importance of data visualization in presentation

The Role of Data Visualization in Presentations

Data visualization in presentations: types and advantages.

Sep 19, 2022

Your presentation should inspire, persuade, and inform your audience without boring them to tears. However, even with a creative mind and polished design skills, infusing life into sticky and data-populated presentation topics can be a tall order. But not if you leverage data visualization. 

Data visualization is the representation of data through visual displays such as charts, histograms, maps, tables, dashboards, graphs, and infographics. Integrating data visualization into your presentation makes it easy for your audience to digest, absorb, and remember complex information and data. The American Management Association says visuals and actions make written information 70% more memorable . 

Thus, if you want to design a stellar presentation that delights your audience from start to finish, utilize graphical displays to your advantage. Fortunately, as we discuss below, you can employ several types of data visualization in your presentation. 

The Different Types of Interactive Data Visualizations

Interactive information visualization helps your audience quickly gather your presentation’s primary insights and takeaways by analyzing the visuals. 

Interactive visualizations create a synergetic interaction between your audience and the data, empowering them to summarize and correlate findings more efficiently. They’re especially effective in the corporate world, for instance, when delivering a business process improvement presentation.

While interactive visualizations can take many forms, these are the most prevalent in presentations:

Pie Charts To Show Important Percentages

Pie charts are by far the most effective way of representing data in percentages. A pie chart denotes individual percentages of a whole figure, making it easier to interpret data since percentages tally up to 100%. 

The full circle represents the whole figure, while each slice of the pie portrays the individual percentages. Ideally, you should use the pie chart to visualize five to six parts utmost, so it’s legible and not too populated. If you have seven or more sections to compare, go for the donut chart . 

Lastly, make good use of color coding to differentiate each wedge of your pie chart as color schemes make your data more memorable. Research has shown that colors improve human memory  by boosting concentration and focus. 

Bar Chart or Scatter Plots for Easy Data Comparison

Bar charts contrast data along a vertical axis (y-axis) and a horizontal axis (x-axis). The graphical representation created by bar charts makes it easy to compare correlative data. For instance, when comparing the yearly profit revenues of a company, you can display the revenue numbers on the x-axis and the years on the y-axis. 

Complete Dashboard Design With Multiple Graphs and Maps

When you need to display geographical data and protracted metrics, a dashboard design that integrates maps and graphs will suffice. You may need multiple graphs to present overlapping information like sales, revenue, and marketing data. Maps are handy when displaying geographical data like election results or meteorological data. 

You need ample graphic design knowledge to create aesthetic data visualization designs — like business process flowcharts — to integrate them smoothly into your presentation. Good thing you can hire graphic design experts who understand the assignment inside out and are flexible and prompt.

Why Data Visualization Tools Are Necessary for a Presentation

You need data visualization tools to create all types of visual displays. These tools are software applications designed to render and present raw data in graphical formats, such as pie charts, graphs, and bar charts. Besides handling data rendering, data visualizations tools offer the following benefits:   

Tells Your Data Story in an Elegant and Meaningful Way

Data in its raw form is complex and challenging to interpret and understand. It’s hard to tell a perceptive data story using blocks of text only. Given that the attention span for a typical audience is seven minutes , you’ll lose your audience sooner if your presentation is crammed with lots of raw data and statistics. 

Conversely, visuals help you tell a compelling data story that your audience can follow without being at sea. Good thing you’ll find a suitable data visualization tool no matter your field of expertise. For instance, you’ll find a tool for creating complex scientific visualizations if you’re a scientist and one for creating simple pie charts if you’re a motivational speaker.

Supports Idea Generation Beyond Just Those in the Field of Statistics

It’s easier for your audience to derive business insights and spot data inaccuracies from a presentation with a lot of data visualizations. By assessing and probing these insights, your audience may get a light-bulb moment that births a conceptual idea with a real-world transformational impact.

With a graphical representation of data, it’s easier for a discerning eye to spot marginal differences in cycles and patterns. These are the subtle insights that decision-makers and top professionals need to implement innovative ideas. Without data visualization tools, it would take a great deal of time to structure raw data in an easy-to-read format that can foster idea generation. 

Simplifies Data and Business Processes

If you had to draw all the data visualization examples you need in a presentation by yourself, it would be a huge undertaking that would tie up most of your productive time. But with data visualization tools, it’s simple and less time and resource-intensive. This has multifold benefits for you and your audience.

On the one hand, you’ll prepare your presentation visuals more swiftly. Faster preparation gives you more time to complete other tasks on your tab. On the other hand, your audience will access real-time data in a digested form, making it more valuable to their business processes.

Visualize Data With Ease By Outsourcing Your Presentations

Admittedly, adding data visualizations in your presentations isn’t a no-sweat job. Particularly, when dealing with large-scale data that needs multiple visual and graphic representations, the workflow can easily overwhelm you as there's much design thinking needed. But, creating data visualizations shouldn’t be overwhelming since you can hire presentation design experts  like GhostRanch Communications to do all the heavy lifting.

At GhostRanch Communications, we design any graphical and visual representations you need for your presentation. Whether you want 3-D maps, bar graphs, or simple pie charts, we have the tools and talent to deliver exquisite designs that’ll turn heads, close deals, and save you time.

Contact us today , and let us help you visualize your next presentation. 

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Home Blog Design Understanding Data Presentations (Guide + Examples)

Understanding Data Presentations (Guide + Examples)

In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey. Different types of visualizations serve distinct purposes. Whether you’re dealing with how to develop a report or simply trying to communicate complex information, how you present data influences how well your audience understands and engages with it. This extensive guide leads you through the different ways of data presentation.

Table of Contents

What is a Data Presentation?

What should a data presentation include, line graphs, treemap chart, scatter plot, how to choose a data presentation type, recommended data presentation templates, common mistakes done in data presentation.

A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations to improve understanding and boost retention rate.

Data presentations require us to cull data in a format that allows the presenter to highlight trends, patterns, and insights so that the audience can act upon the shared information. In a few words, the goal of data presentations is to enable viewers to grasp complicated concepts or trends quickly, facilitating informed decision-making or deeper analysis.

Data presentations go beyond the mere usage of graphical elements. Seasoned presenters encompass visuals with the art of data storytelling , so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

To nail your upcoming data presentation, ensure to count with the following elements:

• Clear Objectives: Understand the intent of your presentation before selecting the graphical layout and metaphors to make content easier to grasp.
• Engaging introduction: Use a powerful hook from the get-go. For instance, you can ask a big question or present a problem that your data will answer. Take a look at our guide on how to start a presentation for tips & insights.
• Structured Narrative: Your data presentation must tell a coherent story. This means a beginning where you present the context, a middle section in which you present the data, and an ending that uses a call-to-action. Check our guide on presentation structure for further information.
• Visual Elements: These are the charts, graphs, and other elements of visual communication we ought to use to present data. This article will cover one by one the different types of data representation methods we can use, and provide further guidance on choosing between them.
• Insights and Analysis: This is not just showcasing a graph and letting people get an idea about it. A proper data presentation includes the interpretation of that data, the reason why it’s included, and why it matters to your research.
• Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow your audience into acquiring your services, inspire them to change the world, or whatever the purpose of your presentation, there must be a stage in which you convey all that you shared and show the path to staying in touch. Plan ahead whether you want to use a thank-you slide, a video presentation, or which method is apt and tailored to the kind of presentation you deliver.
• Q&A Session: After your speech is concluded, allocate 3-5 minutes for the audience to raise any questions about the information you disclosed. This is an extra chance to establish your authority on the topic. Check our guide on questions and answer sessions in presentations here.

Bar charts are a graphical representation of data using rectangular bars to show quantities or frequencies in an established category. They make it easy for readers to spot patterns or trends. Bar charts can be horizontal or vertical, although the vertical format is commonly known as a column chart. They display categorical, discrete, or continuous variables grouped in class intervals [1] . They include an axis and a set of labeled bars horizontally or vertically. These bars represent the frequencies of variable values or the values themselves. Numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

Real-Life Application of Bar Charts

Let’s say a sales manager is presenting sales to their audience. Using a bar chart, he follows these steps.

Step 1: Selecting Data

The first step is to identify the specific data you will present to your audience.

The sales manager has highlighted these products for the presentation.

• Product A: Men’s Shoes
• Product B: Women’s Apparel
• Product C: Electronics
• Product D: Home Decor

Step 2: Choosing Orientation

Opt for a vertical layout for simplicity. Vertical bar charts help compare different categories in case there are not too many categories [1] . They can also help show different trends. A vertical bar chart is used where each bar represents one of the four chosen products. After plotting the data, it is seen that the height of each bar directly represents the sales performance of the respective product.

It is visible that the tallest bar (Electronics – Product C) is showing the highest sales. However, the shorter bars (Women’s Apparel – Product B and Home Decor – Product D) need attention. It indicates areas that require further analysis or strategies for improvement.

Step 3: Colorful Insights

Different colors are used to differentiate each product. It is essential to show a color-coded chart where the audience can distinguish between products.

• Men’s Shoes (Product A): Yellow
• Women’s Apparel (Product B): Orange
• Electronics (Product C): Violet
• Home Decor (Product D): Blue

Bar charts are straightforward and easily understandable for presenting data. They are versatile when comparing products or any categorical data [2] . Bar charts adapt seamlessly to retail scenarios. Despite that, bar charts have a few shortcomings. They cannot illustrate data trends over time. Besides, overloading the chart with numerous products can lead to visual clutter, diminishing its effectiveness.

For more information, check our collection of bar chart templates for PowerPoint .

Line graphs help illustrate data trends, progressions, or fluctuations by connecting a series of data points called ‘markers’ with straight line segments. This provides a straightforward representation of how values change [5] . Their versatility makes them invaluable for scenarios requiring a visual understanding of continuous data. In addition, line graphs are also useful for comparing multiple datasets over the same timeline. Using multiple line graphs allows us to compare more than one data set. They simplify complex information so the audience can quickly grasp the ups and downs of values. From tracking stock prices to analyzing experimental results, you can use line graphs to show how data changes over a continuous timeline. They show trends with simplicity and clarity.

Real-life Application of Line Graphs

To understand line graphs thoroughly, we will use a real case. Imagine you’re a financial analyst presenting a tech company’s monthly sales for a licensed product over the past year. Investors want insights into sales behavior by month, how market trends may have influenced sales performance and reception to the new pricing strategy. To present data via a line graph, you will complete these steps.

First, you need to gather the data. In this case, your data will be the sales numbers. For example:

• January: $45,000
• February: $55,000
• March: $45,000
• April: $60,000
• May: $ 70,000
• June: $65,000
• July: $62,000
• August: $68,000
• September: $81,000
• October: $76,000
• November: $87,000
• December: $91,000

After choosing the data, the next step is to select the orientation. Like bar charts, you can use vertical or horizontal line graphs. However, we want to keep this simple, so we will keep the timeline (x-axis) horizontal while the sales numbers (y-axis) vertical.

Step 3: Connecting Trends

After adding the data to your preferred software, you will plot a line graph. In the graph, each month’s sales are represented by data points connected by a line.

Step 4: Adding Clarity with Color

If there are multiple lines, you can also add colors to highlight each one, making it easier to follow.

Line graphs excel at visually presenting trends over time. These presentation aids identify patterns, like upward or downward trends. However, too many data points can clutter the graph, making it harder to interpret. Line graphs work best with continuous data but are not suitable for categories.

For more information, check our collection of line chart templates for PowerPoint and our article about how to make a presentation graph .

A data dashboard is a visual tool for analyzing information. Different graphs, charts, and tables are consolidated in a layout to showcase the information required to achieve one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs). You don’t make new visuals in the dashboard; instead, you use it to display visuals you’ve already made in worksheets [3] .

Keeping the number of visuals on a dashboard to three or four is recommended. Adding too many can make it hard to see the main points [4]. Dashboards can be used for business analytics to analyze sales, revenue, and marketing metrics at a time. They are also used in the manufacturing industry, as they allow users to grasp the entire production scenario at the moment while tracking the core KPIs for each line.

Real-Life Application of a Dashboard

Consider a project manager presenting a software development project’s progress to a tech company’s leadership team. He follows the following steps.

Step 1: Defining Key Metrics

To effectively communicate the project’s status, identify key metrics such as completion status, budget, and bug resolution rates. Then, choose measurable metrics aligned with project objectives.

Step 2: Choosing Visualization Widgets

After finalizing the data, presentation aids that align with each metric are selected. For this project, the project manager chooses a progress bar for the completion status and uses bar charts for budget allocation. Likewise, he implements line charts for bug resolution rates.

Step 3: Dashboard Layout

Key metrics are prominently placed in the dashboard for easy visibility, and the manager ensures that it appears clean and organized.

Dashboards provide a comprehensive view of key project metrics. Users can interact with data, customize views, and drill down for detailed analysis. However, creating an effective dashboard requires careful planning to avoid clutter. Besides, dashboards rely on the availability and accuracy of underlying data sources.

For more information, check our article on how to design a dashboard presentation , and discover our collection of dashboard PowerPoint templates .

Treemap charts represent hierarchical data structured in a series of nested rectangles [6] . As each branch of the ‘tree’ is given a rectangle, smaller tiles can be seen representing sub-branches, meaning elements on a lower hierarchical level than the parent rectangle. Each one of those rectangular nodes is built by representing an area proportional to the specified data dimension.

Treemaps are useful for visualizing large datasets in compact space. It is easy to identify patterns, such as which categories are dominant. Common applications of the treemap chart are seen in the IT industry, such as resource allocation, disk space management, website analytics, etc. Also, they can be used in multiple industries like healthcare data analysis, market share across different product categories, or even in finance to visualize portfolios.

Real-Life Application of a Treemap Chart

Let’s consider a financial scenario where a financial team wants to represent the budget allocation of a company. There is a hierarchy in the process, so it is helpful to use a treemap chart. In the chart, the top-level rectangle could represent the total budget, and it would be subdivided into smaller rectangles, each denoting a specific department. Further subdivisions within these smaller rectangles might represent individual projects or cost categories.

Step 1: Define Your Data Hierarchy

While presenting data on the budget allocation, start by outlining the hierarchical structure. The sequence will be like the overall budget at the top, followed by departments, projects within each department, and finally, individual cost categories for each project.

• Top-level rectangle: Total Budget
• Second-level rectangles: Departments (Engineering, Marketing, Sales)
• Third-level rectangles: Projects within each department
• Fourth-level rectangles: Cost categories for each project (Personnel, Marketing Expenses, Equipment)

Step 2: Choose a Suitable Tool

It’s time to select a data visualization tool supporting Treemaps. Popular choices include Tableau, Microsoft Power BI, PowerPoint, or even coding with libraries like D3.js. It is vital to ensure that the chosen tool provides customization options for colors, labels, and hierarchical structures.

Here, the team uses PowerPoint for this guide because of its user-friendly interface and robust Treemap capabilities.

Step 3: Make a Treemap Chart with PowerPoint

After opening the PowerPoint presentation, they chose “SmartArt” to form the chart. The SmartArt Graphic window has a “Hierarchy” category on the left.  Here, you will see multiple options. You can choose any layout that resembles a Treemap. The “Table Hierarchy” or “Organization Chart” options can be adapted. The team selects the Table Hierarchy as it looks close to a Treemap.

Step 5: Input Your Data

After that, a new window will open with a basic structure. They add the data one by one by clicking on the text boxes. They start with the top-level rectangle, representing the total budget.  

Step 6: Customize the Treemap

By clicking on each shape, they customize its color, size, and label. At the same time, they can adjust the font size, style, and color of labels by using the options in the “Format” tab in PowerPoint. Using different colors for each level enhances the visual difference.

Treemaps excel at illustrating hierarchical structures. These charts make it easy to understand relationships and dependencies. They efficiently use space, compactly displaying a large amount of data, reducing the need for excessive scrolling or navigation. Additionally, using colors enhances the understanding of data by representing different variables or categories.

In some cases, treemaps might become complex, especially with deep hierarchies.  It becomes challenging for some users to interpret the chart. At the same time, displaying detailed information within each rectangle might be constrained by space. It potentially limits the amount of data that can be shown clearly. Without proper labeling and color coding, there’s a risk of misinterpretation.

A heatmap is a data visualization tool that uses color coding to represent values across a two-dimensional surface. In these, colors replace numbers to indicate the magnitude of each cell. This color-shaded matrix display is valuable for summarizing and understanding data sets with a glance [7] . The intensity of the color corresponds to the value it represents, making it easy to identify patterns, trends, and variations in the data.

As a tool, heatmaps help businesses analyze website interactions, revealing user behavior patterns and preferences to enhance overall user experience. In addition, companies use heatmaps to assess content engagement, identifying popular sections and areas of improvement for more effective communication. They excel at highlighting patterns and trends in large datasets, making it easy to identify areas of interest.

We can implement heatmaps to express multiple data types, such as numerical values, percentages, or even categorical data. Heatmaps help us easily spot areas with lots of activity, making them helpful in figuring out clusters [8] . When making these maps, it is important to pick colors carefully. The colors need to show the differences between groups or levels of something. And it is good to use colors that people with colorblindness can easily see.

Check our detailed guide on how to create a heatmap here. Also discover our collection of heatmap PowerPoint templates .

Pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, making it easy to visualize the contribution of each component to the total.

The size of the pie charts is influenced by the value of data points within each pie. The total of all data points in a pie determines its size. The pie with the highest data points appears as the largest, whereas the others are proportionally smaller. However, you can present all pies of the same size if proportional representation is not required [9] . Sometimes, pie charts are difficult to read, or additional information is required. A variation of this tool can be used instead, known as the donut chart , which has the same structure but a blank center, creating a ring shape. Presenters can add extra information, and the ring shape helps to declutter the graph.

Pie charts are used in business to show percentage distribution, compare relative sizes of categories, or present straightforward data sets where visualizing ratios is essential.

Real-Life Application of Pie Charts

Consider a scenario where you want to represent the distribution of the data. Each slice of the pie chart would represent a different category, and the size of each slice would indicate the percentage of the total portion allocated to that category.

Step 1: Define Your Data Structure

Imagine you are presenting the distribution of a project budget among different expense categories.

• Column A: Expense Categories (Personnel, Equipment, Marketing, Miscellaneous)
• Column B: Budget Amounts ($40,000, $30,000, $20,000, $10,000) Column B represents the values of your categories in Column A.

Step 2: Insert a Pie Chart

Using any of the accessible tools, you can create a pie chart. The most convenient tools for forming a pie chart in a presentation are presentation tools such as PowerPoint or Google Slides.  You will notice that the pie chart assigns each expense category a percentage of the total budget by dividing it by the total budget.

For instance:

• Personnel: $40,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 40%
• Equipment: $30,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 30%
• Marketing: $20,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 20%
• Miscellaneous: $10,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 10%

You can make a chart out of this or just pull out the pie chart from the data.

3D pie charts and 3D donut charts are quite popular among the audience. They stand out as visual elements in any presentation slide, so let’s take a look at how our pie chart example would look in 3D pie chart format.

Step 03: Results Interpretation

The pie chart visually illustrates the distribution of the project budget among different expense categories. Personnel constitutes the largest portion at 40%, followed by equipment at 30%, marketing at 20%, and miscellaneous at 10%. This breakdown provides a clear overview of where the project funds are allocated, which helps in informed decision-making and resource management. It is evident that personnel are a significant investment, emphasizing their importance in the overall project budget.

Pie charts provide a straightforward way to represent proportions and percentages. They are easy to understand, even for individuals with limited data analysis experience. These charts work well for small datasets with a limited number of categories.

However, a pie chart can become cluttered and less effective in situations with many categories. Accurate interpretation may be challenging, especially when dealing with slight differences in slice sizes. In addition, these charts are static and do not effectively convey trends over time.

For more information, check our collection of pie chart templates for PowerPoint .

Histograms present the distribution of numerical variables. Unlike a bar chart that records each unique response separately, histograms organize numeric responses into bins and show the frequency of reactions within each bin [10] . The x-axis of a histogram shows the range of values for a numeric variable. At the same time, the y-axis indicates the relative frequencies (percentage of the total counts) for that range of values.

Whenever you want to understand the distribution of your data, check which values are more common, or identify outliers, histograms are your go-to. Think of them as a spotlight on the story your data is telling. A histogram can provide a quick and insightful overview if you’re curious about exam scores, sales figures, or any numerical data distribution.

Real-Life Application of a Histogram

In the histogram data analysis presentation example, imagine an instructor analyzing a class’s grades to identify the most common score range. A histogram could effectively display the distribution. It will show whether most students scored in the average range or if there are significant outliers.

Step 1: Gather Data

He begins by gathering the data. The scores of each student in class are gathered to analyze exam scores.

After arranging the scores in ascending order, bin ranges are set.

Step 2: Define Bins

Bins are like categories that group similar values. Think of them as buckets that organize your data. The presenter decides how wide each bin should be based on the range of the values. For instance, the instructor sets the bin ranges based on score intervals: 60-69, 70-79, 80-89, and 90-100.

Step 3: Count Frequency

Now, he counts how many data points fall into each bin. This step is crucial because it tells you how often specific ranges of values occur. The result is the frequency distribution, showing the occurrences of each group.

Here, the instructor counts the number of students in each category.

• 60-69: 1 student (Kate)
• 70-79: 4 students (David, Emma, Grace, Jack)
• 80-89: 7 students (Alice, Bob, Frank, Isabel, Liam, Mia, Noah)
• 90-100: 3 students (Clara, Henry, Olivia)

Step 4: Create the Histogram

It’s time to turn the data into a visual representation. Draw a bar for each bin on a graph. The width of the bar should correspond to the range of the bin, and the height should correspond to the frequency.  To make your histogram understandable, label the X and Y axes.

In this case, the X-axis should represent the bins (e.g., test score ranges), and the Y-axis represents the frequency.

The histogram of the class grades reveals insightful patterns in the distribution. Most students, with seven students, fall within the 80-89 score range. The histogram provides a clear visualization of the class’s performance. It showcases a concentration of grades in the upper-middle range with few outliers at both ends. This analysis helps in understanding the overall academic standing of the class. It also identifies the areas for potential improvement or recognition.

Thus, histograms provide a clear visual representation of data distribution. They are easy to interpret, even for those without a statistical background. They apply to various types of data, including continuous and discrete variables. One weak point is that histograms do not capture detailed patterns in students’ data, with seven compared to other visualization methods.

A scatter plot is a graphical representation of the relationship between two variables. It consists of individual data points on a two-dimensional plane. This plane plots one variable on the x-axis and the other on the y-axis. Each point represents a unique observation. It visualizes patterns, trends, or correlations between the two variables.

Scatter plots are also effective in revealing the strength and direction of relationships. They identify outliers and assess the overall distribution of data points. The points’ dispersion and clustering reflect the relationship’s nature, whether it is positive, negative, or lacks a discernible pattern. In business, scatter plots assess relationships between variables such as marketing cost and sales revenue. They help present data correlations and decision-making.

Real-Life Application of Scatter Plot

A group of scientists is conducting a study on the relationship between daily hours of screen time and sleep quality. After reviewing the data, they managed to create this table to help them build a scatter plot graph:

In the provided example, the x-axis represents Daily Hours of Screen Time, and the y-axis represents the Sleep Quality Rating.

The scientists observe a negative correlation between the amount of screen time and the quality of sleep. This is consistent with their hypothesis that blue light, especially before bedtime, has a significant impact on sleep quality and metabolic processes.

There are a few things to remember when using a scatter plot. Even when a scatter diagram indicates a relationship, it doesn’t mean one variable affects the other. A third factor can influence both variables. The more the plot resembles a straight line, the stronger the relationship is perceived [11] . If it suggests no ties, the observed pattern might be due to random fluctuations in data. When the scatter diagram depicts no correlation, whether the data might be stratified is worth considering.

Choosing the appropriate data presentation type is crucial when making a presentation . Understanding the nature of your data and the message you intend to convey will guide this selection process. For instance, when showcasing quantitative relationships, scatter plots become instrumental in revealing correlations between variables. If the focus is on emphasizing parts of a whole, pie charts offer a concise display of proportions. Histograms, on the other hand, prove valuable for illustrating distributions and frequency patterns. 

Bar charts provide a clear visual comparison of different categories. Likewise, line charts excel in showcasing trends over time, while tables are ideal for detailed data examination. Starting a presentation on data presentation types involves evaluating the specific information you want to communicate and selecting the format that aligns with your message. This ensures clarity and resonance with your audience from the beginning of your presentation.

1. Fact Sheet Dashboard for Data Presentation

Convey all the data you need to present in this one-pager format, an ideal solution tailored for users looking for presentation aids. Global maps, donut chats, column graphs, and text neatly arranged in a clean layout presented in light and dark themes.

Use This Template

2. 3D Column Chart Infographic PPT Template

Represent column charts in a highly visual 3D format with this PPT template. A creative way to present data, this template is entirely editable, and we can craft either a one-page infographic or a series of slides explaining what we intend to disclose point by point.

3. Data Circles Infographic PowerPoint Template

An alternative to the pie chart and donut chart diagrams, this template features a series of curved shapes with bubble callouts as ways of presenting data. Expand the information for each arch in the text placeholder areas.

4. Colorful Metrics Dashboard for Data Presentation

This versatile dashboard template helps us in the presentation of the data by offering several graphs and methods to convert numbers into graphics. Implement it for e-commerce projects, financial projections, project development, and more.

5. Animated Data Presentation Tools for PowerPoint & Google Slides

A slide deck filled with most of the tools mentioned in this article, from bar charts, column charts, treemap graphs, pie charts, histogram, etc. Animated effects make each slide look dynamic when sharing data with stakeholders.

6. Statistics Waffle Charts PPT Template for Data Presentations

This PPT template helps us how to present data beyond the typical pie chart representation. It is widely used for demographics, so it’s a great fit for marketing teams, data science professionals, HR personnel, and more.

7. Data Presentation Dashboard Template for Google Slides

A compendium of tools in dashboard format featuring line graphs, bar charts, column charts, and neatly arranged placeholder text areas. 

8. Weather Dashboard for Data Presentation

Share weather data for agricultural presentation topics, environmental studies, or any kind of presentation that requires a highly visual layout for weather forecasting on a single day. Two color themes are available.

9. Social Media Marketing Dashboard Data Presentation Template

Intended for marketing professionals, this dashboard template for data presentation is a tool for presenting data analytics from social media channels. Two slide layouts featuring line graphs and column charts.

10. Project Management Summary Dashboard Template

A tool crafted for project managers to deliver highly visual reports on a project’s completion, the profits it delivered for the company, and expenses/time required to execute it. 4 different color layouts are available.

11. Profit & Loss Dashboard for PowerPoint and Google Slides

A must-have for finance professionals. This typical profit & loss dashboard includes progress bars, donut charts, column charts, line graphs, and everything that’s required to deliver a comprehensive report about a company’s financial situation.

Overwhelming visuals

One of the mistakes related to using data-presenting methods is including too much data or using overly complex visualizations. They can confuse the audience and dilute the key message.

Inappropriate chart types

Choosing the wrong type of chart for the data at hand can lead to misinterpretation. For example, using a pie chart for data that doesn’t represent parts of a whole is not right.

Lack of context

Failing to provide context or sufficient labeling can make it challenging for the audience to understand the significance of the presented data.

Inconsistency in design

Using inconsistent design elements and color schemes across different visualizations can create confusion and visual disarray.

Failure to provide details

Simply presenting raw data without offering clear insights or takeaways can leave the audience without a meaningful conclusion.

Lack of focus

Not having a clear focus on the key message or main takeaway can result in a presentation that lacks a central theme.

Visual accessibility issues

Overlooking the visual accessibility of charts and graphs can exclude certain audience members who may have difficulty interpreting visual information.

In order to avoid these mistakes in data presentation, presenters can benefit from using presentation templates . These templates provide a structured framework. They ensure consistency, clarity, and an aesthetically pleasing design, enhancing data communication’s overall impact.

Understanding and choosing data presentation types are pivotal in effective communication. Each method serves a unique purpose, so selecting the appropriate one depends on the nature of the data and the message to be conveyed. The diverse array of presentation types offers versatility in visually representing information, from bar charts showing values to pie charts illustrating proportions. 

Using the proper method enhances clarity, engages the audience, and ensures that data sets are not just presented but comprehensively understood. By appreciating the strengths and limitations of different presentation types, communicators can tailor their approach to convey information accurately, developing a deeper connection between data and audience understanding.

[1] Government of Canada, S.C. (2021) 5 Data Visualization 5.2 Bar Chart , 5.2 Bar chart .  https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/bargraph-diagrammeabarres/5214818-eng.htm

[2] Kosslyn, S.M., 1989. Understanding charts and graphs. Applied cognitive psychology, 3(3), pp.185-225. https://apps.dtic.mil/sti/pdfs/ADA183409.pdf

[3] Creating a Dashboard . https://it.tufts.edu/book/export/html/1870

[4] https://www.goldenwestcollege.edu/research/data-and-more/data-dashboards/index.html

[5] https://www.mit.edu/course/21/21.guide/grf-line.htm

[6] Jadeja, M. and Shah, K., 2015, January. Tree-Map: A Visualization Tool for Large Data. In GSB@ SIGIR (pp. 9-13). https://ceur-ws.org/Vol-1393/gsb15proceedings.pdf#page=15

[7] Heat Maps and Quilt Plots. https://www.publichealth.columbia.edu/research/population-health-methods/heat-maps-and-quilt-plots

[8] EIU QGIS WORKSHOP. https://www.eiu.edu/qgisworkshop/heatmaps.php

[9] About Pie Charts.  https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c8.htm

[10] Histograms. https://sites.utexas.edu/sos/guided/descriptive/numericaldd/descriptiven2/histogram/ [11] https://asq.org/quality-resources/scatter-diagram

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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

• JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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What Is Data Visualization: Brief Theory, Useful Tips and Awesome Examples

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By Al Boicheva

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Updated: June 23, 2022

To create data visualization in order to present your data is no longer just a nice to have skill. Now, the skill to effectively sort and communicate your data through charts is a must-have for any business in any field that deals with data. Data visualization helps businesses quickly make sense of complex data and start making decisions based on that data. This is why today we’ll talk about what is data visualization. We’ll discuss how and why does it work, what type of charts to choose in what cases, how to create effective charts, and, of course, end with beautiful examples.

So let’s jump right in. As usual, don’t hesitate to fast-travel to a particular section of your interest.

Article overview: 1. What Does Data Visualization Mean? 2. How Does it Work? 3. When to Use it? 4. Why Use it? 5. Types of Data Visualization 6. Data Visualization VS Infographics: 5 Main Differences 7. How to Create Effective Data Visualization?: 5 Useful Tips 8. Examples of Data Visualization

1. What is Data Visualization?

Data Visualization is a graphic representation of data that aims to communicate numerous heavy data in an efficient way that is easier to grasp and understand . In a way, data visualization is the mapping between the original data and graphic elements that determine how the attributes of these elements vary. The visualization is usually made by the use of charts, lines, or points, bars, and maps.

• Data Viz is a branch of Descriptive statistics but it requires both design, computer, and statistical skills.
• Aesthetics and functionality go hand in hand to communicate complex statistics in an intuitive way.
• Data Viz tools and technologies are essential for making data-driven decisions.
• It’s a fine balance between form and functionality.
• Every STEM field benefits from understanding data.

2. How Does it Work?

If we can see it, our brains can internalize and reflect on it. This is why it’s much easier and more effective to make sense of a chart and see trends than to read a massive document that would take a lot of time and focus to rationalize. We wouldn’t want to repeat the cliche that humans are visual creatures, but it’s a fact that visualization is much more effective and comprehensive.

In a way, we can say that data Viz is a form of storytelling with the purpose to help us make decisions based on data. Such data might include:

• Tracking sales
• Identifying trends
• Identifying changes
• Monitoring goals
• Monitoring results
• Combining data

3. When to Use it?

Data visualization is useful for companies that deal with lots of data on a daily basis. It’s essential to have your data and trends instantly visible. Better than scrolling through colossal spreadsheets. When the trends stand out instantly this also helps your clients or viewers to understand them instead of getting lost in the clutter of numbers.

With that being said, Data Viz is suitable for:

• Annual reports
• Presentations
• Social media micronarratives
• Informational brochures
• Trend-trafficking
• Candlestick chart for financial analysis
• Determining routes

Common cases when data visualization sees use are in sales, marketing, healthcare, science, finances, politics, and logistics.

4. Why Use it?

Short answer: decision making. Data Visualization comes with the undeniable benefits of quickly recognizing patterns and interpret data. More specifically, it is an invaluable tool to determine the following cases.

• Identifying correlations between the relationship of variables.
• Getting market insights about audience behavior.
• Determining value vs risk metrics.
• Monitoring trends over time.
• Examining rates and potential through frequency.
• Ability to react to changes.

5. Types of Data Visualization

As you probably already guessed, Data Viz is much more than simple pie charts and graphs styled in a visually appealing way. The methods that this branch uses to visualize statistics include a series of effective types.

Map visualization is a great method to analyze and display geographically related information and present it accurately via maps. This intuitive way aims to distribute data by region. Since maps can be 2D or 3D, static or dynamic, there are numerous combinations one can use in order to create a Data Viz map.

COVID-19 Spending Data Visualization POGO by George Railean

The most common ones, however, are:

• Regional Maps: Classic maps that display countries, cities, or districts. They often represent data in different colors for different characteristics in each region.
• Line Maps: They usually contain space and time and are ideal for routing, especially for driving or taxi routes in the area due to their analysis of specific scenes.
• Point Maps: These maps distribute data of geographic information. They are ideal for businesses to pinpoint the exact locations of their buildings in a region.
• Heat Maps: They indicate the weight of a geographical area based on a specific property. For example, a heat map may distribute the saturation of infected people by area.

Charts present data in the form of graphs, diagrams, and tables. They are often confused with graphs since graphs are indeed a subcategory of charts. However, there is a small difference: graphs show the mathematical relationship between groups of data and is only one of the chart methods to represent data.

Infographic Data Visualization by Madeline VanRemmen

With that out of the way, let’s talk about the most basic types of charts in data visualization.

They use a series of bars that illustrate data development.  They are ideal for lighter data and follow trends of no more than three variables or else, the bars become cluttered and hard to comprehend. Ideal for year-on-year comparisons and monthly breakdowns.

These familiar circular graphs divide data into portions. The bigger the slice, the bigger the portion. They are ideal for depicting sections of a whole and their sum must always be 100%. Avoid pie charts when you need to show data development over time or lack a value for any of the portions. Doughnut charts have the same use as pie charts.

They use a line or more than one lines that show development over time. It allows tracking multiple variables at the same time. A great example is tracking product sales by a brand over the years. Area charts have the same use as line charts.

Scatter Plot

These charts allow you to see patterns through data visualization. They have an x-axis and a y-axis for two different values. For example, if your x-axis contains information about car prices while the y-axis is about salaries, the positive or negative relationship will tell you about what a person’s car tells about their salary.

Unlike the charts we just discussed, tables show data in almost a raw format. They are ideal when your data is hard to present visually and aim to show specific numerical data that one is supposed to read rather than visualize.

Data Visualisation | To bee or not to bee by Aishwarya Anand Singh

For example, charts are perfect to display data about a particular illness over a time period in a particular area, but a table comes to better use when you also need to understand specifics such as causes, outcomes, relapses, a period of treatment, and so on.

6. Data Visualization VS Infographics

5 main differences.

They are not that different as both visually represent data. It is often you search for infographics and find images titled Data Visualization and the other way around. In many cases, however, these titles aren’t misleading. Why is that?

• Data visualization is made of just one element. It could be a map, a chart, or a table. Infographics , on the other hand, often include multiple Data Viz elements.
• Unlike data visualizations that can be simple or extremely complex and heavy, infographics are simple and target wider audiences. The latter is usually comprehensible even to people outside of the field of research the infographic represents.
• Interestingly enough, data Viz doesn’t offer narratives and conclusions, it’s a tool and basis for reaching those. While infographics, in most cases offer a story and a narrative. For example, a data visualization map may have the title “Air pollution saturation by region”, while an infographic with the same data would go “Areas A and B are the most polluted in Country C”.
• Data visualizations can be made in Excel or use other tools that automatically generate the design unless they are set for presentation or publishing. The aesthetics of infographics , however, are of great importance and the designs must be appealing to wider audiences.
• In terms of interaction, data visualizations often offer interactive charts, especially in an online form. Infographics, on the other hand, rarely have interaction and are usually static images.

While on topic, you could also be interested to check out these 50 engaging infographic examples that make complex data look great.

7. Tips to Create Effective Data Visualization

The process is naturally similar to creating Infographics and it revolves around understanding your data and audience. To be more precise, these are the main steps and best practices when it comes to preparing an effective visualization of data for your viewers to instantly understand.

1. Do Your Homework

Preparation is half the work already done. Before you even start visualizing data, you have to be sure you understand that data to the last detail.

Knowing your audience is undeniable another important part of the homework, as different audiences process information differently. Who are the people you’re visualizing data for? How do they process visual data? Is it enough to hand them a single pie chart or you’ll need a more in-depth visual report?

The third part of preparing is to determine exactly what you want to communicate to the audience. What kind of information you’re visualizing and does it reflect your goal?

And last, think about how much data you’ll be working with and take it into account.

2. Choose the Right Type of Chart

In a previous section, we listed the basic chart types that find use in data visualization. To determine best which one suits your work, there are a few things to consider.

• How many variables will you have in a chart?
• How many items will you place for each of your variables?
• What will be the relation between the values (time period, comparison, distributions, etc.)

With that being said, a pie chart would be ideal if you need to present what portions of a whole takes each item. For example, you can use it to showcase what percent of the market share takes a particular product. Pie charts, however, are unsuitable for distributions, comparisons, and following trends through time periods. Bar graphs, scatter plots,s and line graphs are much more effective in those cases.

Another example is how to use time in your charts. It’s way more accurate to use a horizontal axis because time should run left to right. It’s way more visually intuitive.

3. Sort your Data

Start with removing every piece of data that does not add value and is basically excess for the chart. Sometimes, you have to work with a huge amount of data which will inevitably make your chart pretty complex and hard to read. Don’t hesitate to split your information into two or more charts. If that won’t work for you, you could use highlights or change the entire type of chart with something that would fit better.

Tip: When you use bar charts and columns for comparison, sort the information in an ascending or a descending way by value instead of alphabetical order.

4. Use Colors to Your Advantage

In every form of visualization, colors are your best friend and the most powerful tool. They create contrasts, accents, and emphasis and lead the eye intuitively. Even here, color theory is important.

When you design your chart, make sure you don’t use more than 5 or 6 colors. Anything more than that will make your graph overwhelming and hard to read for your viewers. However, color intensity is a different thing that you can use to your advantage. For example, when you compare the same concept in different periods of time, you could sort your data from the lightest shade of your chosen color to its darker one. It creates a strong visual progression, proper to your timeline.

Things to consider when you choose colors:

• Different colors for different categories.
• A consistent color palette for all charts in a series that you will later compare.
• It’s appropriate to use color blind-friendly palettes.

5. Get Inspired

Always put your inspiration to work when you want to be at the top of your game. Look through examples, infographics, and other people’s work and see what works best for each type of data you need to implement.

This Twitter account Data Visualization Society is a great way to start. In the meantime, we’ll also handpick some amazing examples that will get you in the mood to start creating the visuals for your data.

8. Examples for Data Visualization

As another art form, Data Viz is a fertile ground for some amazing well-designed graphs that prove that data is beautiful. Now let’s check out some.

Dark Souls III Experience Data

We start with Meng Hsiao Wei’s personal project presenting his experience with playing Dark Souls 3. It’s a perfect example that infographics and data visualization are tools for personal designs as well. The research is pretty massive yet very professionally sorted into different types of charts for the different concepts. All data visualizations are made with the same color palette and look great in infographics.

My dark souls 3 playing data by Meng Hsiao Wei

Greatest Movies of all Time

Katie Silver has compiled a list of the 100 greatest movies of all time based on critics and crowd reviews. The visualization shows key data points for every movie such as year of release, oscar nominations and wins, budget, gross, IMDB score, genre, filming location, setting of the film, and production studio. All movies are ordered by the release date.

100 Greatest Movies Data Visualization by Katie Silver

The Most Violent Cities

Federica Fragapane shows data for the 50 most violent cities in the world in 2017. The items are arranged on a vertical axis based on population and ordered along the horizontal axis according to the homicide rate.

The Most Violent Cities by Federica Fragapane

Family Businesses as Data

These data visualizations and illustrations were made by Valerio Pellegrini for Perspectives Magazine. They show a pie chart with sector breakdown as well as a scatter plot for contribution for employment.

PERSPECTIVES MAGAZINE – Family Businesses by Valerio Pellegrini

Orbit Map of the Solar System

The map shows data on the orbits of more than 18000 asteroids in the solar system. Each asteroid is shown at its position on New Years’ Eve 1999, colored by type of asteroid.

An Orbit Map of the Solar System by Eleanor Lutz

The Semantics Of Headlines

Katja Flükiger has a take on how headlines tell the story. The data visualization aims to communicate how much is the selling influencing the telling. The project was completed at Maryland Institute College of Art to visualize references to immigration and color-coding the value judgments implied by word choice and context.

The Semantics of Headlines by Katja Flükiger

Moon and Earthquakes

This data visualization works on answering whether the moon is responsible for earthquakes. The chart features the time and intensity of earthquakes in response to the phase and orbit location of the moon.

Moon and Earthquakes by Aishwarya Anand Singh

Dawn of the Nanosats

The visualization shows the satellites launched from 2003 to 2015. The graph represents the type of institutions focused on projects as well as the nations that financed them. On the left, it is shown the number of launches per year and satellite applications.

WIRED UK – Dawn of the by Nanosats by Valerio Pellegrini

Final Words

Data visualization is not only a form of science but also a form of art. Its purpose is to help businesses in any field quickly make sense of complex data and start making decisions based on that data. To make your graphs efficient and easy to read, it’s all about knowing your data and audience. This way you’ll be able to choose the right type of chart and use visual techniques to your advantage.

You may also be interested in some of these related articles:

• Infographics for Marketing: How to Grab and Hold the Attention
• 12 Animated Infographics That Will Engage Your Mind from Start to Finish
• 50 Engaging Infographic Examples That Make Complex Ideas Look Great
• Good Color Combinations That Go Beyond Trends: Inspirational Examples and Ideas

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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.

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Blog Graphic Design What is Data Visualization? (Definition, Examples, Best Practices)

What is Data Visualization? (Definition, Examples, Best Practices)

Written by: Midori Nediger Jun 05, 2020

Words don’t always paint the clearest picture. Raw data doesn’t always tell the most compelling story. 

The human mind is very receptive to visual information. That’s why data visualization is a powerful tool for communication.    

But if “data visualization” sounds tricky and technical don’t worry—it doesn’t have to be. 

This guide will explain the fundamentals of data visualization in a way that anyone can understand. Included are a ton of examples of different types of data visualizations and when to use them for your reports, presentations, marketing, and more.

Table of Contents

• What is data visualization?

What is data visualization used for?

Types of data visualizations.

• How to present data visually  (for businesses, marketers, nonprofits, and education)
• Data visualization examples

Data visualization is used everywhere. 

Businesses use data visualization for reporting, forecasting, and marketing. 

CREATE THIS REPORT TEMPLATE

Nonprofits use data visualizations to put stories and faces to numbers. 

Source:  Bill and Melinda Gates Foundation

Scholars and scientists use data visualization to illustrate concepts and reinforce their arguments.

CREATE THIS MIND MAP TEMPLATE

Reporters use data visualization to show trends and contextualize stories. 

While data visualizations can make your work more professional, they can also be a lot of fun.

What is data visualization? A simple definition of data visualization:

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 a data visualization can be creative and pleasing to look at, it should also be functional in its visual communication of the data. 

Data, especially a lot of data, can be difficult to wrap your head around. Data visualization can help both you and your audience interpret and understand data. 

Data visualizations often use elements of visual storytelling to communicate a message supported by the data. 

There are many situations where you would want to present data visually. 

Data visualization can be used for:

• Making data engaging and easily digestible
• Identifying trends and outliers within a set of data
• Telling a story found within the data
• Reinforcing an argument or opinion
• Highlighting the important parts of a set of data

Let’s look at some examples for each use case.

1. Make data digestible and easy to understand

Often, a large set of numbers can make us go cross-eyed. It can be difficult to find the significance behind rows of data. 

Data visualization allows us to frame the data differently by using illustrations, charts, descriptive text, and engaging design. Visualization also allows us to group and organize data based on categories and themes, which can make it easier to break down into understandable chunks. 

Related : How to Use Data Visualization in Your Infographics

For example, this infographic breaks down the concept of neuroplasticity in an approachable way:

Source: NICABM

The same goes for complex, specialized concepts. It can often be difficult to break down the information in a way that non-specialists will understand. But an infographic that organizes the information, with visuals, can demystify concepts for novice readers.

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The report uses data gathered from over 100,000 customers of HubSpot CRM. In addition to that, you’ll also know about the trends in using visuals in content marketing and the impacts of the pandemic on visual content, from 200+ marketers all over the world interviewed by Venngage.

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2. Identify trends and outliers

If you were to sift through raw data manually, it could take ages to notice patterns, trends or outlying data. But by using data visualization tools like charts, you can sort through a lot of data quickly. 

Even better, charts enable you to pick up on trends a lot quicker than you would sifting through numbers.

For example, here’s a simple chart generated by Google Search Console that shows the change in Google searches for “toilet paper”. As you can see, in March 2020 there was a huge increase in searches for toilet paper:

Source: How to Use SEO Data to Fuel Your Content Marketing Strategy in 2020

This chart shows an outlier in the general trend for toilet paper-related Google searches. The reason for the outlier? The outbreak of COVID-19 in North America. With a simple data visualization, we’ve been able to highlight an outlier and hint at a story behind the data. 

Uploading your data into charts, to create these kinds of visuals is easy. While working on your design in the editor, select a chart from the left panel. Open the chart and find the green IMPORT button under the DATA tab. Then upload the CSV file and your chart automatically visualizes the information. 

3. Tell a story within the data

Numbers on their own don’t tend to evoke an emotional response. But data visualization can tell a story that gives significance to the data. 

Designers use techniques like color theory , illustrations, design style and visual cues to appeal to the emotions of readers, put faces to numbers, and introduce a narrative to the data. 

Related : How to Tell a Story With Data (A Guide for Beginners)

For example, here’s an infographic created by World Vision. In the infographics, numbers are visualized using illustrations of cups. While comparing numbers might impress readers, reinforcing those numbers with illustrations helps to make an even greater impact. 

Source: World Vision

Meanwhile, this infographic uses data to draw attention to an often overlooked issue:

Read More:  The Coronavirus Pandemic and the Refugee Crisis

4. Reinforce an argument or opinion

When it comes to convincing people your opinion is right, they often have to see it to believe it. An effective infographic or chart can make your argument more robust and reinforce your creativity. 

For example, you can use a comparison infographic to compare sides of an argument, different theories, product/service options, pros and cons, and more. Especially if you’re blending data types.

5. Highlight an important point in a set of data

Sometimes we use data visualizations to make it easier for readers to explore the data and come to their own conclusions. But often, we use data visualizations to tell a story, make a particular argument, or encourage readers to come to a specific conclusion. 

Designers use visual cues to direct the eye to different places on a page. Visual cues are shapes, symbols, and colors that point to a specific part of the data visualization, or that make a specific part stand out.

For example, in this data visualization, contrasting colors are used to emphasize the difference in the amount of waste sent to landfills versus recycled waste:

Here’s another example. This time, a red circle and an arrow are used to highlight points on the chart where the numbers show a drop: 

Highlighting specific data points helps your data visualization tell a compelling story.

6. Make books, blog posts, reports and videos more engaging

At Venngage, we use data visualization to make our blog posts more engaging for readers. When we write a blog post or share a post on social media, we like to summarize key points from our content using infographics. 

The added benefit of creating engaging visuals like infographics is that it has enabled our site to be featured in publications like The Wall Street Journal , Mashable , Business Insider , The Huffington Post and more. 

That’s because data visualizations are different from a lot of other types of content people consume on a daily basis. They make your brain work. They combine concrete facts and numbers with impactful visual elements. They make complex concepts easier to grasp. 

Here’s an example of an infographic we made that got a lot of media buzz:

Read the Blog Post: Every Betrayal Ever in Game of Thrones

We created this infographic because a bunch of people on our team are big Game of Thrones fans and we wanted to create a visual that would help other fans follow the show. Because we approached a topic that a lot of people cared about in an original way, the infographic got picked up by a bunch of media sites. 

Whether you’re a website looking to promote your content, a journalist looking for an original angle, or a creative building your portfolio, data visualizations can be an effective way to get people’s attention.

Data visualizations can come in many different forms. People are always coming up with new and creative ways to present data visually. 

Generally speaking, data visualizations usually fall under these main categories:

An infographic is a collection of imagery, charts, and minimal text that gives an easy-to-understand overview of a topic. 

While infographics can take many forms, they can typically be categorized by these infographic types:

• Statistical infographics
• Informational infographics
• Timeline infographics
• Process infographics
• Geographic infographics
• Comparison infographics
• Hierarchical infographics
• List infographics
• Resume infographics

Read More: What is an Infographic? Examples, Templates & Design Tips

Charts 

In the simplest terms, a chart is a graphical representation of data. Charts use visual symbols like line, bars, dots, slices, and icons to represent data points. 

Some of the most common types of charts are:

• Bar graphs /charts
• Line charts
• Bubble charts
• Stacked bar charts
• Word clouds
• Pictographs
• Area charts
• Scatter plot charts
• Multi-series charts

The question that inevitably follows is: what type of chart should I use to visualize my data? Does it matter?

Short answer: yes, it matters. Choosing a type of chart that doesn’t work with your data can end up misrepresenting and skewing your data. 

For example: if you’ve been in the data viz biz for a while, then you may have heard some of the controversy surrounding pie charts. A rookie mistake that people often make is using a pie chart when a bar chart would work better. 

Pie charts display portions of a whole. A pie chart works when you want to compare proportions that are substantially different. Like this:

CREATE THIS CHART TEMPLATE

But when your proportions are similar, a pie chart can make it difficult to tell which slice is bigger than the other. That’s why, in most other cases, a bar chart is a safer bet.

Here is a cheat sheet to help you pick the right type of chart for your data:

Want to make better charts? Make engaging charts with Venngage’s Chart Maker .

Related : How to Choose the Best Types of Charts For Your Data

Similar to a chart, a diagram is a visual representation of information. Diagrams can be both two-dimensional and three-dimensional. 

Some of the most common types of diagrams are:

• Venn diagrams
• Tree diagrams
• SWOT analysis
• Fishbone diagrams
• Use case diagrams

Diagrams are used for mapping out processes, helping with decision making, identifying root causes, connecting ideas, and planning out projects.

CREATE THIS DIAGRAM TEMPLATE

Want to make a diagram ? Create a Venn diagram and other visuals using our free Venn Diagram Maker .

A map is a visual representation of an area of land. Maps show physical features of land like regions, landscapes, cities, roads, and bodies of water. 

Source: National Geographic

A common type of map you have probably come across in your travels is a choropleth map . Choropleth maps use different shades and colors to indicate average quantities. 

For example, a population density map uses varying shades to show the difference in population numbers from region to region:

Create your own map for free with Venngage’s Map Maker .

How to present data visually (data visualization best practices)

While good data visualization will communicate data or information clearly and effectively, bad data visualization will do the opposite. Here are some practical tips for how businesses and organizations can use data visualization to communicate information more effectively. 

Not a designer? No problem. Venngage’s Graph Maker  will help you create better graphs in minutes.

1. Avoid distorting the data

This may be the most important point in this whole blog post. While data visualizations are an opportunity to show off your creative design chops, function should never be sacrificed for fashion. 

The chart styles, colors, shapes, and sizing you use all play a role in how the data is interpreted. If you want to present your data accurately and ethically, then you need to take care to ensure that your data visualization does not present the data falsely. 

There are a number of different ways data can be distorted in a chart. Some common ways data can be distorted are:

• Making the baselines something other than 0 to make numbers seem bigger or smaller than they are – this is called “truncating” a graph
• Compressing or expanding the scale of the Y-axis to make a line or bar seem bigger or smaller than it should be
• Cherry picking data so that only the data points you want to include are on a graph (i.e. only telling part of the story)
• Using the wrong type of chart, graph or diagram for your data
• Going against standard, expected data visualization conventions

Because people use data visualizations to reinforce their opinions, you should always read data visualizations with a critical eye. Often enough, writers may be using data visualization to skew the data in a way that supports their opinions, but that may not be entirely truthful.

Read More: 5 Ways Writers Use Graphs To Mislead You

Want to create an engaging line graph? Use Venngage’s Line Graph Maker to create your own in minutes.

2. Avoid cluttering up your design with “chartjunk”

When it comes to best practices for data visualization, we should turn to one of the grandfather’s of data visualization: Edward Tufte. He coined the term “ chartjunk ”, which refers to the use of unnecessary or confusing design elements that skews or obscures the data in a chart. 

Here’s an example of a data visualization that suffers from chartjunk:

Source: ExcelUser

In this example, the image of the coin is distracting for readers trying to interpret the data. Note how the fonts are tiny – almost unreadable. Mistakes like this are common when a designers tries to put style before function. 

Read More : The Worst Infographics of 2020 (With Lessons for 2021)

3. Tell a story with your data

Data visualizations like infographics give you the space to combine data and narrative structure in one page. Visuals like icons and bold fonts let you highlight important statistics and facts.

For example, you could customize this data visualization infographic template to show the benefit of using your product or service (and post it on social media):

USE THIS TEMPLATE

  This data visualization relies heavily on text and icons to tell the story of its data:

This type of infographic is perfect for those who aren’t as comfortable with charts and graphs. It’s also a great way to showcase original research, get social shares and build brand awareness.

4. Combine different types of data visualizations

While you may choose to keep your data visualization simple, combining multiple types of charts and diagrams can help tell a more rounded story.

Don’t be afraid to combine charts, pictograms and diagrams into one infographic. The result will be a data visualization infographic that is engaging and rich in visual data.

Design Tip: This data visualization infographic would be perfect for nonprofits to customize and include in an email newsletter to increase awareness (and donations).

Or take this data visualization that also combines multiple types of charts, pictograms, and images to engage readers. It could work well in a presentation or report on customer research, customer service scores, quarterly performance and much more:

Design Tip: This infographic could work well in a presentation or report on customer research, customer service scores, quarterly performance and much more.

Make your own bar graph in minutes with our free Bar Graph Maker .

5. Use icons to emphasize important points

Icons are perfect for attracting the eye when scanning a page. (Remember: use visual cues!)

If there are specific data points that you want readers to pay attention to, placing an icon beside it will make it more noticeable:

Design Tip: This infographic template would work well on social media to encourage shares and brand awareness.

You can also pair icons with headers to indicate the beginning of a new section.

Meanwhile, this infographic uses icons like bullet points to emphasize and illustrate important points. 

Design Tip: This infographic would make a great sales piece to promote your course or other service.  

6. Use bold fonts to make text information engaging

A challenge people often face when setting out to visualize information is knowing how much text to include. After all, the point of data visualization is that it presents information visually, rather than a page of text. 

Even if you have a lot of text information, you can still create present data visually. Use bold, interesting fonts to make your data exciting. Just make sure that, above all else, your text is still easy to read.

This data visualization uses different fonts for the headers and body text that are bold but clear. This helps integrate the text into the design and emphasizes particular points:

Design Tip: Nonprofits could use this data visualization infographic in a newsletter or on social media to build awareness, but any business could use it to explain the need for their product or service. 

As a general rule of thumb, stick to no more than three different font types in one infographic.

This infographic uses one font for headers, another font for body text, and a third font for accent text. 

Read More: How to Choose Fonts For Your Designs (With Examples)

Design Tip: Venngage has a library of fonts to choose from. If you can’t find the icon you’re looking for , you can always request they be added. Our online editor has a chat box with 24/7 customer support.

7. Use colors strategically in your design

In design, colors are as functional as they are fashionable. You can use colors to emphasize points, categorize information, show movement or progression, and more. 

For example, this chart uses color to categorize data:

Design Tip : This pie chart can actually be customized in many ways. Human resources could provide a monthly update of people hired by department, nonprofits could show a breakdown of how they spent donations and real estate agents could show the average price of homes sold by neighbourhood.

You can also use light colored text and icons on dark backgrounds to make them stand out. Consider the mood that you want to convey with your infographic and pick colors that will reflect that mood. You can also use contrasting colors from your brand color palette.

This infographic template uses a bold combination of pinks and purples to give the data impact:

Read More: How to Pick Colors to Captivate Readers and Communicate Effectively

8. Show how parts make up a whole

It can be difficult to break a big topic down into smaller parts. Data visualization can make it a lot easier for people to conceptualize how parts make up a whole.

Using one focus visual, diagram or chart can convey parts of a whole more effectively than a text list can. Look at how this infographic neatly visualizes how marketers use blogging as part of their strategy:

Design Tip: Human resources could use this graphic to show the results of a company survey. Or consultants could promote their services by showing their success rates.

Or look at how this infographic template uses one focus visual to illustrate the nutritional makeup of a banana:

CREATE THIS FLYER TEMPLATE

9. Focus on one amazing statistic

If you are preparing a presentation, it’s best not to try and cram too many visuals into one slide. Instead, focus on one awe-inspiring statistic and make that the focus of your slide.

Use one focus visual to give the statistic even more impact. Smaller visuals like this are ideal for sharing on social media, like in this example:

Design Tip: You can easily swap out the icon above (of Ontario, Canada) using Venngage’s drag-and-drop online editor and its in-editor library of icons. Click on the template above to get started.

This template also focuses on one key statistic and offers some supporting information in the bar on the side:

10. Optimize your data visualization for mobile

Complex, information-packed infographics are great for spicing up reports, blog posts, handouts, and more. But they’re not always the best for mobile viewing. 

To optimize your data visualization for mobile viewing, use one focus chart or icon and big, legible font. You can create a series of mobile-optimized infographics to share multiple data points in a super original and attention-grabbing way.

For example, this infographic uses concise text and one chart to cut to the core message behind the data:

CREATE THIS SOCIAL MEDIA TEMPLATE

Some amazing data visualization examples

Here are some of the best data visualization examples I’ve come across in my years writing about data viz. 

Evolution of Marketing Infographic

Graphic Design Trends Infographic

Stop Shark Finning Nonprofit Infographic

Source: Ripetungi

Coronavirus Impact on Environment Data Visualization

What Disney Characters Tell Us About Color Theory

World’s Deadliest Animal Infographic

Source: Bill and Melinda Gates Foundation

The Secret Recipe For a Viral Creepypasta

Read More: Creepypasta Study: The Secret Recipe For a Viral Horror Story

The Hero’s Journey Infographic

Read More: What Your 6 Favorite Movies Have in Common

Emotional Self Care Guide Infographic

Source: Carley Schweet

Want to look at more amazing data visualization? Read More: 50+ Infographic Ideas, Examples & Templates for 2020 (For Marketers, Nonprofits, Schools, Healthcare Workers, and more)

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Data Visualization: Understanding How To Visualize Your Data

• PowerPoint Tutorials
• Charts & Data Viz
• April 22, 2019

What is data visualization?

• 90% of the information transmitted to the brain is visual
• Humans process images 60,000 times faster than text
• 70% of our sensory receptors are in our eyes
• 65% of people are visual learners

Why is data visualization important?

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2. Gives meaning to your data

3. Saves time

Instead of spending the time trying to figure out what the facts and figures mean, your audience members can ENGAGE with the meaning. A visual representation allows you to analyze huge amounts of info in the blink of an eye. As we know, the human eye can recognize and process visual information much faster than text.

4. Makes for better decision making

Assuming your data visualizations contain correct data and are done properly, you’ll not only be able to make decisions faster, but they will be based on data that you fully comprehend.

5. Is more shareable and digestible

One of the best things about data visualization is that they are accessible and easier to share across departments, with colleagues, your boss, or with a large audience. They can be inserted in your PowerPoint presentation, printed for seminar handouts, or even posted and shared on social media.

For example, below is a data visualization superimposing the Titanic over the world’s new cruise ship (the Allure of the Seas) to demonstrate that the new ship is almost 5 times bigger.

Data Visualization Examples

As stated in the research report “ Information Animation Applications in the Capital Markets ,”

The impact of your visual data is only be good as the visualisation! Here are a few great examples of successful data visualizations to give you an idea of the best way to deliver complex information through visuals:

http://www.visualinformation.info/visualizing-the-national-debt-infographic/

It’s important to choose the right visualization for the right information. Here are some of the most common data visualization techniques:

Column Chart Example

Bar charts are one of the most popular data visualizations and most commonly used for comparing items over a horizontal time frame, moving left to right.

These specific data visualizations usually work best with less than 15 data points. If you have more than 15 data points, I recommend using a line chart instead.

You will see this type of data frequently used for showing sales figures over a specific time period, with the years running along the x-axis and the unit running along the Y axis, as you can see in the example below showing Apple’s Revenue from 2014 to 2018.

Bar Chart Example

Similar to the bar chart, the column chart is used to compare multiple values outside of a horizontal based time frame.

This works well for survey data, responses or any other non-date based category. For this type of data visualization you will see the category running up and down

You can also use two different column charts to show how a category has changed over a specific time period.

The two-sided bar chart is a great way to show changes in specific categories over two specific time periods, without having to show all of the information in between those two dates.

Pie Chart and Doughnut Chart Examples

Another familiar visualization technique is the humble doughnut chart (one step better than a pie chart).

The key to making your doughnut charts or pie charts clearer, is to break down your data into two pieces of data to highlight the point you want to make.

For example, if you point is that 85% of survey respondents are using 4 or 5 programs, combine those pieces together and highlight it again the other data as contrasted in the visual below.

Timeline Example

A timeline is a great data visualization technique when you wish to show data in a chronological order and highlighting those important points in time.

To create a Timeline, simply layout your data points along a PowerPoint shape, and mark the data off to visually see your overall project.

If you need to create lots of time based timelines like this, I recommend checking out  Office Timeline . Their PowerPoint app makes creating timelines and Gantt charts extremely easy.

Venn Diagram Example

The Venn Diagram is a great technique for comparing and contrasting ideas and concepts, especially when showing an overlapping piece is important.

For example, in the simple Venn diagram below, your point could be that for a successful launch, you need strategy, execution and commitment.

For help making a Venn diagram in PowerPoint, read our guide here .

Gantt Chart Example

Gantt Charts are particularly useful for project managers as a visualisation technique to show when different phases of a project are starting and stopping over a specific timeline.

Below is an example of a Gantt chart (with a timeline) built entirely using PowerPoint shapes.

Gantt charts like this allow you to organize tasks, team members, timing, potential problems, etc.

They can also be useful for showing multiple categories of continuous data over a period of time.

Infographic Example

Infographics are the business buzzword of the moment. Infographics are a great way to deliver facts and figures in a narrative format which is both attractive and easy to read. They are particularly useful to explain case studies or to summarize complex reports.

You can create your own infographic simply by adding icons, graphics, charts, and formatting your numbers in interesting ways.

Below is an example of a simple infographic I created in Microsoft PowerPoint using nothing but the default PowerPoint icons, fonts and charts. You can obviously make them more complicated if you like.

The goal of an infographic like this is to break down your data points into something visual so that users can more easily digest the information you are presenting them.

Dashboard Data Visualization Example

A dashboard is a data visualization that brings multiple pieces of data (charts, graphics, etc.) onto a single slide. That way you can show the overall health of a situation instead of just individual pieces. Or as explained by Smart Data Collective’s Keyan Keihani.

To create a dashboard, simply create individual graphics of the relevant data points you need to see the overall health or performance of your topic.

For example, your dashboard could be as simple as 3 column charts showing annual sales, annual expenses and annual net income as shown below.

To keep the above dashboard consistent, I used the same vertical axis value of 130 billion. That way the data stays consistent across all 3 column charts. I also used a darker color to highlight the year 2020.

Data Visualization Tips

Don’t forget to be specific in your titles.

While data visualization helps your audience understand your data more easily, you’ll still want to label it well. If you don’t properly label your graphics, your entire meaning can be lost.

Give your graph or chart a title and it instantly becomes more readable.

Highlight your main points clearly

Highlighting certain figures, sections or columns helps draw attention to the most essential information and can direct your message.

Add rectangles or to emphasis your main points

Add a ‘call out’ to highlight your main point

Use colors to highlight your main points

Simplify your pie charts.

When presenting complex information, the right data visualizations are critical to the success of your presentation.

The best data in the world won’t save you unless you present it in a way that people can understand.

So the next time you plan on presenting complex information, it’s worth taking the time to create the right visualization.

To learn more about our PowerPoint training and other free presentation resources,  visit us here .

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Data Visualisation in Presentations – Its Importance and How To Use It Effectively

Data Data Visualization Design Presentation Design Visual Data

From sales figures to research findings to classroom statistics, data is an essential part of any presentation. Unfortunately, it can easily become overwhelming and detract from the message if not presented carefully and in a visually appealing manner. Luckily, data visualisation is a powerful tool to help you make your data more engaging and impactful. In this article, we'll explore the world of data visualisation and provide tips on how to make the most of this powerful tool in your presentations. Get ready to discover how to make your data stand out and leave a lasting impression on your audience.

What is Data Visualisation?

Data visualisation is the process of representing data in a graphical or visual format. It includes using charts, graphs, maps, or other visual aids, to help people understand and interpret large or complex data sets. Data visualisation’s goal is to make complex data more accessible, understandable, and actionable by presenting it in a way that is easy to comprehend at a glance.

Making use of data visualisation makes it easier to identify patterns, trends, and correlations that might not be immediately apparent in a table or text-based format. Data visualisation is widely used in a number of fields. These include business, science, engineering, and social sciences to communicate insights and findings to stakeholders, decision-makers, and the general public.

Data visualisation tools are a great way to make data more aesthetically appealing and easier to understand. These tools can range from charts, graphs, maps, and even tables, in some cases. Visualising data allows you to identify patterns, trends, and relationships between different data sets. By using data visualisations, you can easily gain insights into the data and make informed decisions. Furthermore, data visualisation can help to easily communicate complex information and make it more accessible to a wider audience. With the right tools and techniques, data visualisations can help to make data more meaningful and engaging. Check out our article - Choosing the Right Chart for Your Sales Pitch Deck for a glimpse at how charts can be effective for presenting data.

The Importance of Using Data Visualisation in Presentations

Data visualisation is crucial in presentations because it allows the audience to understand complex information quickly and efficiently. Using visual aids enhances the understanding of data, especially when dealing with large amounts of data or complicated concepts. Here are some reasons why data visualisation is important for presentations:

Easy interpretation: Visuals such as charts, graphs, and maps are designed to make data easy to interpret. They present information in a visual way that can be easily understood and remembered by the audience.

Clarity: Visualisation can help to clarify complex information by presenting it in a simple and concise way. This is particularly useful when dealing with data that might be difficult to explain or understand without visuals.

Engaging: Visuals are more engaging than text and can help to maintain the audience's attention throughout the presentation. This is particularly important when dealing with long and complex presentations.

Memory retention: People are known to be highly visual creatures. Studies have shown that people are more likely to remember information presented visually. Data visualisation can help to ensure that the key points of a presentation are remembered long after the presentation has ended.

Persuasion: Data visualisation can be used to persuade the audience of the presenter's argument or point of view. By presenting data in a clear and concise way, the presenter can make a compelling case for their argument.

In summary, data visualisation is an essential tool for presentations as it can help to clarify complex information, engage the audience, improve memory retention, and persuade the audience of the presenter's argument.

5 Ways to Use Data Visualisation

Now that you understand why data visualisation in presentations is so important, let's take a look at some of the ways you can start visualising your data the right way. To create a meaningful and impactful presentation, you must be mindful of how you’re presenting your data. Here are 5 tips on how to effectively visualise data:

Visualise the data: Avoid presenting data in raw text form. Instead create visualisations such as graphs, charts, or diagrams. This will help the audience to quickly and easily understand the information being presented.

Use color to highlight important information: Color can be a powerful tool in drawing attention to important information. Contrasting colors help make certain data points stand out and draw the audience's eye to the key information.

Simplify complex information: Data can be complex and difficult to understand, so it's important to simplify it as much as possible. Clear, concise language helps to break down complex information into smaller, more easily digestible pieces.

Use storytelling: Data can be made more compelling when presented as part of a larger narrative. Use storytelling techniques to frame the data in a way that is engaging and memorable for the audience.

Add context: Data without context can be confusing and difficult to understand. Provide context for the data by explaining what it means and how it fits into the bigger picture. This will help the audience to understand the relevance and importance of the information being presented.

Be Creative with Data Visualisation

Data visualisation is an incredibly useful tool in business presentations and can be easily done in PowerPoint. It allows the audience to instantly understand the message and make an impact. PowerPoint has a wide array of options available for creating data visualisation. These can then easily be incorporated into your presentations with minimal effort and time. However, this isn’t without its challenges. Therefore, if you need help understanding and using data visualisation, you can always trust the professionals to do it for you!

Get in touch with our presentation design team at Deck Sherpa to know more. Check out our website, and visit the Services and Showcase sections to see what we do and what we’ve done. You may also check out the Sherpa Wisdom section for insights on other topics on presentations and presentation design.

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What Is Data Visualization & Why Is It Important?

The sheer amount of data generated today means we need new ways to understand what’s happening in order to take action faster. Every click, transaction, subscription, loyalty card swipe, and social media interaction contributes to a digital footprint that continues to grow exponentially. The result? A massive explosion of data that is revolutionizing the way we live and work. Data visualization, in particular, plays a critical role in presenting data in a meaningful and understandable format. By using a visual representation of data , it’s much easier to identify patterns, trends, and relationships that may not be immediately apparent when sifting through large data sets.

Here’s what we’ll cover in this guide to data visualization: 

• Data Visualization Definition 

Benefits of Data Visualization

Why data visualization is important .

• Types of Data Visualization and Examples
• Evaluating Data Visualization Tools
• Take the Next Step and Start Analyzing With Data Visualization 

‍ Data Visualization Definition

Data visualization is the process of transforming raw data into visual formats, such as charts, graphs, or maps, to help identify patterns, trends, and insights that might not be apparent from numerical data alone. 

Additionally, it enables data to be more accessible, understandable, and impactful, especially when communicating with stakeholders, investors, or team members who may not be familiar with the data.

For example, data visualization could help:

• In retail, gaining insights into customer behavior, purchase patterns, and product performance.
• In finance, monitoring market trends, tracking portfolio performance, and conducting risk analysis. 
• In public health, showing the geographical distribution of outbreaks and helping track the spread of infectious diseases.
• In supply chain industries, tracking inventory levels, monitoring logistics operations, and optimizing resource allocation. 
• In sports, evaluating player performance, game strategies, and match statistics.
• In education, tracking student performance, analyzing learning outcomes, and identifying areas for improvement.

Data visualization has several benefits for businesses including: the ability to process information faster, identify trends at scale, and make data more digestible. Companies regularly use data to make decisions, and through data visualization, can find insights quickly and move to action. Data visualization specifically helps with the following:

• Visualizing patterns and relationships
• Storytelling, including specifically data storytelling
• Accessibility to information 

Exploration

Let’s take a look at each of these benefits in detail. 

‍ Visualize patterns and relationships

Data visualization constitutes an excellent method for the discernment of interconnections and patterns amidst vast collections of information. For example, a scatter plot can be used to display the relationship between two variables, such as the correlation between temperature and sales. This enables users to understand the relationship and identify trends and outliers more quickly and easily.

Read a guide of Sigma’s visual library.

Storytelling

Your audience, whether it's coworkers or clients, want to hear a coherent story from your data. Storytelling with data cannot be done successfully without visualizations. Colorful charting and dynamic pivots are just as important as characters and plots are in a traditional story, so using them to communicate information makes data that much more engaging and memorable for audiences. Data can be complex and convoluted for some audiences, so data storytelling is an approach to convey important information effectively through a captivating narrative. Good visualizations are a vital part of that narrative.  

For example, if an analyst is investigating the performance of e-commerce sales for their retail company over time, they may leverage several data sources such as spreadsheets, calculations, code, etc. to do so. However, when they report these new insights to their stakeholders, the analyst will need to summarize and communicate their findings in a digestible way. 

An easy way the analyst could do this is by using the data to create a map of the U.S. with a color gradient overlaying every state that is lighter or darker based on its total sales volume. This visual story tells the least and most successful retail locations at a glance.

Accessibility / Easily Share Information

Data visualization serves as an invaluable mechanism for the facilitation of accessibility, allowing for the communication of information amongst individuals, even for those who may not usually engage with data , which broadens the audience.

Visualizations help simplify complex information by leveraging people’s ability to naturally recognize patterns. A viewer typically does not have to be taught that bigger means more and that smaller means less. In a case where an analyst wants to highlight the difference in scale between one product’s profitability vs. another, a bar chart can clearly show the user which product is more profitable and by how much, making it easy for even non-technical team members to understand and compare the performance of different products.

Exploration is a key component of successful data visualization. The more flexible charting and dashboarding is, the more follow-up questions end users can ask directly of their data. For example, an interactive dashboard can be used to explore retail sales data over time, enabling users to filter and drill down into the data to identify trends and patterns.

Data visualization exploration is often associated with the concept of “drill downs.” Drill downs in data visualization refer to the process of starting with an overview of data and then narrowing the focus to more specific aspects of it. As an example, one might start with a visualization of global climate data and drill down to data about a specific country, a specific state, a specific city, or even a specific neighborhood within that city. Each drill down reveals more precise, detailed, and nuanced information. 

The main goal of data visualization is that it helps unify and bring teams onto the same page. The human mind is wired to grasp visual information more effortlessly than raw data in spreadsheets or detailed reports. Thus, graphical representation of voluminous and intricate data is more user-friendly. Data visualization offers a swift and straightforward method to communicate ideas in a universally understood format, with the added benefit of enabling scenario testing through minor modifications.

By translating information into visual form, it ensures everyone, irrespective of the complexity of the data or the depth of the analysis, can share a unified understanding. Any industry can benefit from using data visualization, because pretty much every industry relies on data to power it. That includes finance, marketing, consumer goods, education, government, sports, history, and many more. ‍ Another thing to keep in mind is that data visualization can be a double-edged sword. For example, charts can be manipulated and skewed to force a desired outcome. Ungoverned, static, desktop tools can become the wild west in suggesting an inaccurate outcome “proven by data.” Even in the cases where the visualization builder is acting in good faith, there are still pitfalls to watch out for. Always be considerate of:

• Individual outliers having an outsized impact, skewing the visual direction of a chart
• The need for for business users to see the underlying data
• Allowing for transparency down to row-level detail in data sets

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Types of Data Visualizations & Examples

There is a long list of types of data visualization techniques and methods that can be used to represent data. While no type of data visualization is perfect, we’ll walk through different examples and when to apply each one. 

We’ll be looking at:

• Line charts and area charts
• Scatter plots 
• Pivot tables
• Box-and-whisker plots
• Sankey charts 

Tables, although more commonly thought of as a data source, can also be considered a type of data visualization. Especially when conditional formatting is applied to the table’s rows and columns, the data within the table becomes more visually engaging and informative. With conditional formatting, important insights and patterns can be highlighted, making it easier for viewers to identify trends and outliers at a glance. Additionally, tables offer a structured and organized way to present information, allowing for a comprehensive comparison of data points, which further enhances data understanding and analysis. ‍ For example, Sigma’s UI is based on a spreadsheet-like interface, which means almost everything in Sigma begins in a table format. That said, you can also create visual tables that display a smaller amount of data in order to tell a clearer story. In data visualization, tables are a simplified way of representing this interface. 

When to use tables:

• For detailed numeric comparisons, or when precision of data is key
• For displaying multidimensional data; tables can handle this complexity quite well

When to avoid tables: 

• When patterns, trends, or relationships need to be highlighted at a glance
• When dealing with large amounts of data

Pie charts —similar to stacked bar charts—are useful for displaying categorical data, such as market share or customer demographics. Pie charts are often used to display data that can be divided into categories or subgroups, and to show how each category or subgroup contributes to the whole. For example, a pie chart could be used to show the proportion of sales for different product categories in a given period of time, or the percent of a company's revenue broken down by various regions.

When to use pie charts:

• You want to display a proportion or percentage of a whole
• You’re visualizing only seven categories or less

When to avoid pie charts:

• You’re visualizing more than seven categories
• You want to compare something with more details, rather than just proportion
• You want to display and pinpoint exact values 

A bar chart, or bar graph, constitutes a variety of graphs that employ rectangular bars to depict data. These bars can be oriented either horizontally or vertically, with their extent being directly proportional to the numerical values they are intended to embody. Predominantly utilized for juxtaposing data across disparate categories or illustrating shifts in data over temporal progressions, bar charts offer a straightforward, yet potent means of conveying information visually. They frequently function as the initial tool in the exploratory process of data investigation.

When to use bar charts:

• Emphasizing and contrasting different sets of data, making the disparities or similarities between categories clear
• To display a subset of a larger dataset

When to avoid bar charts: 

• When a particular field encompasses an overwhelming variety of data types
• When the differences between fields are too subtle, or when these differences exist on different scales, as it could lead to confusion or misinterpretation

Line Charts & Area Charts

Line charts and area charts are two types of charts that are commonly used to visualize data trends over time. A line chart, also called a line graph, is a distinct type of graphical representation that exhibits information in the form of a multitude of data points, which are interconnected by unbroken lines. These line charts are typically employed to demonstrate transformations in data over a certain duration, where the horizontal axis symbolizes time, and the vertical axis signifies the values under scrutiny. Furthermore, they can serve to juxtapose several series of data within the same chart, or to graphically illustrate predicted time periods. 

For example, a line chart can be used to visualize a company's stock prices over the course of a year. Similarly, an area chart can be used to visualize the temperature changes over a day.

When to use line charts:

• When you’re displaying time-based continuous data 
• When you have multiple series or larger datasets 

When to avoid line charts:

• When you have smaller datasets, bar charts are likely a better way to present the information 
• Avoid when you need to compare multiple categories at once

When to use area charts:

• When you want to display the volume of the data you have 
• When comparing data across more than one time period 

When to avoid area charts:

• Avoid if you need to compare multiple categories, as well as when you need to examine the specific data value

Scatter Plots

A scatter plot , also called a scatter chart or scatter graph, is a specialized form of chart that demonstrates the correlation between two distinct variables by mapping them as a succession of individual data points. Each data point denotes a combined value of the two variables, with its specific placement within the chart dictated by these values.

Scatter charts prove instrumental in discerning patterns and trends within data, and they also help us understand how strong and in what direction the relationship is between two variables. They also serve as effective tools for identifying outliers, or those data points that deviate significantly from anticipated values based on the pattern displayed by other data points. These charts find widespread use across a range of fields including, but not limited to, statistics, engineering, and social sciences, for the purpose of analyzing and visualizing intricate data sets. In the realm of business, they are frequently utilized to identify correlations between different variables, for instance, examining the relationship between marketing outlays and resultant sales revenue. ‍ For example, a scatter plot might be used to visualize the relationship between the age and income of a group of people. Another example would be to plot the correlation between the amount of rainfall and the crop yield for a particular region.

When to use scatter plots:

• Highlight correlations within your data
• They are useful tools for statistical investigations
• Consider scatter plots to reveal underlying patterns or trends

When to avoid scatter plots:

• For smaller datasets, scatter plots may not be optimal
• Avoid scatter plots for excessively large datasets to prevent unintelligible data clustering
• If your data lacks correlations, scatter plots may not be the best choice

Pivot Tables

While pivot tables may not be what first comes to mind for data visualization, they can give important context with hard numbers and provide strong visual indicators through formatting. ‍ Pivot tables can also be enhanced with conditional formatting to provide color scales that make performance trends more visible. Data bars can also be added to cells to run either red or green for positive and negative values. 

When to Use Pivot Tables:

• Cohort analysis performance trends or portfolio analysis with a mix of positive and negative values

What Not to Use Pivot Tables:

• When your dataset is too large to get a good understanding of the whole
• When data can easily be summarized with a bar chart instead

An example of a pivot table, where colors are used to show positive or negative progress on a company’s portfolio. The user can pivot the table to show multiple categories in different ways.

A heat map is a type of chart that uses color to represent data values. It is often used to visualize data that is organized in a matrix or table format. The color of each cell in the matrix is determined by the value of the corresponding data point. Heat maps are best used when analyzing data that is organized in a two-dimensional grid or matrix.

For example, a heat map can be used to visualize a company's website traffic, where the rows represent different pages on the website, and the columns represent different periods of time.

When to use heat maps:

• When you need to visualize the density or intensity of variables
• When you want to display patterns or trends over time or space 

When to avoid heat maps:

• When precise values are needed; heat maps are better at showing relative differences rather than precise values
• When working with small data sets 

A tree map is a type of chart that is used to visualize hierarchical data. It consists of a series of nested rectangles, where the size and color of each rectangle represent a different variable. Tree maps are best used when analyzing data that has a hierarchical structure.

For example, a tree map can be used to visualize the market share of different companies in an industry. The largest rectangle would represent the entire industry, with smaller rectangles representing the market share of each individual company.

When to use tree maps:

• When you want to visualize hierarchical data
• When you need to illustrate the proportion of different categories within a whole 

When to avoid tree maps:

• When exact values are important
• When there are too many categories

Box-and-Whisker Plots

Box plots are useful for quickly summarizing the distribution of a dataset, particularly its central tendency and variability. For example, a box-and-whisker plot can be used to visualize the test scores of a group of students. 

Colloquially recognized as a box-and-whisker plot, a box plot is a distinct form of chart that showcases the distribution of a collection of numerical data through its quartile divisions. Box plots serve as efficient tools for rapidly encapsulating the distribution of a dataset, specifically its central propensity and variability. 

A box-and-whisker plot consists of a rectangle (the "box") and a pair of "whiskers" that extend from it. The box embodies the middle 50% of the data, with the lower boundary of the box signaling the first quartile (25th percentile) and the upper boundary of the box indicating the third quartile (75th percentile). The line situated within the box signifies the median value of the data. The whiskers project from the box to the minimum and maximum values of the data, or to a designated distance from the box referred to as the "fences." Any data points that reside outside the whiskers or fences are categorized as outliers and are plotted as individual points. When to use box plot charts:

• When you want to display data spread and skewness
• When showcasing the distribution of data, including the range, quartiles, and potential outliers
• When comparing multiple groups or categories side-by-side; they allow for easy comparison of different distributions.

When to avoid box plot charts:

• If you need to show more detail, since box plots focus on a high-level summary 
• When individual data points are important to the story you’re telling
• When your audience isn’t familiar with them, since they can sometimes be less intuitive than other types of visualizations

A histogram is a type of chart that displays the distribution of a dataset. It consists of a series of vertical bars, where the height of each bar represents the number of observations in a particular range. Histograms are best used when analyzing continuous data. It’s used the most when you want to understand the frequency distribution of a numerical variable, like height, weight, or age. For example, a histogram can be used to visualize the distribution of heights in a population. Read more about building histograms in Sigma here.

When to Use a Histogram:

• When understanding the shape of a distribution; for example, whether it’s symmetric, skewed to the left or right, or bimodal
• When identifying outliers, like which data points are significantly different from the rest of the data
• When comparing distribution of a variable across different groups, such as males and females, or different age groups.
• To set boundaries for data ranges; for example, you might use a histogram to determine what constitutes a "normal" or "abnormal" value for a particular variable

When to Avoid a Histogram:

• When you need to look at multiple dimensions at the same time
• If your data isn’t all on the same scale

Sankey Charts

We end our guide with the controversial Sankey chart. A Sankey chart is a type of diagram that illustrates the movement or transfer of data, resources, or quantities through various stages of a system or process. Common applications of Sankey charts include visualizing complex sequences like energy usage, material distribution, or even a website's user journey. The structure of the chart includes nodes and links—with nodes representing the starting points, endpoints, or intermediate steps, and links depicting the transition of quantities or data between these nodes.

The thickness of the links in a Sankey chart directly corresponds to the volume of data or resources being moved, offering an intuitive comparison of the relative sizes of these transfers. They can be invaluable for recognizing inefficiencies, bottlenecks, or potential areas for enhancement in a system or process. These charts serve as a powerful tool for communicating complex information in a straightforward and comprehensible way. However, if there are too many nodes or links, Sankey charts can become cluttered and challenging to interpret, hence their use should be considerate and targeted.

‍ When to use Sankey charts:

• When you want to show the data as part of a process

When to avoid Sankey charts:

• When it starts to feel too confusing, which can quickly happen when there are too many nodes or links
• When you need to see exact values, it might not be the most intuitive option. 

Evaluating Data Visualization Tools 

Data visualization tools have become increasingly popular in recent years, with a wide variety of options available to choose from. However, determining which tool best suits your needs can be challenging with so many options. When evaluating data visualization tools, there are several key questions to consider:

• What are your goals and needs?   It's crucial to clearly understand your goals and needs before selecting a data visualization tool. Are you looking to explore your data, communicate a specific message, or both? Understanding your objectives will help you choose the right tool for your project.
• What features do you require?   Different data visualization tools come with different features. Before selecting a tool, you should consider what features you need to achieve your goals. For example, do you require interactive capabilities or the ability to create custom visualizations?
• Where will your data come from?   The source of your data is another critical factor to consider when selecting a data visualization tool. Some tools are better suited for specific types of data, such as structured or unstructured data, while others may require specific file formats or data storage solutions.
• Where will you need to see your data?   Different data visualization tools may be more suitable for specific platforms or devices. For example, some tools may be optimized for mobile devices, while others are designed for desktop computers or specific web browsers. You may also be interested in embedding visualizations elsewhere , such as internal applications or external portals.
• Where would you like to publish your visualization?   Finally, consider where you would like to publish your visualization. Some tools may provide built-in publishing capabilities, while others may require you to export your visualization to a separate platform. Selecting a tool that supports your publishing needs is important to ensure your visualization reaches your intended audience.

By considering these key questions, you can evaluate different data visualization tools and select the one that best meets your needs.

Read a side-by-side comparison of Sigma against similar BI tools.

Take the Next Step & Start Analyzing With Data Visualization

Data visualization is a powerful tool for understanding and communicating complex data. While there are many data visualization tools on the market, Sigma offers an intuitive and familiar spreadsheet interface that allows users to easily explore, analyze, and collaborate on their data. 

Explore Sigma’s capabilities and start transforming your data today via a free trial of Sigma .

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The step-by-step approach to data visualization in presentations

We live in an age of digital information flow. Credibility and verifiability are important currencies when it comes to conveying information. While facts and figures have gained new significance, they often struggle to get noticed alongside the much better performing visual content.

Data visualization is the art of converting contexts, circumstances, and developments derived from verifiable data into visual objects in such a way that they are, ideally, graspable and comprehensible right from the first glance. Infographics, pie charts, bar graphs, or radar charts are all popular formats.

But how can larger volumes of data also be used to paint an informative picture? In five steps, we’ll explain what you need to remember when creating charts, diagrams, and graphs.

The data assessment

Companies generally have access to a wide range of data. Be selective when choosing the data you want to communicate. Assess the informative value and knowledge gain offered by the data sets. When it comes to graphs, charts and diagrams, the same approach applies as for all other PowerPoint-presentation content: Keep your presentation as short and concise as possible, and avoid overloading it with superfluous information.

Data is relevant if, for example:

• It shows developments that enable forecasts to be made for the future.
• It describes pattern deviations that indicate changes in trends.
• It highlights contexts/correlations previously unheard of.
• It confirms previously unverifiable assumptions. 

Our tip: If you’re not sure whether certain data is important for your presentation, put it on a backup slide or in the appendix. If a relevant discussion or question comes up during your presentation, you can instantly access it then. This will show you’re well prepared and have thought your presentation through very carefully. And can focus on what’s most important for your audience.

The message

Data is often multidimensional, which makes it complex and difficult to understand. When it comes to data visualization, it is important to concentrate on the main findings and make clear, simple statements. But this also means you need to leave out anything that’s unnecessary. Excessive detail does not make a chart better. A core statement is not made more concise by having extra aspects added on to it. When handling data, people often fall into the trap of following an almost scientific approach. But most business presentations are not of a scientific nature; they’re about key findings. And the more scaled back these are, the clearer they become.

Our tip: Do you feel like you lose too much information by reducing data to a single message? Then check whether you can address the individual aspects separately. Make the most of visual storytelling, and combine various data representations into a well-structured narrative.

The target audience

Carefully consider the target audience of your presentation, and think about how familiar they are with handling data. While data is seen as proof of certain statements, it often also raises new questions. Always state the data source, and be prepared for questions about the collection method, time frame, and contexts/correlations.

Try not to overwhelm your target audience. Remember, even though you’ve been working with the tables and charts for a long time, this is the first time your audience will be seeing them.

Our tip: Remember the 15-second rule for presenting data. Anything that doesn’t trigger a “lightbulb moment” among your target audience within this time frame is definitely too complex. Test it on your coworkers and scale back your information if necessary.

When visualizing your data, only apply principles that will help your audience navigate and grasp the message. Making a table colorful because it looks nicer is not constructive; it might even end up being confusing. The human brain takes in lots of information subconsciously and sorts it. And there are certain perception principles you can utilize. Give your audience whatever they need to understand and easily grasp what you are showing, such as:

• A clear verbal introduction
• A clear heading conveying the slide’s core statement
• Colors of emotional significance (red=danger, yellow=neutral, green=desirable, corporate colors and competitor colors)
• Logical reading order (left to right and clockwise, e.g. based on importance and percentage in a pie chart)
• Labelling and accompanying texts reduced to the absolutely necessary, avoid repetition
• Learned symbols, e.g. symbols for female and male or flags
• Important information highlighted—using size, color, or distinguishing elements

Implementation

Only once you are familiar with your data and have identified the main statement can you determine the type of data suitable. It’s not just the visual attractiveness of a display format that is crucial; its function is too. Situations can be shown differently to processes. Contexts/correlations require a different format to percentages. A list of the most common display formats and their areas of use is available  here .

When visualizing your data, be sure to take into account your company’s corporate design. PowerPoint masters are unfortunately often patchy when it comes to data visualization. This is a shame, because uniformly designed tables and charts convey a sense of professionalism, and underline the credibility of the information. Data visualizations are a key part of branding.

Contact us  if you want to know how to professionally create data in your corporate design in PowerPoint.

Also remember our 5 criteria for successful PowerPoint presentations, and follow the  OSCAR principle , which can also be applied to data visualization.

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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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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

The Need for Data Visualization in Presentations

August 5, 2013 / Blog, In The News, PowerPoint Design, Presentation Science, Rick Enrico Blog, Tips & Tricks Custom Designed Presentations, data, Data Visualization, PowerPoint Agency, PowerPoint Design, PowerPoint design experts, PowerPoint specialist, presentation, Presentation Agency, Presentation Company, Presentation Consultation, Presentation Designers, Presentation Firm, professional presentation

All the numbers, figures, facts and stats that you gathered for your professional presentation are all useless…let me tell you why.

Let me explain through this: “ If a tree falls  in a forest and no one is around to hear it, does it make a sound?” My answer to that philosophical question would be “who cares?” If the tree is not in my sight, my space, causing me any problem, or bringing me any benefit, why am I interested?

Much like that tree, and its sound or soundless noise, is of no direct interest to me, neither is your data, at least not yet. At the moment, your data is like that tree, it is a vague and irrelevant issue to the typical audience member. They don’t see, hear, feel, or need the sound or the tree, so to them, it is useless.  The only way your complicated bar charts, pie graphs, numbers or percentages (your tree, if you will) will make me care is if they are relevant to me. The first step to relevance is making your data understandable.

What is data visualization?

Data Visualization is the idea of visualizing data away from your computer and in so doing, making sense of your complex issues. Once your audience can make sense of your data, they can begin to see any personal relevance or interest in it. This is where data visualization comes in again.

Data visualization has the power to harness the relationships of  meaning ,  cause  and dependency . These three elements are what people use to judge personal relevance and interest on. If your audience understands the true meaning of what your showing, then sees what it can cause, or has caused to them personally, and finally understands that the next move is dependent on them, then you will have effectively made your data useful.

How to visualize your data

Extract the data from your current line graph, bar graph, pie chart, statistics or whatever medium you are displaying your data through. Then take a relevant photograph, or drawing, or design, and incorporate the data in it. For example:

“It’s one thing to see bar charts for average daily water consumption per capita in the United States, Mexico, the United Kingdom, China and Haiti…”

“… it’s another to see those bar charts shown as cups of water filled to different levels.”

The key point to understand with this is that the design is meant to support and complement the data. For example:

• data on football fan attendance visualized by having fans hold up helmets to make a bar chart;
• data on declining education budgets visualized as a bar chart traced in the dusty rear window of a preschool;
• data on increasing dental costs can be visualized in a toothpaste line graph on a dollar bill

Additionally it is essential to focus on the central aspect of the visual. Whether it may be photography, hand drawn art, or a mix of the two, be sure it is good quality. Check the lighting, the picture quality, contrast, etc. Keep the photo free of extraneous elements ; they will only confuse the audience. Take you base piece and add some descriptive text and a title in an image-editing program. Pretend it is your original chart or graph .

Science behind visualizations

These visualizations are effective interesting because as humans, we are not yet hardwired to understand and compute the things we see on screens and on paper . We are built to understand the real world, and we are very good at evaluating the things in it, and this lends an immediate grasp of the subject that rectangles on a screen sometimes can’t imitate.

Work Cited:

http://blog.visual.ly/real-life-dataviz/

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Before jumping into the term “Data Visualization”, let’s have a brief discussion on the term “Data Science” because these two terms are interrelated. But how? Let’s understand. So, in simple terms, “ Data Science is the science of analyzing raw data using statistics and machine learning techniques to conclude that information “. But do you know what is Data Science Pipeline? First, we will understand the basics of Data Visualization & will see why is it important before moving to the Data Science Pipeline.

What is Data Visualization?

Data visualization is the graphical representation of information and data in a pictorial or graphical format ( Visualization of Data could be: charts, graphs, and maps). Data visualization tools provide an accessible way to see and understand trends, patterns in data, and outliers. Data visualization tools and technologies are essential to analyzing massive amounts of information and making data-driven decisions. The concept of using pictures is to understand data that has been used for centuries. General types of data visualization are Charts, Tables, Graphs, Maps, and Dashboards.

Categories of Data Visualization

Performing accurate visualization of data is very critical to market research where both numerical and categorical data can be visualized, which helps increase the impact of insights and also helps in reducing the risk of analysis paralysis. So, data visualization is categorized into the following categories:

Data Visualization Categories

• Numerical Data 
• Categorical Data

Let’s understand the visualization of data via a diagram with its all categories.

To read more on this refer to: Categories of Data Visualization

Why is Data Visualization Important?

Let’s take an example. Suppose you compile visualization data of the company’s profits from 2013 to 2023 and create a line chart. It would be very easy to see the line going constantly up with a drop in just 2018. So you can observe in a second that the company has had continuous profits in all the years except a loss in 2018. It would not be that easy to get this information so fast from a data table. This is just one demonstration of the usefulness of data visualization. Let’s see some more reasons why visualization of data is so important.

1. Data Visualization Discovers the Trends in Data

The most important thing that data visualization does is discover the trends in data. After all, it is much easier to observe data trends when all the data is laid out in front of you in a visual form as compared to data in a table. For example, the screenshot below on Tableau demonstrates the sum of sales made by each customer in descending order. However, the color red denotes loss while grey denotes profits. So it is very easy to observe from this visualization that even though some customers may have huge sales, they are still at a loss. This would be very difficult to observe from a table.

2. Data Visualization Provides a Perspective on the Data

Visualizing Data provides a perspective on data by showing its meaning in the larger scheme of things. It demonstrates how particular data references stand concerning the overall data picture. In the data visualization below, the data between sales and profit provides a data perspective concerning these two measures. It also demonstrates that there are very few sales above 12K and higher sales do not necessarily mean a higher profit.

3. Data Visualization Puts the Data into the Correct Context

It isn’t easy to understand the context of the data with data visualization. Since context provides the whole circumstances of the data, it is very difficult to grasp by just reading numbers in a table. In the below data visualization on Tableau , a TreeMap is used to demonstrate the number of sales in each region of the United States. It is very easy to understand from this data visualization that California has the largest number of sales out of the total number since the rectangle for California is the largest. But this information is not easy to understand outside of context without visualizing data.

4. Data Visualization Saves Time

It is definitely faster to gather some insights from the data using data visualization rather than just studying a chart. In the screenshot below on Tableau, it is very easy to identify the states that have suffered a net loss rather than a profit. This is because all the cells with a loss are coloured red using a heat map, so it is obvious states have suffered a loss. Compare this to a normal table where you would need to check each cell to see if it has a negative value to determine a loss. Visualizing Data can save a lot of time in this situation!

5. Data Visualization Tells a Data Story

Data visualization is also a medium to tell a data story to the viewers. The visualization can be used to present the data facts in an easy-to-understand form while telling a story and leading the viewers to an inevitable conclusion. This data story, like any other type of story, should have a good beginning, a basic plot, and an ending that it is leading towards. For example, if a data analyst has to craft a data visualization for company executives detailing the profits of various products, then the data story can start with the profits and losses of multiple products and move on to recommendations on how to tackle the losses.

To find out more points please refer to this article: Why is Data Visualization so Important?

Now, that we have understood the basics of Data Visualization, along with its importance, now will be discussing the Advantages, Disadvantages and Data Science Pipeline (along with the diagram) which will help you to understand how data is compiled through various checkpoints.

What is Data Science?

Data Science is a field that focuses on extracting insights using different techniques for Statistics, Mathematics, Computer Science, etc. The objective of data science is to provide valuable insight to understand patterns and take efficient decisions. 

You can also refer to this to learn more about Data Science: What is Data Science?

Data Science Pipeline

In simple words, a pipeline in data science is “a set of actions which changes the raw (and confusing) data from various sources (surveys, feedback, list of purchases, votes, etc.), to an understandable format so that we can store it and use it for analysis.”

The Pipeline in Data Science also represents numerous stages that go through will processing data, they include the following list:

• Fetching/Obtaining the Data
• Identifying Errors
• Identifying Missing Values
• Identifying Corrupt Records
• Replace or fill in missing values/errors
• Exploratory Data Analysis or EDA
• Modeling the Data
• Interpreting the Data

The picture below also explains how raw data undergoes different stages within a pipeline:

Let’s now discuss some of the Advantages & Disadvantages of Data Visualization.

Advantages of Data Visualization

1. Better Agreement: In business, for numerous periods, it happens that we need to look at the exhibitions of two components or two situations. A conventional methodology is to experience the massive information of both circumstances and afterward examine it. This will take a great deal of time.  

2. A Superior Method: It can tackle the difficulty of placing the information of both perspectives into the pictorial structure. This will unquestionably give a superior comprehension of the circumstances. For instance, Google patterns assist us with understanding information identified with top ventures or inquiries in pictorial or graphical structures.  

3. Simple Sharing of Data: With the representation of the information, organizations present another arrangement of correspondence. Rather than sharing cumbersome information, sharing the visual data will draw in and pass on across the data which is more absorbable.  

4. Deals Investigation: With the assistance of information representation, a salesman can, without much of a stretch, comprehend the business chart of items. With information perception instruments like warmth maps, he will have the option to comprehend the causes that are pushing the business numbers up just as the reasons that are debasing the business numbers. Information representation helps in understanding the patterns and different variables like sorts of clients keen on purchasing, rehash clients, the impact of topography, and so forth.  

5. Discovering Relations Between Occasions: A business is influenced by a lot of elements. Finding a relationship between these elements or occasions encourages chiefs to comprehend the issues identified with their business.  For instance, the online business market is anything but another thing today. Each time during certain happy seasons, like Christmas or Thanksgiving, the diagrams of online organizations go up. Along these lines, state if an online organization is doing a normal $1 million business in a specific quarter and the business ascends straightaway, at that point they can rapidly discover the occasions compared to it.  

6. Investigating Openings and Patterns: With the huge loads of information present, business chiefs can discover the profundity of information regarding the patterns and openings around them. Utilizing information representation, the specialists can discover examples of the conduct of their clients, subsequently preparing them to investigate patterns and open doors for business.

Disadvantages of Data Visualization 

• Can be time-consuming: Creating visualizations can be a time-consuming process, especially when dealing with large and complex datasets. This can slow down the machine learning workflow and reduce productivity.
• Can be misleading: While data visualization can help identify patterns and relationships in data, it can also be misleading if not done correctly. Visualizations can create the impression of patterns or trends that may not exist, leading to incorrect conclusions and poor decision-making.
• Can be difficult to interpret: Some types of visualizations, such as those that involve 3D or interactive elements, can be difficult to interpret and understand. This can lead to confusion and misinterpretation of the data.
• May not be suitable for all types of data: Certain types of data, such as text or audio data, may not lend themselves well to visualization. In these cases, alternative methods of analysis may be more appropriate.
• May not be accessible to all users: Some users may have visual impairments or other disabilities that make it difficult or impossible for them to interpret visualizations. In these cases, alternative methods of presenting data may be necessary to ensure accessibility.

Data Visualization Tools

The following are the 10 best Data Visualization Tools

• Zoho Analytics
• IBM Cognos Analytics
• Microsoft Power BI
• SAP Analytics Cloud

To  find out more about these tools please refer to this article: Best Data Visualization Tools

Top Data Visualization Libraries Available in Python, R, and Javascript

The following are the top Data Visualization Libraries 

To find out more about these libraries please refer to this article: Top Libraries for Data Visualization

Frequently Asked Questions on Data Visualization

What are the key benefits of data visualization.

 Consider these five advantages of data visualization: Visualized Data Is Processed Faster. … Data Visualization Dashboards Support Visual Learners. … Data visualization tools show insights that may be missed in traditional reports. Data visualization gives actionable items. Data visualization increases productivity and sales.

Why is Data Visualization?

Data visualization  helps to tell stories by curating data into a form easier to understand, highlighting the trends and outliers . A good visualization tells a story, removing the noise from data and highlighting useful information.

What are the different types of Data Visualization?

Common categorization of Data Visualisation is given below – Charts Tables Graphs Maps Infographics Dashboards

What are Data Visualization tools?

There are lots of tools to visualize your data. Most of them are javascript libraries because the power of modern web browsers allows you to create magnificent visuals with some imagination. 

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Why data visualization is important.

June 10, 2020

Written by: Mathieu Stark

What is Data Visualization?

With so much information being collected through data analysis in the business world today, we must have a way to paint a picture of that data so we can interpret it. Data visualization gives us a clear idea of what the information means by giving it visual context through maps or graphs. This makes the data more natural for the human mind to comprehend and therefore makes it easier to identify trends, patterns, and outliers within large data sets. 

Why is Data Visualization Important?

No matter what business or career you’ve chosen, data visualization can help by delivering data in the most efficient way possible. As one of the essential steps in the business intelligence process, data visualization takes the raw data, models it, and delivers the data so that conclusions can be reached. In advanced analytics, data scientists are creating machine learning algorithms to better compile essential data into visualizations that are easier to understand and interpret.

Specifically, data visualization uses visual data to communicate information in a manner that is universal, fast, and effective. This practice can help companies identify which areas need to be improved, which factors affect customer satisfaction and dissatisfaction, and what to do with specific products (where should they go and who should they be sold to). Visualized data gives stakeholders, business owners, and decision-makers a better prediction of sales volumes and future growth. 

What Are The Benefits of Data Visualization?

Data visualization positively affects an organization’s decision-making process with interactive visual representations of data. Businesses can now recognize patterns more quickly because they can interpret data in graphical or pictorial forms. Here are some more specific ways that data visualization can benefit an organization: 

• Correlations in Relationships:  Without data visualization, it is challenging to identify the correlations between the relationship of independent variables. By making sense of those independent variables, we can make better business decisions. 
• Trends Over Time:  While this seems like an obvious use of data visualization, it is also one of the most valuable applications. It’s impossible to make predictions without having the necessary information from the past and present. Trends over time tell us where we were and where we can potentially go.  
• Frequency:  Closely related to trends over time is frequency. By examining the rate, or how often, customers purchase and when they buy gives us a better feel for how potential new customers might act and react to different marketing and customer acquisition strategies. 
• Examining the Market:  Data visualization takes the information from different markets to give you insights into which audiences to focus your attention on and which ones to stay away from. We get a clearer picture of the opportunities within those markets by displaying this data on various charts and graphs.
• Risk and Reward:  Looking at value and risk metrics requires expertise because, without data visualization, we must interpret complicated spreadsheets and numbers. Once information is visualized, we can then pinpoint areas that may or may not require action.
• Reacting to the Market:  The ability to obtain information quickly and easily with data displayed clearly on a functional dashboard allows businesses to act and respond to findings swiftly and helps to avoid making mistakes.

Which Data Visualization Techniques are Used?

There are many different methods of putting together information in a way that the data can be visualized. Depending on the data being modeled, and what its intended purpose is, a variety of different graphs and tables may be utilized to create an easy to interpret dashboard. Some visualizations are manually created, while others are automated. Either way, there are many types to meet your visualization needs. 

• Infographics:  Unlike a single data visualization, infographics take an extensive collection of information and gives you a comprehensive visual representation. An infographic is excellent for exploring complex and highly-subjective topics.
• Heatmap Visualization:  This method uses a graph with numerical data points highlighted in light or warm colors to indicate whether the data is a high-value or a low-value point. Psychologically, this data visualization method helps the viewer to identify the information because studies have shown that humans interpret colors much better than numbers and letters.
• Fever Charts:  A fever chart shows changing data over a period of time. As a marketing tool, we could take the performance from the previous year and compare that to the prior year to get an accurate projection of next year. This can help decision-makers easily interpret wide and varying data sources.
• Area Chart (or Graph):  Area charts are excellent for visualizing the data’s time-series relationship. Whether you’re looking at the earnings for individual departments on a month to month basis or the popularity of a product since the 1980s, area charts can visualize this relationship.
• Histogram:  Rather than looking at the trends over time, histograms are measuring frequencies instead. These graphs show the distribution of numerical data using an automated data visualization formula to display a range of values that can be easily interpreted. 

Who Uses Data Visualization?

Data visualization is used across all industries to increase sales with existing customers and target new markets and demographics for potential customers. The World Advertising and Research Center (WARC) predicts that in 2020 half of the world’s advertising dollars will be spent online, which means companies everywhere have discovered the importance of web data. As a crucial step in data analytics, data visualization gives companies critical insights into untapped information and messages that would otherwise be lost. The days of scouring through thousands of rows of spreadsheets are over, as now we have a visual summary of data to identify trends and patterns.

We need data visualization because the human brain is not well equipped to devour so much raw, unorganized information and turn it into something usable and understandable. We need graphs and charts to communicate data findings so that we can identify patterns and trends to gain insight and make better decisions faster. 

At analytiks, we understand the importance of data visualization and what it means to our clients. We provide them with user-friendly and beautiful visualization features and tools to depict their data in a clear and meaningful way. We’re here to ensure our clients have everything they need to make quick and informed decisions based on sound data that is easy to interpret. Contact our friendly team of professionals at analytiks today to hear how we can better your business.

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Exploring The Benefits Of Geospatial Data Visualization Techniques

by Sam McKay, CFA | Data Analytics

Data visualization has come a long way from simple bar charts and line graphs. As the volume and complexity of data continue to grow, advanced data visualization techniques have become essential for making sense of it all.

In this article, we’ll explore two advanced data visualization techniques — geospatial data visualization and multi-dimensional data visualization.

By combining these techniques with more traditional data visualization methods, we can create highly informative and visually compelling data visualizations that provide deep insights into complex data sets.

Geospatial data visualization involves mapping data points onto a geographical map. This can help us understand the spatial relationships within the data.

Multi-dimensional data visualization involves representing data points in a space with more than two or three dimensions. This allows us to see patterns that would be hidden in simpler visualizations.

Advanced data visualization techniques offer a powerful way to communicate data’s complexities and insights. As data professionals, we must continue to expand our data visualization toolkit to unlock new insights and share them with our audience.

Let’s dive in!

Table of Contents

Understanding Data Visualization

Data visualization is the presentation of data in a graphical format. It helps people understand the significance of data by summarizing and presenting huge amounts of information in a simple, visual way.

Data visualization can help you find insights that you might otherwise miss or that are too complex to explain with text or tables.

For example, you can use data visualization to:

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

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.

By providing a visual representation of what might seem like endless streams of data, data visualization is an invaluable tool to help data scientists make better business decisions, provide a more in-depth analysis, and share their work with others more effectively.

What is Advanced Data Visualization?

Advanced data visualization is an emerging field of data visualization that focuses on visualizing complex, multi-dimensional data.

It goes beyond traditional data visualization techniques to help people understand the significance of data more effectively.

This approach uses sophisticated software and algorithms to interpret and display large volumes of data in an understandable format.

With the rise of big data and the increasing importance of data-driven decision-making, advanced data visualization is becoming an essential tool for businesses and organizations to turn their data into actionable insights.

This approach offers several advantages over traditional data visualization methods, including the ability to:

• Identify trends and patterns: Advanced data visualization can help you find hidden patterns and trends in your data, which can be useful for making predictions and understanding the root causes of certain events.
• Improve data understanding: By presenting data in a visual format, you can make complex data sets easier to understand, allowing you to communicate your findings more effectively to a wider audience.
• Make data-driven decisions: Advanced data visualization allows you to make better decisions by providing a clear picture of what the data is telling you. This can help you identify opportunities, avoid potential risks, and optimize your business processes.

Some common tools for advanced data visualization include Tableau, Power BI, and QlikView. These tools offer a wide range of features for creating interactive, multi-dimensional visualizations that can help you gain valuable insights from your data.

Exploring Geospatial Data Visualization

Geospatial data visualization is a type of data visualization that involves presenting information about geographic features and phenomena in a visual way.

It is an important field because it allows people to understand the spatial relationships between different pieces of information and make better decisions.

This type of visualization can include maps, graphs, and charts that show data about locations, distances, areas, and movements.

In recent years, geospatial data visualization has become even more powerful due to advances in technology and the availability of large amounts of data.

There are several important applications of geospatial data visualization, including:

• Urban planning : Geospatial visualization can help urban planners understand how cities are growing and changing, allowing them to make informed decisions about zoning, transportation, and infrastructure.
• Disaster response : During natural disasters, geospatial visualization can help emergency responders quickly understand the scope of the disaster and allocate resources more effectively.
• Healthcare : Geospatial visualization is used to track the spread of diseases, identify areas with high rates of certain health conditions, and plan for the location of healthcare facilities.
• Environmental management : Geospatial visualization can be used to monitor changes in the environment, such as deforestation, land use, and climate change.
• Retail and marketing : Businesses use geospatial visualization to understand where their customers are located, how they move through stores, and how they interact with products.

To create effective geospatial visualizations, it is essential to have a good understanding of geographic information systems (GIS), data science, and graphic design.

The process typically involves acquiring geospatial data, processing it, and then using specialized software to create visualizations that convey important information in a clear and understandable way.

Geospatial data visualization is a rapidly evolving field that has the potential to revolutionize the way we understand and interact with the world around us.

Multi-dimensional Data Visualization

Multidimensional data visualization is the process of displaying information in a way that allows you to see more than two dimensions at once.

This technique is essential for understanding complex data sets, as it can reveal patterns and relationships that would be difficult or impossible to see using traditional two-dimensional visualizations.

There are several common techniques for visualizing multi-dimensional data, including:

• Parallel coordinates : This technique involves drawing lines between points on multiple axes, allowing you to see how data points relate to each other across multiple dimensions.
• Scatterplot matrices : A scatterplot matrix is a grid of small scatterplots, each of which shows the relationship between two variables. This allows you to see how variables relate to each other and identify patterns in the data.
• Star plots : Star plots use a series of lines radiating from a central point to represent different variables. By looking at how these lines intersect, you can see how variables relate to each other across multiple dimensions.
• Hypercube visualization : Hypercube visualization represents data in four or more dimensions by projecting it into three dimensions. This allows you to see how data points relate to each other in a higher-dimensional space.
• Chernoff faces : Chernoff faces represent data using human faces, with different facial features representing different variables. This can be a fun and intuitive way to visualize multidimensional data.

It’s important to note that the most effective technique for visualizing multi-dimensional data depends on the specific data set and the questions you want to answer.

Experimenting with different visualization techniques can help you find the best way to represent your data.

With the rise of big data and the increasing complexity of the problems we face, multi-dimensional data visualization is becoming an essential tool for making sense of the world around us.

By learning how to effectively visualize data in multiple dimensions, you can unlock valuable insights and make better decisions in a wide range of fields.

5 Examples of Advanced Data Visualization

Advanced data visualization allows data scientists and analysts to unlock the potential of data and turn it into actionable insights. By using cutting-edge tools and techniques, they can create visual representations of complex data sets that are both informative and visually compelling.

In this section, we’ll look at five examples of advanced data visualization to see how it’s being used in the real world to drive innovation and make better decisions.

1. Data Visualization in Healthcare

In the healthcare industry, data visualization plays a crucial role in understanding patient data, improving patient outcomes, and making data-driven decisions.

For example, doctors can use visualizations to track a patient’s health over time, identify trends, and adjust treatments accordingly.

Healthcare organizations can also use data visualization to identify areas for improvement in their processes, such as reducing patient wait times or improving the efficiency of their supply chain.

2. Data Visualization in Finance

In the finance industry, data visualization is used to analyze stock market trends, monitor the performance of investment portfolios, and detect potential fraud.

By using advanced data visualization techniques, financial analysts can quickly identify patterns and anomalies in large datasets, allowing them to make more informed decisions and respond to market changes more effectively.

3. Data Visualization in Marketing

Data visualization is also widely used in marketing to analyze customer behavior, track the success of marketing campaigns, and identify new opportunities.

For example, marketers can use visualizations to understand how different customer segments interact with their website, which can help them optimize their website design and improve the customer experience.

4. Data Visualization in Government

In the government sector, data visualization is used to make data more accessible and understandable to the public.

Government agencies often publish interactive dashboards that allow citizens to explore data on topics such as crime rates, education, and public health.

This transparency helps to build trust and allows citizens to make more informed decisions.

5. Data Visualization in Sports

In the sports industry, data visualization is used to analyze player performance, identify game trends, and provide insights to coaches and teams.

For example, in soccer, data scientists can use visualizations to track player movements, passing patterns, and shooting accuracy.

This information can help teams optimize their strategies and make better decisions on the field.

The Future of Advanced Data Visualization

The future of advanced data visualization is bright. With the rapid growth of big data, AI, and machine learning, the need for powerful data visualization tools has never been greater.

As technology continues to advance, we can expect to see more sophisticated data visualization techniques that can handle even larger and more complex data sets.

The development of tools that can create highly interactive, customizable visualizations will allow users to explore their data in new ways and gain deeper insights.

In addition, as data visualization becomes more accessible, we can expect to see a growing number of industries adopting it to make better decisions and solve complex problems.

From healthcare to finance to government, data visualization will continue to play a crucial role in helping organizations turn their data into valuable insights.

So, what does the future hold for data visualization? It is difficult to predict, but one thing is for certain: data visualization is here to stay, and it will only continue to grow in importance and sophistication.

As data professionals, it is essential that we stay up-to-date with the latest trends and technologies in data visualization to ensure that we can effectively analyze and communicate data in this rapidly evolving data-driven world.

Final Thoughts

In conclusion, the world of data visualization is evolving rapidly, and the techniques we’ve explored here are just the tip of the iceberg. As we move forward, it’s clear that advanced data visualization is a game-changer.

By mastering these advanced techniques, data professionals can unlock the full potential of their data and gain valuable insights that can drive innovation and growth. With the right tools and a willingness to learn, the possibilities are endless.

Data visualization has the power to transform how we understand and interact with the world around us, and it’s an essential skill for anyone working with data. So, let’s continue to explore, experiment, and push the boundaries of what’s possible with data visualization. For more Data Viz tips check out Data Mentor Agents . Describe what you’re working on and get detailed learning docs on virtually any data topic!

Frequently Asked Questions

What are the key elements of advanced data visualization?

The key elements of advanced data visualization include interactivity, real-time data updating, and the use of modern data visualization tools like Tableau and Power BI. These elements help to enhance the effectiveness and usefulness of data visualizations.

How does advanced data visualization differ from basic visualization?

Advanced data visualization goes beyond basic visualization techniques by using more complex tools and methods to create visual representations of data. This allows for a deeper understanding of data and the identification of more intricate patterns and insights.

What are the common tools and software used in advanced data visualization?

Some common tools and software used in advanced data visualization include Tableau, Power BI, QlikView, R, and Python. These tools offer a wide range of features and functionalities to create interactive and sophisticated data visualizations.

What is the importance of advanced data visualization in decision-making?

Advanced data visualization plays a crucial role in decision-making by providing a clear and concise representation of data. This allows decision-makers to quickly identify trends, patterns, and insights, leading to more informed and data-driven decisions.

How can you learn advanced data visualization techniques?

To learn advanced data visualization techniques, you can take courses and attend workshops that focus on the topic. Additionally, there are numerous online resources, tutorials, and forums where you can find information and guidance on advanced data visualization.

What are some examples of advanced data visualization projects?

Examples of advanced data visualization projects include interactive dashboards that provide real-time data updates, 3D visualizations of complex data sets, and data visualizations that incorporate machine learning algorithms to identify patterns and anomalies in data.

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