/16/graphic-details
• Mason, B. Why scientists need to be better at data visualization. Knowable Magazine, 2019. Available at https://knowablemagazine.org/article/mind/2019/science-data-visualization

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Graphical Representation of Data

Graphical representation of data is an attractive method of showcasing numerical data that help in analyzing and representing quantitative data visually. 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. Graphical representation of data is done through different mediums such as lines, plots, diagrams, etc. Let us learn more about this interesting concept of graphical representation of data, the different types, and solve a few examples.

Definition of Graphical Representation of Data

A graphical representation is a visual representation of data statistics-based results using graphs, plots, and charts. This kind of representation is more effective in understanding and comparing data than seen in a tabular form. Graphical representation helps to qualify, sort, and present data in a method that is simple to understand for a larger audience. Graphs enable in studying the cause and effect relationship between two variables through both time series and frequency distribution. The data that is obtained from different surveying is infused into a graphical representation by the use of some symbols, such as lines on a line graph, bars on a bar chart, or slices of a pie chart. This visual representation helps in clarity, comparison, and understanding of numerical data.

Representation of Data

The word data is from the Latin word Datum, which means something given. The numerical figures collected through a survey are called data and can be represented in two forms - tabular form and visual form through graphs. Once the data is collected through constant observations, it is arranged, summarized, and classified to finally represented in the form of a graph. There are two kinds of data - quantitative and qualitative. Quantitative data is more structured, continuous, and discrete with statistical data whereas qualitative is unstructured where the data cannot be analyzed.

Principles of Graphical Representation of Data

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 -axis and y-axis intersect each other at the origin it divides the plane into four parts which are called Quadrant I, Quadrant II, Quadrant III, Quadrant IV. This form of representation is seen in a frequency distribution that is represented in four methods, namely Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Advantages and Disadvantages of Graphical Representation of Data

Listed below are some advantages and disadvantages of using a 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.
• It is mainly used in statistics to determine the mean, median, and mode for different data

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 represent it graphically.

Rules of Graphical Representation of Data

While presenting data graphically, there are certain rules that need to be followed. They are listed below:

• Suitable Title: The title of the graph should be appropriate that indicate the subject of the presentation.
• Measurement Unit: The measurement unit in the graph should be mentioned.
• Proper Scale: A proper scale needs to be chosen to represent the data accurately.
• Index: For better understanding, index the appropriate colors, shades, lines, designs in the graphs.
• Data Sources: Data should be included wherever it is necessary at the bottom of the graph.
• Simple: The construction of a graph should be easily understood.
• Neat: The graph should be visually neat in terms of size and font to read the data accurately.

Uses of Graphical Representation of Data

The main use of a graphical representation of data is understanding and identifying the trends and patterns of the data. It helps in analyzing large quantities, comparing two or more data, making predictions, and building a firm decision. The visual display of data also helps in avoiding confusion and overlapping of any information. Graphs like line graphs and bar graphs, display two or more data clearly for easy comparison. This is important in communicating our findings to others and our understanding and analysis of the data.

Types of Graphical Representation of Data

Data is represented in different types of graphs such as plots, pies, diagrams, etc. They are as follows,

Related Topics

Listed below are a few interesting topics that are related to the graphical representation of data, take a look.

• x and y graph
• Frequency Polygon
• Cumulative Frequency

Examples on Graphical Representation of Data

Example 1 : A pie chart is divided into 3 parts with the angles measuring as 2x, 8x, and 10x respectively. Find the value of x in degrees.

We know, the sum of all angles in a pie chart would give 360º as result. ⇒ 2x + 8x + 10x = 360º ⇒ 20 x = 360º ⇒ x = 360º/20 ⇒ x = 18º Therefore, the value of x is 18º.

Example 2: Ben is trying to read the plot given below. His teacher has given him stem and leaf plot worksheets. Can you help him answer the questions? i) What is the mode of the plot? ii) What is the mean of the plot? iii) Find the range.

Solution: i) Mode is the number that appears often in the data. Leaf 4 occurs twice on the plot against stem 5.

Hence, mode = 54

ii) The sum of all data values is 12 + 14 + 21 + 25 + 28 + 32 + 34 + 36 + 50 + 53 + 54 + 54 + 62 + 65 + 67 + 83 + 88 + 89 + 91 = 958

To find the mean, we have to divide the sum by the total number of values.

Mean = Sum of all data values ÷ 19 = 958 ÷ 19 = 50.42

iii) Range = the highest value - the lowest value = 91 - 12 = 79

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Practice Questions on Graphical Representation of Data

Faqs on graphical representation of data, what is graphical representation.

Graphical representation is a form of visually displaying data through various methods like graphs, diagrams, charts, and plots. It helps in sorting, visualizing, and presenting data in a clear manner through different types of graphs. Statistics mainly use graphical representation to show data.

What are the Different Types of Graphical Representation?

The different types of graphical representation of data are:

• Stem and leaf plot
• Scatter diagrams
• Frequency Distribution

Is the Graphical Representation of Numerical Data?

Yes, these graphical representations are numerical data that has been accumulated through various surveys and observations. The method of presenting these numerical data is called a chart. There are different kinds of charts such as a pie chart, bar graph, line graph, etc, that help in clearly showcasing the data.

What is the Use of Graphical Representation of Data?

Graphical representation of data is useful in clarifying, interpreting, and analyzing data plotting points and drawing line segments , surfaces, and other geometric forms or symbols.

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, organization, and analysis of data as part of the process of a scientific study.

What is the Objective of Graphical Representation of Data?

The main objective of representing data graphically is to display information visually that helps in understanding the information efficiently, clearly, and accurately. This is important to communicate the findings as well as analyze the data.

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Data Visualization

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Textual Analysis Tools

• Voyant A web-based reading and analysis environment for digital texts.
• JSTOR Text Analyzer Allows you to map a document to JSTOR's content.
• Google Books Ngram Viewer A web application that displays the usage of words or phrases over time, sampled from the millions of books that Google has scanned.
• HathiTrust Bookworm
• Venn Diagrams HeinOnline includes a new feature, which uses venn diagrams to facilitate easier, more organized research with databases.

Mapping Tools

• PolicyMap A web-based GIS-light mapping tool for US economic, health and education data. Seton Hall has a subscription, although a public version is available.
• Tableau Public Helps you create and share interactive charts and graphs, maps, and dashboards
• Tour Builder Uses Google Earth.
• Excel Power Map Allows you to map out data on a 3D globe/map.
• Power BI Business analytics tool that changes data information into visual designs.

Data Visualization Tools

• Canva Enables the creation of infographics with drag and drop functionality.
• RAW Graphs Transforms complex data into a visual representation.

Getting Started

What is data visualization? Data visualization is the presentation of data in a pictorial or graphical format.  Think maps or infographics.  Many steps past PowerPoint integrating rules of mathematics and psychology for stronger data analysis and interpretation..

How does this differ from statistics?   Statistics are tables or reports you may find in an article or online.  They provide an interpretation of data, but data visualization takes you a step beyond.  It allows you to display information in a graphical format.

So then what is data ?  View data as the raw material for your research.  You may create original data in a survey or view data online about topics ranging from the US Census, literacy rates or global heal

How can this help my research as a student?   You can set yourself apart from your peers by generating an interesting infographic or map.  For example, you may have heard of GIS (Geographic Information Systems). These systems and those listed below let us question, analyze, and interpret data to understand relationships, patterns, and trends.  You can add an electronic module to a research paper or a thesis.  This can be a conversation starter for a graduate school or job application.  There may be a digital storytelling opportunity in your dissertation.

How will this help after Seton Hall?   Analyzing a data set in any field, a government policy or part of an economic crisis is a life skill.  In addition to good writing and citing habits, being able to interpret and articulate trends and relationships about a subject will help you succeed whether you become a teacher, a PhD student or a stockbroker. Let us help you now!

Great Resources

• "How We Helped Our Reporters Learn to Love Spreadsheets" Free training and tip sheets from the New York Times
• FlowingData Explores how statisticians, designers, data scientists, and others use analysis, visualization, and exploration to understand data and ourselves.
• Spurious Correlations Project designed as a fun way to look at correlations and to think about data. The aim of this project is to foster interest in statistics and numerical research.
• FiveThirtyEight
• NodeXL NodeXL Basic and NodeXL Pro are add-ins for Microsoft® Excel® (2007, 2010, 2013, 2016) that support social network and content analysis.

• Coggle Allows you to create collaborative flowcharts and mind maps.

Our Collection

Edward R. Tufte has created important books about data visuaization.  We have several books here in the library including

• The visual display of quantitative information
• Envisioning information
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Data visualization is the presentation of data in a pictorial or graphical format. It enables decision makers to see analytics presented visually, so they can grasp difficult concepts or identify new patterns.

Popular Visualization Tools

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Data Visualization.  https://www.sas.com/en_us/insights/big-data/data-visualization.html

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Visual Communication: Data Visualization

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What is Data Visualization?

"Data visualization is the presentation of data in a pictorial or graphical format. It enables decision makers to see analytics  presented visually, so they can grasp difficult concepts or identify new patterns. With interactive visualization, you can take the concept a step further by using technology to drill down into charts and graphs for more detail, interactively changing what data you see and how it’s processed." - SAS

Tools for Visualizing Data

• Datawrapper Create fast, easily digestible visualizations.
• Leaflet An open-source JavaScript library for mobile-friendly interactive maps.
• OpenRefine “OpenRefine (previously Google Refine) is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.”
• Quartz chart builder Allows you to build a line chart quickly.
• TableauPublic A free tool for visualizing data in a wide variety of design options.

" R is a language and environment for statistical computing and graphics."  - R Project

• CRAN The Comprehensive R Archive Network
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Data Visualization Tips

• 10 Data Visualization Tips From Canada's International Development Research Centre, an infographic with 10 tips for creating a visualization.
• 10 Dashboard Design Errors 10 common errors to avoid when creating a dashboard presentation.

"Python is an interpreted, object-oriented, high-level programming language with dynamic semantics." - Python

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• Python Tutorial Tutorials on Python from w3schools.com
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• Australian National Corpus The Australian National Corpus is a discovery service that collates and provides access to assorted examples of Australian English text, transcriptions, audio and audio-visual materials.
• British National Corpus The British National Corpus (BNC) was originally created by Oxford University press in the 1980s - early 1990s, and it contains 100 million words of text from a wide range of genres (e.g. spoken, fiction, magazines, newspapers, and academic).
• Corpus of Contemporary American English The corpus contains more than one billion words of text (25+ million words each year 1990-2019) from eight genres: spoken, fiction, popular magazines, newspapers, academic texts, and (with the update in March 2020): TV and Movies subtitles, blogs, and other web pages.
• Corpus of Español This corpus contains about two billion words of Spanish, taken from about two million web pages from 21 different Spanish-speaking countries from the past three to four years. The corpus has been funded by the US National Endowment for the Humanities, and it has allowed us to update the original Corpus del Español (2002), which was also funded by the NEH.
• Corpus of Spanish in Georgia The Corpus of Spanish in Georgia (CSG) is a collection of semi-structured interviews conducted in 2015 with members of the Latinx immigrant community in the metropolitan Atlanta area, primarily in the city of Roswell, Georgia.
• Corpus of Spontaneous Japanese The "Corpus of Spontaneous Japanese" (or CSJ) is a database containing a large collection of Japanese spoken language data and information for use in linguistic research; jointly developed by NINJAL, NICT and the Tokyo Institute of Technology, the CSJ is world-class in both the quantity and quality of the available data.
• English Corpora 10 Most Widely Used Page has links to the most widely used corpora online.
• English Corpora NOW The NOW corpus (News on the Web) contains 9.0 billion words of data from web-based newspapers and magazines from 2010 to the present time.
• French Treebank A lexcical and syntactic resource richly annotated (and validated manually) for linguists, NLP-ready.
• Open Language Archives Community This catalog, developed by the Open Language Archives Community (OLAC), provides access to a wealth of information about thousands of languages, including details of text collections, audio recordings, dictionaries, and software, sourced from dozens of digital and traditional archives. Contains a large number of languages.

" D3.js  is a JavaScript library for manipulating documents based on data." - D3.js

• Data Driven Documents "D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS. D3’s emphasis on web standards gives you the full capabilities of modern browsers without tying yourself to a proprietary framework, combining powerful visualization components and a data-driven approach to DOM manipulation. " (D3)

NASA Jet Propulsion Lab Mission history, created in 2012

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Techniques for Data Visualization and Reporting

Data analytics is the science by which experts analyze massive amounts of raw data in order to make conclusions about information. It has exploded in popularity as businesses seek new ways to understand their clients and optimize performance. According to Precedence Research, the global market for data analytics is predicted to surge from $30 billion in 2022 to $393 billion in 2032. 1 As this market grows, effective data visualization–”the presentation of data in a pictorial or graphical format” 2 –will become increasingly important.

While the definition can vary depending on context and application, the term ‘data’ usually refers to “raw, unprocessed information that is not recognized as having any meaning.” 2 Data can be organized in many forms, including name, numbers, age, signs, characters, symbols, files, reports, and graphs.

Through data visualization, people present complex data in graphical representations: interactive charts, dashboards, pie charts, and so on. These displays enable business analysts to tell interesting stories with data and share their findings with diverse audiences. 2 However, creating clear and engaging data visualizations requires the right skills and tools. This article explores data visualization best practices for business and analytics professionals .

Understanding Data Visualization

Data visualization uses digital tools to present substantial amounts of information in a graphic format. Business analysts manipulate these visual representations to explore data sets and identify patterns. They also use visualizations to explain their findings to company leaders, stakeholders, and other audiences. 2

Common types of data visualization include: 2

• Scatter-plots: Graphs with at least two variables plotted along an x and y axis
• Simulations
• Trees: Graphics that draw hierarchical connections between ideas
• Waveforms: Graphs showing waves that represent change over time

Good data visualization allows business analysts to identify and correct problems in data sets quickly, so they don’t draw incorrect conclusions, and to process large amounts of information and gain insights more efficiently. 2

Choosing the Right Data Presentation and Reporting Techniques

As a business analyst, you can use many data visualization techniques to report and represent information in a visual format. It’s best to determine which methods to use based on the kind of data you’re examining and the goals of the analysis. 2

Common data presentation methods and their uses include: 2

• Maps represent relationships between geographic locations or objects
• Tables use columns and rows to display quantitative or qualitative data
• Tree maps depict hierarchical relationships between different categories of data

After you select a format, you’ll also need to consider other visual elements such as color and labels. Common color-related data visualization methods include: 3

• Sequential: Colors go from light to dark to show increasing values
• Diverging: Two contrasting colors represent two extremes
• Qualitative: Colors have no symbolic meaning but highlight different categories of information

Additionally, it's especially helpful in presenting data to use detailed captions and labels to explain the information in greater detail. 3

Data Visualization Tools

A data visualization tool is the software that generates the desired presentations. 2 Business analysts use a variety of data reporting and visualization tools to represent and analyze information. Popular software includes:

Microsoft Power BI allows analysts to gather, clean, and look at structured and unstructured data. It also features artificial intelligence (AI) tools, which use complex algorithms to mimic human thought and help users derive fresh insights from data. 4

Tableau is a platform that offers data management, analytics, and visualization tools. The company’s products enable users to transform data into bar charts, graphs, heat maps, and other representations. Tableau tools also include features for collaboration so that analysts can work together to analyze and visualize data. 5

Strategies for Effective Data Storytelling

Data visualizations can help audiences better understand data sets, such as a company’s financial records and survey responses from clients. However, most people need additional context to get the full picture. Business analysts use data storytelling to create an engaging story about the data, explain the meaning of patterns in data, and communicate important insights. 6

Analysts can use many techniques to craft stories about data, such as: 6

• Explaining the origins and significance of data
• Comparing different types of charts
• Adding captions or labels to explain each part of the graphic representation
• Suggesting future actions based on the findings

Dashboard Design Best Practices

Dashboards are reporting tools that consolidate data sets and key metrics into interfaces, which are visual displays or webpages that showcase important information. They feature presentation tools such as charts and tables that users can explore to understand data. They also highlight key performance indicators, such as revenue and website traffic. 7

Principles for designing an engaging dashboard include: 7

• Create an accessible and easy-to-navigate user interface
• Clearly label data points
• Use color and size to spotlight the most significant data points and insights
• Ensure the dashboard adapts to different devices, such as computers and smartphones
• Limit the amount of data included to avoid overwhelming users

Infographics and Visual Data Communication

Infographics allow business analysts to communicate large amounts of data in concise and visually appealing formats. They typically blend images and text to share information. For instance, the United States Census Bureau uses interactive bar graphs and maps to convey information about population demographics. 8 These infographics allow people without backgrounds in business analytics to grasp patterns and insights from complex data sets quickly.

Guidelines for creating effective infographics include: 9

• Use color, shape, and size to organize data into a visual hierarchy
• Incorporate three to five colors, including at least one light, one dark, and one for emphasis
• Pair a light background with dark text and images
• Include graphic elements such as charts and timelines to display data

Data Visualization Best Practices

Data analysis experts have developed many best practices to ensure accessibility and accuracy in data visualizations, such as: 3

• Identify essential information that you want to include
• Pick a suitable geometry–or type of graphic–for the dataset
• Choose colors strategically to strengthen your message
• Use resources such as ColorBrewer to choose accessible color schemes that people with color blindness and other visual challenges can see
• Acknowledge uncertainty in data analysis
• Balance simplistic visuals with more comprehensive captions to convey the full story
• Ask other business analysts for feedback

Prepare for Leadership in Data Analytics

Fine-tune your data visualization abilities in the Online Master of Science in Business Analytics program from Santa Clara University. Taught by world-class faculty , you’ll gain industry-leading expertise through a robust curriculum that emphasizes data science and machine learning, fintech , natural language processing , and other technologies. The industry practicum experience will enrich your skillset and expand your professional network .

Don’t wait to advance your career. Schedule a call with one of our admissions outreach advisors today.

• Retrieved on August 25, 2023, from precedenceresearch.com/data-analytics-market
• Retrieved on August 25, 2023, from ncbi.nlm.nih.gov/pmc/articles/PMC7303292/
• Retrieved on August 25, 2023, from cell.com/patterns/fulltext/S2666-3899(20)30189-6
• Retrieved on August 25, 2023, from powerbi.microsoft.com/en-us/why-power-bi/
• Retrieved on August 25, 2023, from tableau.com/why-tableau
• Retrieved on August 25, 2023, from tdwi.org/articles/2022/08/25/bi-all-best-practices-for-data-storytelling.aspx
• Retrieved on August 25, 2023, from forbes.com/sites/forbesbusinesscouncil/2023/08/02/the-crucial-role-of-well-designed-dashboards/
• Retrieved on August 25, 2023, from census.gov/library/visualizations.html
• Retrieved on August 25, 2023, from academic.oup.com/cid/article/74/Supplement_3/e14/6585966

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Big Data Visualization Tools

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Exploratory data analysis ; Information visualization ; Interactive visualization ; Visual analytics ; Visual exploration

Data visualization is the presentation of data in a pictorial or graphical format, and a data visualization tool is the software that generates this presentation. Data visualization provides users with intuitive means to interactively explore and analyze data, enabling them to effectively identify interesting patterns, infer correlations and causalities, and supports sensemaking activities.

Exploring, visualizing, and analyzing data is a core task for data scientists and analysts in numerous applications. Data visualization (Throughout the article, terms visualization and visual exploration , as well as terms tool and system are used interchangeably.) (Ward et al. 2015 ) provides intuitive ways for the users to interactively explore and analyze data, enabling them to effectively identify interesting patterns, infer correlations and...

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School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW, Australia

Rodrigo N. Calheiros

Inria, LIP, ENS Lyon, Lyon, France

Marcos de Dias Assuncao Ph.D

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Bikakis, N. (2018). Big Data Visualization Tools. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_109-1

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DOI : https://doi.org/10.1007/978-3-319-63962-8_109-1

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Published : 04 April 2018

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DOI: https://doi.org/10.1007/978-3-319-63962-8_109-1

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Data Visualization

Data visualization refers to the presentation of data in a pictorial or graphical format using different graphs such as histograms, polygons, line charts, bar charts, etc.

A histogram is a graphical representation of the data contained in a frequency distribution. It is a bar chart of data that groups data into intervals. The intervals should capture all the data points, and, in addition, the intervals should not overlap. A histogram is constructed by plotting intervals on the horizontal axis and the absolute frequencies on the vertical axis.

If a histogram has equal size intervals, a rectangle should be erected over it, with its height being proportional to the absolute frequency. If intervals are of unequal sizes, then the erected rectangle has an area proportional to the absolute frequency of that particular interval. In such a case, we would have the vertical axis labeled as ‘density’ instead of frequency. There should be no space between bars to indicate that the intervals are continuous .

Example: Histogram  

Consider the previous example of the returns offered by a stock. To bring you up to speed, these were the intervals and the corresponding frequencies:

$$ \begin{array}{c|c|c} \text { Interval } & \text { Tally } & \text { Frequency } \\ \hline-30 \% \leq \mathrm{R}_{\mathrm{t}} \leq-20 \% & \text { II } & 2  \\ -20 \% \leq \mathrm{R}_{\mathrm{t}} \leq-10 \% & \text { I } & 1  \\ -10 \% \leq \mathrm{R}_{t} \leq 0 \% & \text { III } & 3 \\ 0 \% \leq \mathrm{R}_{t} \leq 10 \% & \text { IIIII } & 6  \\ 10 \% \leq \mathrm{R}_{t} \leq 20 \% & \text { IIIIII } & 7  \\ 20 \% \leq \mathrm{R}_{t} \leq 30 \% & \text { IIII } & 5\\ 30 \% \leq \mathrm{R}_{t} \leq 40 \% & \text { I } & 1  \\ \hline \text { Total } & & 25 & =25 / 25=100 \% \end{array} $$

As mentioned earlier, histograms can also be created with relative frequencies—the choice of using either absolute or relative frequency depends on the question being answered. An absolute frequency histogram best answers the question of how many items are in each bin. In contrast, a relative frequency histogram gives the proportion or percentage of the total observations in each bin.

Frequency Polygon

A frequency polygon is used to represent the distribution of data graphically. However, there is a major difference between a frequency polygon and a histogram. Instead of having the class intervals on the horizontal axis clearly showing their upper and lower limits, a frequency polygon uses the midpoints of the class intervals  where:

$$\text{Midpoint of a class interval}= \text{Lower limit}+\frac{\text{Upper limit-Lower limit}}{2}$$

The vertical axis features the absolute frequencies, which are then joined using straight lines and markers.

Example: Frequency Polygon

Going back to the stock return data, we could come up with a frequency polygon. To come up with the midpoints, we use the formula above. As an example, the midpoint of the interval -30% ≤ R t ≤ -20% is:

$$ \text{Midpoint} = -30 + \cfrac {(-20 – – 30)}{2} = -25 $$

We can calculate the midpoints for the other intervals in a similar manner. The final frequency polygon should look like this:

The frequency polygon is important because it shows the shape of a distribution of data. It can also be very useful when comparing two sets of data side-by-side. Note that the endpoints touch the X-axis. The vertical scale can also be positioned at the left margin.

A cumulative frequency distribution graph can plot the cumulative frequency or relative frequency against the upper interval limit. The cumulative frequency distribution allows us to see how many or what percentage of the observations lie below a certain value. The figure below is an example of a cumulative frequency distribution.

As we move from one interval to the next, The change in the cumulative relative frequency represents the interval’s relative frequency. A steep slope of the cumulative frequency distribution indicates that the frequencies are large. It is pertinent to note that the slope of the cumulative absolute distribution at any particular interval is proportional to the number of observations in that interval.

A bar chart is used to plot the frequency distribution of category-based data. In a bar chart, each bar represents a distinct category, while the height of a bar is proportional to the frequency of the corresponding category. Bar charts can be vertical or horizontal.

• In a vertical (horizontal) bar chart, the y-axis (x-axis) represents the absolute or relative frequency.
• In contrast, the x-axis (y-axis) represents the mutually exclusive categories to be compared rather than bins that group numerical data (unlike histogram).

Pareto Chart

A bar chart in which categories are ordered by frequency in descending order and has a line displaying cumulative relative frequency is known as a Pareto Chart. This chart is used to highlight dominant categories or the most important groups.

Grouped Bar Chart

A grouped bar chart (also known as a clustered bar chart) plots two category-based variables to represent their joint frequencies.

Stacked Bar Chart

A stacked bar chart also presents the joint frequency distribution of two cat­egory-based variables. In a vertically stacked bar chart, the bars representing the sub-groups are placed on top of each other to form a single bar. Each sub-section of the bar is shown in a different color to represent the contribution of each sub-group. Further, the stacked bar’s overall height represents the category’s marginal frequency.

A tree-map chart displays hierarchical data. A rectangular shape represents each item on a tree-map. It is critical to note that smaller rectangles represent the sub-groups. Further, there is a correlation between the color and size of rectangles and the tree structure. Equally worth noting is the fact that the area of each rectangle is proportional to the value of the corresponding group.

The tree-map below depicts the revenues of different companies in the food sector. We can observe that ABC Company has the highest revenue in the sector, represented by the rectangle with the largest area.

A word cloud (also known as a tag cloud) is a visual representation of textual data. The size of each specific word is proportional to the frequency of words within a given body of text. We can use a different colors to convey different sentiments. For example, we can use green color to display profit. On the other hand, we can use red color to symbolize loss. A word cloud is generally used to display unstructured data.

A line chart is used to display the change in data series over time. It’s important to note that both the frequency polygons and the cumulative frequency distribution charts are line charts, representing data frequency distributions. However, they do not display the change in data over time.

In a line chart, the x-axis represents the period (say years), while the y-axis represents the data we want to plot (say, the real GDP growth rate).

A line chart can be drawn using more than one set of data points, which can be used for comparisons.

Bubble Line Chart

In a bubble line chart, data points are represented by bubbles of various sizes. The bubbles represent the third dimension of data. These bubbles can be of different colors to represent additional information, i.e., red bubbles for negative values and green bubbles for positive values.

Scatter Plot

A scatter plot is a graph that shows the relationship between two numerical variables. It helps in displaying and understanding potential relationships between two variables. In a scatter plot, one variable is plotted on the x-axis and another, on the y-axis.

As shown above, a positive slope for a line of data points indicates a positive relationship between two variables, and vice-versa. The strength of the relationship between variables can be determined based on how closely the data points are clustered around the line. Tight clustering indicates a potentially stronger relationship. Further, data points located toward the ends of each axis represent the maximum or minimum values (i.e., outliers).

Scatter Plot Matrix

A scatter plot matrix is a grid of scatter plots used to visualize bivariate relationships between combinations of variables.

A heat map is a graphical representation of data that uses a system of color-coding to represent different values. More intense color is displayed as the marks “heat up” due to their higher values or density of records. Heat maps can display frequency distributions and visualize the degree of correlation among different variables.

Question Which of the following most likely represents the graph drawn by connecting successive mid-points in a histogram by straight lines? Histogram. Frequency curve. Frequency polygon. Solution The correct answer is  C . When straight lines connect successive midpoints in a histogram, the graph is called a frequency polygon. A is incorrect . A histogram is a graphical representation of the data contained in the frequency distribution but not using mid-points. Instead, a rectangle should be erected over the whole interval. B is incorrect . When a frequency polygon is smoothed, a frequency curve is obtained.

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Blog Data Visualization 10 Data Presentation Examples For Strategic Communication

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower. 

Data presentation today is no longer just about numbers on a screen; it’s storytelling with a purpose. It’s about captivating your audience, making complex stuff look simple and inspiring action. 

To help turn your data into stories that stick, influence decisions and make an impact, check out Venngage’s free chart maker or follow me on a tour into the world of data storytelling along with data presentation templates that work across different fields, from business boardrooms to the classroom and beyond. Keep scrolling to learn more! 

Click to jump ahead:

10 Essential data presentation examples + methods you should know

What should be included in a data presentation, what are some common mistakes to avoid when presenting data, faqs on data presentation examples, transform your message with impactful data storytelling.

Data presentation is a vital skill in today’s information-driven world. Whether you’re in business, academia, or simply want to convey information effectively, knowing the different ways of presenting data is crucial. For impactful data storytelling, consider these essential data presentation methods:

1. Bar graph

Ideal for comparing data across categories or showing trends over time.

Bar graphs, also known as bar charts are workhorses of data presentation. They’re like the Swiss Army knives of visualization methods because they can be used to compare data in different categories or display data changes over time. 

In a bar chart, categories are displayed on the x-axis and the corresponding values are represented by the height of the bars on the y-axis. 

It’s a straightforward and effective way to showcase raw data, making it a staple in business reports, academic presentations and beyond.

Make sure your bar charts are concise with easy-to-read labels. Whether your bars go up or sideways, keep it simple by not overloading with too many categories.

2. Line graph

Great for displaying trends and variations in data points over time or continuous variables.

Line charts or line graphs are your go-to when you want to visualize trends and variations in data sets over time.

One of the best quantitative data presentation examples, they work exceptionally well for showing continuous data, such as sales projections over the last couple of years or supply and demand fluctuations. 

The x-axis represents time or a continuous variable and the y-axis represents the data values. By connecting the data points with lines, you can easily spot trends and fluctuations.

A tip when presenting data with line charts is to minimize the lines and not make it too crowded. Highlight the big changes, put on some labels and give it a catchy title.

3. Pie chart

Useful for illustrating parts of a whole, such as percentages or proportions.

Pie charts are perfect for showing how a whole is divided into parts. They’re commonly used to represent percentages or proportions and are great for presenting survey results that involve demographic data. 

Each “slice” of the pie represents a portion of the whole and the size of each slice corresponds to its share of the total. 

While pie charts are handy for illustrating simple distributions, they can become confusing when dealing with too many categories or when the differences in proportions are subtle.

Don’t get too carried away with slices — label those slices with percentages or values so people know what’s what and consider using a legend for more categories.

4. Scatter plot

Effective for showing the relationship between two variables and identifying correlations.

Scatter plots are all about exploring relationships between two variables. They’re great for uncovering correlations, trends or patterns in data. 

In a scatter plot, every data point appears as a dot on the chart, with one variable marked on the horizontal x-axis and the other on the vertical y-axis.

By examining the scatter of points, you can discern the nature of the relationship between the variables, whether it’s positive, negative or no correlation at all.

If you’re using scatter plots to reveal relationships between two variables, be sure to add trendlines or regression analysis when appropriate to clarify patterns. Label data points selectively or provide tooltips for detailed information.

5. Histogram

Best for visualizing the distribution and frequency of a single variable.

Histograms are your choice when you want to understand the distribution and frequency of a single variable. 

They divide the data into “bins” or intervals and the height of each bar represents the frequency or count of data points falling into that interval. 

Histograms are excellent for helping to identify trends in data distributions, such as peaks, gaps or skewness.

Here’s something to take note of — ensure that your histogram bins are appropriately sized to capture meaningful data patterns. Using clear axis labels and titles can also help explain the distribution of the data effectively.

6. Stacked bar chart

Useful for showing how different components contribute to a whole over multiple categories.

Stacked bar charts are a handy choice when you want to illustrate how different components contribute to a whole across multiple categories. 

Each bar represents a category and the bars are divided into segments to show the contribution of various components within each category. 

This method is ideal for highlighting both the individual and collective significance of each component, making it a valuable tool for comparative analysis.

Stacked bar charts are like data sandwiches—label each layer so people know what’s what. Keep the order logical and don’t forget the paintbrush for snazzy colors. Here’s a data analysis presentation example on writers’ productivity using stacked bar charts:

7. Area chart

Similar to line charts but with the area below the lines filled, making them suitable for showing cumulative data.

Area charts are close cousins of line charts but come with a twist. 

Imagine plotting the sales of a product over several months. In an area chart, the space between the line and the x-axis is filled, providing a visual representation of the cumulative total. 

This makes it easy to see how values stack up over time, making area charts a valuable tool for tracking trends in data.

For area charts, use them to visualize cumulative data and trends, but avoid overcrowding the chart. Add labels, especially at significant points and make sure the area under the lines is filled with a visually appealing color gradient.

8. Tabular presentation

Presenting data in rows and columns, often used for precise data values and comparisons.

Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points. 

A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They’re commonly used in reports, spreadsheets and academic papers.

When presenting tabular data, organize it neatly with clear headers and appropriate column widths. Highlight important data points or patterns using shading or font formatting for better readability.

9. Textual data

Utilizing written or descriptive content to explain or complement data, such as annotations or explanatory text.

Textual data presentation may not involve charts or graphs, but it’s one of the most used qualitative data presentation examples. 

It involves using written content to provide context, explanations or annotations alongside data visuals. Think of it as the narrative that guides your audience through the data. 

Well-crafted textual data can make complex information more accessible and help your audience understand the significance of the numbers and visuals.

Textual data is your chance to tell a story. Break down complex information into bullet points or short paragraphs and use headings to guide the reader’s attention.

10. Pictogram

Using simple icons or images to represent data is especially useful for conveying information in a visually intuitive manner.

Pictograms are all about harnessing the power of images to convey data in an easy-to-understand way. 

Instead of using numbers or complex graphs, you use simple icons or images to represent data points. 

For instance, you could use a thumbs up emoji to illustrate customer satisfaction levels, where each face represents a different level of satisfaction. 

Pictograms are great for conveying data visually, so choose symbols that are easy to interpret and relevant to the data. Use consistent scaling and a legend to explain the symbols’ meanings, ensuring clarity in your presentation.

Looking for more data presentation ideas? Use the Venngage graph maker or browse through our gallery of chart templates to pick a template and get started! 

A comprehensive data presentation should include several key elements to effectively convey information and insights to your audience. Here’s a list of what should be included in a data presentation:

1. Title and objective

• Begin with a clear and informative title that sets the context for your presentation.
• State the primary objective or purpose of the presentation to provide a clear focus.

2. Key data points

• Present the most essential data points or findings that align with your objective.
• Use charts, graphical presentations or visuals to illustrate these key points for better comprehension.

3. Context and significance

• Provide a brief overview of the context in which the data was collected and why it’s significant.
• Explain how the data relates to the larger picture or the problem you’re addressing.

4. Key takeaways

• Summarize the main insights or conclusions that can be drawn from the data.
• Highlight the key takeaways that the audience should remember.

5. Visuals and charts

• Use clear and appropriate visual aids to complement the data.
• Ensure that visuals are easy to understand and support your narrative.

6. Implications or actions

• Discuss the practical implications of the data or any recommended actions.
• If applicable, outline next steps or decisions that should be taken based on the data.

7. Q&A and discussion

• Allocate time for questions and open discussion to engage the audience.
• Address queries and provide additional insights or context as needed.

Presenting data is a crucial skill in various professional fields, from business to academia and beyond. To ensure your data presentations hit the mark, here are some common mistakes that you should steer clear of:

Overloading with data

Presenting too much data at once can overwhelm your audience. Focus on the key points and relevant information to keep the presentation concise and focused. Here are some free data visualization tools you can use to convey data in an engaging and impactful way. 

Assuming everyone’s on the same page

It’s easy to assume that your audience understands as much about the topic as you do. But this can lead to either dumbing things down too much or diving into a bunch of jargon that leaves folks scratching their heads. Take a beat to figure out where your audience is coming from and tailor your presentation accordingly.

Misleading visuals

Using misleading visuals, such as distorted scales or inappropriate chart types can distort the data’s meaning. Pick the right data infographics and understandable charts to ensure that your visual representations accurately reflect the data.

Not providing context

Data without context is like a puzzle piece with no picture on it. Without proper context, data may be meaningless or misinterpreted. Explain the background, methodology and significance of the data.

Not citing sources properly

Neglecting to cite sources and provide citations for your data can erode its credibility. Always attribute data to its source and utilize reliable sources for your presentation.

Not telling a story

Avoid simply presenting numbers. If your presentation lacks a clear, engaging story that takes your audience on a journey from the beginning (setting the scene) through the middle (data analysis) to the end (the big insights and recommendations), you’re likely to lose their interest.

Infographics are great for storytelling because they mix cool visuals with short and sweet text to explain complicated stuff in a fun and easy way. Create one with Venngage’s free infographic maker to create a memorable story that your audience will remember.

Ignoring data quality

Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.

Simplify your visuals

Fancy charts might look cool, but if they confuse people, what’s the point? Go for the simplest visual that gets your message across. Having a dilemma between presenting data with infographics v.s data design? This article on the difference between data design and infographics might help you out. 

Missing the emotional connection

Data isn’t just about numbers; it’s about people and real-life situations. Don’t forget to sprinkle in some human touch, whether it’s through relatable stories, examples or showing how the data impacts real lives.

Skipping the actionable insights

At the end of the day, your audience wants to know what they should do with all the data. If you don’t wrap up with clear, actionable insights or recommendations, you’re leaving them hanging. Always finish up with practical takeaways and the next steps.

Can you provide some data presentation examples for business reports?

Business reports often benefit from data presentation through bar charts showing sales trends over time, pie charts displaying market share,or tables presenting financial performance metrics like revenue and profit margins.

What are some creative data presentation examples for academic presentations?

Creative data presentation ideas for academic presentations include using statistical infographics to illustrate research findings and statistical data, incorporating storytelling techniques to engage the audience or utilizing heat maps to visualize data patterns.

What are the key considerations when choosing the right data presentation format?

When choosing a chart format , consider factors like data complexity, audience expertise and the message you want to convey. Options include charts (e.g., bar, line, pie), tables, heat maps, data visualization infographics and interactive dashboards.

Knowing the type of data visualization that best serves your data is just half the battle. Here are some best practices for data visualization to make sure that the final output is optimized. 

How can I choose the right data presentation method for my data?

To select the right data presentation method, start by defining your presentation’s purpose and audience. Then, match your data type (e.g., quantitative, qualitative) with suitable visualization techniques (e.g., histograms, word clouds) and choose an appropriate presentation format (e.g., slide deck, report, live demo).

For more presentation ideas , check out this guide on how to make a good presentation or use a presentation software to simplify the process.  

How can I make my data presentations more engaging and informative?

To enhance data presentations, use compelling narratives, relatable examples and fun data infographics that simplify complex data. Encourage audience interaction, offer actionable insights and incorporate storytelling elements to engage and inform effectively.

The opening of your presentation holds immense power in setting the stage for your audience. To design a presentation and convey your data in an engaging and informative, try out Venngage’s free presentation maker to pick the right presentation design for your audience and topic. 

What is the difference between data visualization and data presentation?

Data presentation typically involves conveying data reports and insights to an audience, often using visuals like charts and graphs. Data visualization , on the other hand, focuses on creating those visual representations of data to facilitate understanding and analysis. 

Now that you’ve learned a thing or two about how to use these methods of data presentation to tell a compelling data story , it’s time to take these strategies and make them your own. 

But here’s the deal: these aren’t just one-size-fits-all solutions. Remember that each example we’ve uncovered here is not a rigid template but a source of inspiration. It’s all about making your audience go, “Wow, I get it now!”

Think of your data presentations as your canvas – it’s where you paint your story, convey meaningful insights and make real change happen. 

So, go forth, present your data with confidence and purpose and watch as your strategic influence grows, one compelling presentation at a time.

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