visual representation of worksheet data

Guide To What Is The Pictorial Representation Of Worksheet Data

Introduction.

When dealing with a large amount of data, it can be overwhelming to make sense of it all. This is where the pictorial representation of worksheet data comes into play. It is a visual way of representing data using graphs, charts, and other visual elements. This method helps in presenting complex data in a simplified and easy-to-understand format, making it easier for anyone to analyze and interpret the information.

Using pictorial representation in data analysis is crucial as it allows for quick and easy identification of patterns, trends, and outliers at a glance. This visual aid helps in making informed decisions and presenting findings effectively to stakeholders.

Key Takeaways

• Pictorial representation of worksheet data uses graphs, charts, and visual elements to simplify complex data and make it easier to analyze.
• Using pictorial representation allows for quick identification of patterns, trends, and outliers, aiding in making informed decisions.
• When choosing the right pictorial representation, consider the type of data, the message you want to convey, and the audience's familiarity with different types of graphs.
• Best practices for creating pictorial representation include using appropriate scaling, labeling axes, using consistent colors and symbols, and avoiding data distortion.
• Benefits of using pictorial representation include making data easier to understand, highlighting trends and patterns, simplifying complex information, and engaging the audience.

Types of Pictorial Representation

When it comes to representing worksheet data in a visual format, there are several types of pictorial representations to choose from. Each type has its own unique characteristics and is best suited for displaying specific types of data. Let's take a closer look at some of the most commonly used types of pictorial representation:

Bar graphs are one of the most popular and widely used forms of pictorial representation. They are ideal for comparing the values of different categories or groups. The length of the bars represents the value of each category, making it easy to visualize and compare the data.

Pie charts are circular graphs that are divided into slices to represent the proportion of each category within the data set. They are useful for illustrating the breakdown of a whole into its constituent parts and are particularly effective for showing percentages.

• Line graphs

Line graphs are used to display data points connected by straight lines, making it easy to see trends and patterns over time. They are especially useful for showing changes and fluctuations in data over a continuous period.

• Scatter plots

Scatter plots are used to display the relationship between two variables. Each data point is represented by a dot on the graph, and the pattern of the dots can reveal the nature of the relationship between the variables, such as correlation or clustering.

How to Choose the Right Pictorial Representation

When it comes to representing worksheet data visually, selecting the right pictorial representation is essential for effectively communicating information. Here are some key factors to consider when making this decision:

A. Consider the type of data being represented

• 1. Numerical data: If your data consists of numerical values, bar graphs, line graphs, and pie charts are commonly used to depict this type of information. Bar graphs are ideal for comparing values across different categories, while line graphs show trends over time. Pie charts can be used to illustrate the proportion of each category within a whole.
• 2. Categorical data: When dealing with categorical data, bar graphs, pie charts, and stacked bar graphs can effectively showcase this type of information. Bar graphs and stacked bar graphs can compare values within categories, while pie charts display the distribution of each category.
• 3. Relationships between variables: For showing relationships between variables, scatter plots and line graphs are often utilized. These types of graphs can reveal patterns and correlations within the data.

B. Determine the message you want to convey

• 1. Trends: If the goal is to highlight trends or patterns within the data, a line graph or scatter plot may be the most suitable choice. These representations can effectively showcase changes over time or relationships between variables.
• 2. Comparisons: When the objective is to compare different categories or values, bar graphs and pie charts can clearly illustrate these comparisons. Stacked bar graphs are particularly useful for simultaneously comparing subcategories within larger categories.
• 3. Distribution: If you need to display the distribution of categorical data, a pie chart or stacked bar graph can visually represent the proportions of each category within the dataset.

C. Assess the audience's familiarity with different types of graphs

• 1. General audience: If the intended audience is not familiar with complex data representations, it may be best to opt for simpler graphs such as bar graphs and pie charts. These are commonly understood and can effectively convey the information to a broad audience.
• 2. Data-savvy audience: For an audience well-versed in data analysis, more advanced representations like scatter plots and stacked bar graphs may be appropriate. These individuals can interpret and derive insights from more intricate visualizations.

Best Practices for Creating Pictorial Representation

When creating pictorial representations of worksheet data, it's important to follow best practices to ensure that the visualizations accurately and effectively communicate the information. The following are some key best practices to keep in mind:

• Use appropriate scaling

It's important to choose the right scale for your pictorial representation to ensure that the data is accurately represented. Be mindful of the range of the data and choose a scale that effectively captures the variations without distorting the overall picture.

• Label axes and provide a clear title

Labeling the axes and providing a clear title for your pictorial representation is essential for helping the audience understand the context and meaning of the data. Make sure the labels and title are clear, concise, and informative.

• Use consistent colors and symbols

Consistency in the use of colors and symbols in your pictorial representation is crucial for clarity and coherence. Choose a color palette and set of symbols that are easily distinguishable and use them consistently throughout the visualization.

• Avoid distorting the data

It's important to avoid distorting the data in your pictorial representation. Be cautious of various visual techniques such as skewed scales, exaggerated proportions, and manipulated axes that can misrepresent the data.

Tools for Creating Pictorial Representation

When it comes to visualizing data from a worksheet, there are several tools available that can help you create pictorial representations. Here are some of the most popular ones:

Excel is a widely used spreadsheet software that offers various chart and graph options to visually represent data. You can easily create bar graphs, pie charts, line graphs, and more to effectively showcase your worksheet data.

Google Sheets also provides a range of chart types to visually depict your data. With its intuitive interface and built-in chart tools, you can quickly create appealing visual representations of your worksheet data.

Tableau is a powerful data visualization tool that allows you to create interactive and dynamic visualizations. It offers advanced features for creating dashboards, maps, and other complex visual representations of worksheet data.

Infogram is a user-friendly tool for creating infographics, charts, and maps. It provides a range of templates and customization options to easily transform your worksheet data into engaging visual content.

Benefits of Using Pictorial Representation

When it comes to presenting worksheet data, using pictorial representation can offer several benefits that can enhance understanding, highlight key trends, simplify complex information, and engage the audience.

Pictorial representation such as graphs, charts, and diagrams can make it easier for individuals to comprehend the data presented. Visualizing the data can help in clarifying the information and making it more accessible.

By using visual representations, it becomes easier to identify trends and patterns within the data. Whether it's a fluctuation in sales over time or a correlation between different variables, pictorial representation can make these insights more apparent.

For datasets that contain complex information, using visuals can simplify the presentation of the data. Instead of overwhelming the audience with raw numbers and figures, creating a visual representation can simplify the message and make it more digestible.

Visuals are inherently more engaging than plain text or tables of data. Pictorial representation can capture the audience's attention and make the information more memorable. This can be particularly effective when presenting to a diverse audience with varying levels of data literacy.

As we wrap up this discussion on the pictorial representation of worksheet data, it's important to emphasize the significance of using graphs and charts to visually represent data. Not only do they make complex data more understandable, but they also facilitate quick and easy analysis. I encourage all readers to incorporate various types of graphs, such as bar graphs, pie charts, and line graphs, in their data analysis to gain deeper insights into their data.

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Data Visualization in Excel

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• Advance Excel

Data Visualization is the representation of data in a graphical format. It makes the data easier to understand. Data Visualization can be done using tools like Tableau, Google charts, DataWrapper, and many more. Excel is a spreadsheet that is used for data organization and data visualization as well. In this article, let’s understand Data Visualization in Excel.

Excel provides various types of charts like Column charts, Bar charts, Pie charts, Linecharts, Area charts, Scatter charts, Surface charts, and much more.

Steps for visualizing data in Excel:

• Open the Excel Spreadsheet and enter the data or select the data you want to visualize.
• Click on the Insert tab and select the chart from the list of charts available or the shortcut key for creating chart is by simply selecting a cell in the Excel data and press the F11 function key .

• A chart with the data entered in the excel sheet is obtained.
• You can design and style your chart with different types of styles and colors by selecting the design tab.
• In Excel 2010, the design tab option is visible by clicking on the chart.

The Excel data is as follows:

The column chart obtained for the data by following the above steps:

When excel data contains multiple columns and if you want to make a chart for only a few columns, then select the columns required for making the chart and press the ‘F11’ function key or click on the Insert tab and select the chart from the list of charts available.

We can also select the required data columns by doing right-click on the chart and click on the ‘ select data ‘ option. Now, data can be added or removed for making the chart.

For swapping rows and columns in the chart, use the ‘S witch Row/Column ‘ option available in the design tab.

We can also make different types of charts for the same spreadsheet data by clicking on the ‘ Change Chart Type’ option in the Design tab .

To make your chart more clear, use the ‘Layout’ tab. In this tab, you can more changes to your chart like editing the chart title, adding labels to your chart, adding a legend, and adding horizontal or vertical grid lines.

Example 3: Formatting Chart Area

To format the chart area, right-click on the chart and select the option ‘ Format chart Area ‘.

The format chart area provides various options for formatting the chart like Filling the chart with patterns and solid colors, Border colors, Styles for borders, the shadow effect for your chart, and many more. Formatting makes the chart look more attractive and colorful.

Example 4: Creating Sparklines

Sparklines in Excel are small charts that fit in the data cells of the excel sheets.

Steps for Creating Spark Lines:

• Select the Excel data range for sparklines as shown in the below figure.
• Click on Sparklines in the Insert tab and select any one of the three sparklines.

• Add the Location Range and Data Range for the creation of sparklines and click ok.

Finally, the F, G, H columns are displayed with the line, column, and Win/loss sparklines.

We can also color these sparklines by the Design tab as shown below:

 We can Mark data points and also change Sparkline Color.

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How To Create Data Visualizations in Excel? A Complete Guide

T ransform your Excel sheets from simple spreadsheets to storytelling tools with beautiful and intuitive “data visualizations in Excel”. This powerful feature turns complex data into clear, engaging visuals, making it easier to spot trends, patterns, and insights.

Data visualization in Excel represents numerical value in a visual format. It is the way to organize data in a spreadsheet in a more accessible and organized format. Confidently navigate through the myriad of chart types and customization options available in Excel to effectively communicate your data’s story.

Excel templates can be used for data visualization. Excel as a data visualization tool has several charts, graphs, and maps you can use to visualize your data, like bar graphs, line charts, pivot tables, etc.

This guide will take you step-by-step through creating impactful data visualizations, ensuring your presentations and reports stand out with professional polish and clarity. Let’s dive in and bring your data to life!

What is Data Visualization in Excel?

Definition : Data visualization is the graphic representation of data that makes it easier to interpret. We can create Data visualizations using tools like Data Wrapper, Google Charts, and others. Additionally, data is organized and visualized using an Excel spreadsheet.

Let’s explore Excel’s data visualization features in many different ways. We can use different Excel charts and graphs to visualize data. Additionally, data visualization using Excel templates is possible.

Column charts, bar charts, pie charts, progress bars , line charts, area charts, scatter charts, surface charts, Sankey diagrams , and many others are available in Excel.

Different Types of Data Visualizations in Excel

Excel can be used for several data visualization techniques, including:

Column Chart

It is a simple type of graph where data is shown as vertical bars. Select the data and the required option from the Column chart menu to build a column chart. As we can see, several options exist for the Column chart; the best option must be picked.

The chart can be formatted as required.

Pie charts or diagrams display the percentage share each data type makes up. The pie chart helps us rapidly understand the proportion contribution. To create a pie chart, pick the required columns, then pick the relevant pie chart option from the Pie menu.

Horizontal bars are the only difference between this chart type and a column chart. Select the suitable bar chart from the Bar option to make a horizontal bar.

A line graph is commonly drawn to show data that changes over time. It consists of two axes: the x-axis and the y-axis. Each axis represents a different dataset. It is formed by connecting a series of points using a straight line.

It can be used to check whether the values are increasing or decreasing over time.

Pivot Table

A pivot table is a tabular representation of data used in data visualization that is used to group, sort, and summarize huge volumes of data.

Advanced Excel Charting Techniques

There are the following advanced Excel charting Techniques used for data visualization:

Combining multiple data series

When you have multiple data sets that you want to compare in a single chart, you can combine them into one chart using Excel’s capability to handle multiple data series. Here’s how to do it

• Ensure that your data is organized properly. You may have two columns or rows for categories (e.g., months and sales)
• Highlight the entire dataset, including the category labels and all the data series.
• Go to the “Insert” tab in Excel’s ribbon, and select the chart type that best suits your data. Common choices include clustered column/bar charts for comparing data in different categories or line charts for showing trends.
• Excel will create a basic chart with all the data series. You can further customize the appearance of each data series by changing colors, styles, and labels. It helps differentiate and highlight each series.

• If you have multiple data series, including a legend in your chart is crucial. The legend identifies which color or symbol corresponds to each data series.
• Enhance the visual appeal of your chart by adjusting fonts, colors, and chart elements as needed.

Creating pivot charts

Pivot charts are powerful tools for summarizing and visualizing large datasets quickly. They work with pivot tables to provide a dynamic way to analyze data. Here’s how to create a pivot chart:

• Ensure your data is organized in a tabular format, with columns for different attributes such as dates, categories, and values.
• Pick your data, go to the “Insert” tab, and click “PivotTable.” Configure the pivot table to summarize your data as desired, choosing rows, columns, and values.

• With your pivot table created, select any cell within it. Then, go to the “Insert” tab and click “PivotChart.” Choose the chart type you want to use (e.g., bar chart, line chart).

• Excel will generate a pivot chart linked to your pivot table. You can customize the chart by add the fields from the pivot table to the chart’s elements to create meaningful visualizations.

How to Make a Heat Map Visualization in Excel?

Create a geographic heat map in Excel quickly by using conditional formatting. Colors on a heat map represent the individual values.

To make a heat map, follow the procedures below:

• Choose the range
• On the Home tab, select Conditional Formatting from the Styles category.

• Under Color Scales, select a subtype.
• Format Right-clicking or hitting CTRL + 1 will choose a cell.
• Select Custom from the menu.

Excel offers data visualization tools , from basic charts to advanced mapping features. You can adapt Excel’s visualization capabilities to their specific needs, whether you are a beginner or an expert.

This article introduced the Excel data visualization concept and outlined various visualization techniques. Excel empowers users to communicate data insights, make informed decisions, and create compelling visual narratives , enhancing data analysis processes.

With over two decades of experience in writing about Microsoft Excel, Google Sheets, and various other spreadsheet tools, Muhammad Nadeem Salam is your go-to expert for all things data. Since 2004, he has been passionately sharing his knowledge and insights through engaging and informative blog posts, helping countless readers unlock the full potential of their spreadsheet tools.

Nadeem Salam

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Creating Data Visualizations in Excel: What to Keep in Mind

• 14 Oct 2021

One of the most valuable and abundant resources businesses have at their disposal is data. With vast amounts of data being generated every minute of every day, the insights gleaned can inform virtually every business decision —often resulting in favorable outcomes.

There are many data visualization tools on the market designed for creating illustrations for business purposes. Fortunately, one of the most popular and easy-to-use options is likely already installed on your computer: Microsoft Excel.

If you don’t have access to Microsoft Excel, consider using free options like Google Sheets for a similar, albeit more limited, experience.

While Excel isn’t visualization software, it’s a versatile, powerful tool for professionals of all levels who want to analyze and illustrate datasets. Here are the types of data visualizations you can create in Excel and the steps involved in doing so, along with some tips to help you along the way.

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Types of Data Visualizations in Excel

There are different data visualization techniques you can employ in Excel, depending on the data available to you and the goal you’re trying to achieve, including:

• Area charts
• Scatter plots

Other visualization techniques can be used to illustrate large or complex data sets. These include:

• Gantt charts
• Highlight tables
• Bullet graphs

More advanced visualizations, such as those that include graphic elements like geographical heat maps, may not be possible to create in Excel or require additional tools.

Related: 6 Data Visualization Examples to Inspire Your Own

How to Create Data Visualizations in Excel

The steps involved in creating data visualizations in Excel depend on the type of graph or chart you choose. For basic visualizations, the process is largely the same. More complex datasets and illustrations may require additional steps.

To craft a data visualization in Excel, start by creating an organized spreadsheet. This should include labels and your final dataset.

Then, highlight the data you wish to include in your visual, including the labels. Select “insert” from the main menu and choose the type of chart or graph you’d like to create. Once you’ve made your selection, the visualization will automatically appear in your spreadsheet.

Right-click on the chart or graph to edit details, such as the title, axes labels, and colors. Doing so will open a pop-up or side panel that includes options to add a legend, adjust the scale, and change font styles and sizes.

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Tips for Creating Visualizations in Excel

1. choose the right type of visualization.

To create an effective data visualization, it’s critical to choose the right type of chart or graph. Consider the type of data you’re using, the size of your dataset, and your intended audience.

A mismatch between the type of data being leveraged and the visual used to present it can be detrimental to viewers’ understanding of the information. Whether you’re working with qualitative or quantitative data, for example, impacts how you should display the information.

Your intended audience also influences how simplified or complex your illustration should be. For instance, when presenting to a large audience or high-level stakeholders, it’s helpful to distill your presentation to highlight key trends and insights rather than individual data points.

2. Remove Irrelevant or Inaccurate Data

Ultimately, your visualization’s quality is only as good as that of the data you use. For this reason, it’s important to clean data after it’s been collected to remove any irrelevant or inaccurate information. This process is often referred to as data wrangling or data cleaning.

Failure to thoroughly clean data prior to using it can be detrimental to its integrity and lead to inaccurate or misleading data visualizations .

Related: What Is Data Integrity & Why Does It Matter?

3. Provide Context For the Visualization

If necessary, include a key or legend and additional context to help viewers make sense of your illustration.

For example, consider a heatmap that shows the frequency of COVID-19 infections in a location over a specific period. To form a clear understanding of the information being presented, viewers need to know details such as the period being examined, the data source, and what each color means.

This context is important because it helps viewers interpret the information being displayed. Without a key clearly defining the coloring system, for instance, it would be virtually impossible to know what each color indicates, rendering the heatmap useless.

4. Tell A Story

Finally, the key to crafting a compelling visualization is to use data to tell a story. If the data illustrates a trend or supports a hypothesis, your visualization should make that clear. After all, the purpose of visualizations is to present findings in a way that’s easy for viewers to digest and understand.

Telling a story not only makes your visualization more interesting and engaging but also aids in data-driven decision-making . In addition, it helps stakeholders understand the essence of your findings and, in turn, informs their decision-making processes .

Making Data-Backed Business Decisions

Data visualization is a powerful tool when it comes to addressing business questions and making informed decisions. Learning how to create effective illustrations can empower you to share findings with key stakeholders and other audiences in a manner that’s engaging and easy to understand.

You don’t have to be in an analytics or data science role to take advantage of data visualizations. Professionals of all levels and backgrounds can develop data skills to more effectively communicate within their organizations and make informed decisions.

Are you interested in improving your analytical skills? Learn more about Business Analytics —one of the three courses that comprise CORe —which teaches you how to apply analytical techniques in Excel to solve real business problems.

About the Author

Excel Visualization: A Guide to Clear Data Presentation for Beginners

I once struggled with dull data tables.

Numbers clustered in rows and columns become a blur. But with Excel visualization , you can empower your audience to make informed decisions based on the data presented. Excel charts and graphs replace chaos, revealing patterns and trends.

Convey ideas efficiently with the right visual. It’s not just about creating a chart; it’s about making data understandable and engaging.

In this article, I’ll guide you step-by-step on transforming your Excel data into insightful visuals.

Let’s get started!

Table of Contents

Understanding the Basics of Excel Visualization

Excel provides various visualization options, whether 2D or 3D versions, standard, stacked, or 100% stacked options. It’s all about finding the right fit that best represents your data and message.

The Excel Charting Interface

Let’s start with creating a chart in Excel.

When you click on the Insert tab in Excel, you’ll see various chart types that you can use to visualize your data.

The Excel charting interface provides a wide range of options, from line and area charts to bar and column charts. When you click on a chart, the ‘ Chart Tools ’ contextual tab provides additional features for customizing your charts.

Types of Data for Visualization

Excel visualization data can be broadly categorized into numerical, categorical, and time-series data.

• Numerical data includes values that can be measured, such as sales figures or temperature readings.
• Categorical data includes information such as names, labels, or groups.
• Time-series data involves values measured over time, such as stock prices or website traffic.

Excel offers different chart types depending on your data type.

Selecting the Right Chart Type

Selecting the right chart type is half the battle for effective data visualization in Excel.

Pie charts are best for part-to-whole comparisons. Use line charts for time series or trends. Bar or column charts are the most suitable for categorical comparisons.

However, consider more advanced chart types for more complex data sets.

Scatter plots are excellent for correlation analysis , while histograms and box plots are ideal for distribution analysis of quantitative data.

It’s all about understanding your data and determining the best way to display it.

Steps for Visualizing Data in Excel – Creating Basic Charts

Creating basic charts in Excel is a fundamental skill for anyone looking to present data in a visual format.

Excel offers a variety of chart types, each with unique properties and use cases. The key to successful chart creation in Excel is understanding these different chart types and knowing how to present your data most effectively with them.

Organizing Your Data

Before you dive into creating Excel charts, it is crucial to organize your data correctly . 

Well-organized data will make the charting process easier and the resulting charts more meaningful. Ensure your data is clean, error-free, and arranged clearly and logically.

This will make it easier to select the data for your charts and create visuals that effectively communicate your data analysis results.

Pie and Donut Chart

Pie charts are popular for showing the proportion of different categories within a whole. While visually appealing, they are often misused and can lead to misleading interpretations.

Generally, they are most effective when comparing a few categories representing parts of a whole.

On the other hand, donut charts are a variation of pie charts with a hole in the middle (as the name implies!). Like pie charts, they can display multiple data series, but they should be used sparingly.

To create a pie chart in Excel:

• Select the data you want to visualize
• From the “ Insert ” tab, choose “ Pie ” from the chart options.
• You can customize your chart by changing the colors, adding labels, and adjusting other settings in the “ Format Chart Area ” pane.

Here’s a video guide on how to create a donut chart:

Line and Area Chart

Line and area charts are handy when dealing with time-series data . These charts plot data points on a graph and connect them with a line, allowing you to see trends over time. 

Check out this video for a step-by-step guide on how to create a line chart:

One of the business essentials when working with line and area charts is customizing the axis and gridlines. This can help make your chart more readable and meaningful .

The “ Format Axis ” pane allows you to customize the axis labels, adjust the scale, and add gridlines.

Column and Bar Graph

Bar and column charts are Excel’s most commonly used chart types. They are excellent for comparing different categories of data.

While bar charts and column charts are often used interchangeably, there is a difference: A bar chart presents data horizontally , while a column chart presents data vertically . This distinction can influence how easily your audience interprets the chart.

You can also choose between a stacked or clustered bar and column chart layout.

In a stacked chart , data series are stacked on each other, while in a clustered chart , they are placed side by side.

To create a bar or column chart:

• Select the data
• Then choose either “Bar” or “Column” from the chart options in the “ Insert ” tab
• Remember to format the chart and the axis labels to make the chart easier to understand

Advanced Charting Techniques

In this section, I’ll describe how to present complex data in a visually appealing and easily understandable format. Since each dataset is unique, treat these charts as ideas for meaningfully presenting your data.

Combination Charts

This type of chart combines the features of line and column charts, allowing you to present mixed data more comprehensively.

For example, when you have a target and actual data for comparison , a combination chart can be the perfect tool for visualization.

Clicking the Chart Design tab on the ribbon allows you to change the chart type and create a customized combination chart.

This allows you to have your target values in columns and the actual values marked along the line, which provides a clearer visualization of your data.

Trendlines and Data Analysis

Another essential feature of Excel charts is the ability to add trendlines. These can be linear, polynomial, or moving average trendlines.

A trendline graphically displays trends in your data , and you can extend it beyond the actual data to predict future values.

Along with trendlines, interpreting R-squared values is also crucial in data analysis. This will help you understand the relationship between your dependent and independent variables, thus enhancing your analysis results.

Check out our detailed how-to post on adding trendlines to Excel charts .

Conditional Formatting in Charts

Conditional formatting is another advanced charting technique in Excel that can enhance your data visualization. You can also add data bars, color scales, and icon sets.

These features allow you to customize your charts based on certain conditions, making it easier for your audience to understand your data. Applying these formatting options enables you to create more engaging and visually appealing charts for your data presentation.

Creating a Tornado Chart in Excel

Tornado charts are particularly effective when comparing and contrasting different variables . A well-crafted tornado chart can help you visualize how changes in several factors can impact a specific outcome – for example, the impact of inflation on NPV and IRR results.

Here’s a video showing you how to create a tornado chart:

Designing a Funnel Chart in Excel

Funnel Charts in Excel are highly effective tools for monitoring sales processes or any other process that narrows down over time.

Here are two quick methods for designing funnel charts in Excel:

Building a Waffle Chart in Excel

Waffle charts, also known as square pie or waffle bar charts, are a great way to visualize individual data points compared to the whole data set. They are a fun and engaging way to present percentages or proportions.

Here is a simple method for creating waffle charts:

Data Visualization Tips – Enhancing Chart Aesthetics

The aesthetics of your Excel chart play a significant role in how effectively your data is communicated.

A visually appealing chart is easier to understand and engages your audience. Enhancing chart aesthetics involves working with various chart elements and features, such as colors, styles, and data labels.

Adding data labels, for instance, provides additional information on your chart, making it easier to interpret.

Besides, you can customize the chart’s colors and styles to match your presentation theme or company branding.

Check out this post for more information on good dashboard design principles .

Working with Chart Elements

Working with chart elements can significantly improve the readability and effectiveness of your data visualization.

Some key chart elements you can manipulate include titles, legends, and data labels.

• Data labels provide additional context to your data and can be customized to suit your chart
• Modify axis labels and gridlines to adjust their appearance and improve readability. Check out this video on how to add gridlines to your Excel charts:

These chart elements can enhance your aesthetic appeal and make your data easier to interpret.

Customizing Chart Colors and Styles

Spicing up your Excel charts is easier than you think.

The ‘ Chart Design ‘ tab in the Excel ribbon allows you to alter your charts’ aesthetics significantly.

Navigate to the ‘ Chart Styles ‘ section, and you’ll see various styles for your chart. 

Looking for a bit more customization? No problem! Simply click the ‘ Change Colors ‘ dropdown and choose a color scheme.

You can use Excel’s preset color schemes or create a custom color palette for brand consistency. Minor visual changes can significantly affect your chart’s overall look and feel.

3D Charts and Effects

Adding a third dimension to your charts can make them pop . But be careful.

While 3D effects can add a specific wow factor, they can also lead to misinterpretations of your data if they are not used properly.

To add 3D effects to your charts, click the ‘ Chart Styles ‘ and choose a style with 3D effects. 

Remember, though, that 3D effects should be used sparingly and only when they can enhance the understanding of the data. Overuse of these effects can lead to cluttered, confusing charts. When it comes to 3D effects, less is often more .

Advanced Excel Graphics

Beyond the basic charts, Excel offers advanced graphics capabilities to take your data presentation to the next level.

This includes using Sparklines, shapes, and icons, among other features.

Sparklines are mini-charts within individual cells, each representing a row of data. They give a quick snapshot of trends, helping you understand your data at a glance.

Excel offers line, column, and win/loss types of Sparklines that you can add with the Quick Analysis tool.

Using Shapes and Icons

Remember to appropriately format these shapes and icons to convey the right message and not distract from the data.

Portraying a Story Through Data

Excel visualization is not just about creating charts or diagrams; it’s about telling a story with your data. This is where the concept of data storytelling comes in.

It’s about using visualization tools to highlight key points and trends in your data, making it easier for your audience to understand and absorb.

It’s not unlike creating a plot in a novel where rows and columns of data are the characters, and the chart is the narrative arc. Every element should convey your story effectively and compellingly, from simple bar charts to intricate trend analysis.

Exporting and Sharing Your Visualizations

Once you’ve created your data visualization in Excel, it’s important to know how to share it! This involves exporting the visual representation of data in a format that others can easily access.

Whether you’re sharing a simple bar graph or a complex infographic, the export method will depend on the intended use of the chart/graphic.

This process can be as simple as saving your chart as an image or embedding Excel visuals in PowerPoint presentations and documents.

Saving Charts as Images

One of the simplest ways to share visualizations is by saving them as images .

To do this, right-click the chart and select ‘Save as Picture.’ Several image formats are available, each with its uses.

For instance, JPEG is great for photographic images, while PNG is ideal for images with transparent backgrounds. However, it’s important to consider the resolution of your image. High resolution is crucial for clear, crisp images, especially if they’re intended for print.

Embedding Excel Visuals in Presentations and Documents

Embedding them in presentations and documents is another way to share your Excel visualizations.

This can be done in two ways: linking and embedding .

• Linking refers to connecting the original Excel file and the document where it’s inserted. Any changes made to the original file will automatically update in the document (assuming the link isn’t broken ).
• Embedding involves inserting a copy of the chart into the document. While this won’t update automatically, it ensures that the chart will always be available, regardless of the status of the original file.

Both methods have advantages and should be chosen based on your specific needs.

Frequently Asked Questions

What are some common mistakes for beginners to avoid in data visualization with excel.

Common mistakes include overcrowding the chart with too much data, using inappropriate chart types, neglecting to label axes or data points clearly, and choosing colors or styles that reduce readability.

What are the best practices for presenting Excel data visually to a non-technical audience?

Focus on simplicity and clarity .

Use straightforward chart types, avoid technical jargon, and highlight key takeaways. Ensure your charts are well-labeled, and use annotations or callouts to draw attention to important data points.

What are some resources to learn more about Excel visualization? 

For more tips and tricks, visit my YouTube channel . Alternatively, look at Chandoo’s training, where I learned many excellent dashboard design ideas.

Can Excel visualization help in career development?

Absolutely! Proficiency in Excel visualization is a valuable skill in many industries.

It’s especially relevant in fields like data science, finance, marketing, and others involving large amounts of data. Effectively communicating data through graphical representation can give you a significant advantage in your professional journey.

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How to Use Charts and Graphs in Excel

Experiment with Excel charts and graphs to display your data

What to Know

• Open an Excel file containing data. Select a range to graph.
• Go to the Insert tab and select Chart Type or choose Recommended Charts and hover over the options to see previews.
• Select a chart format to add to your worksheet. Use Chart Tools to make changes to the graph.

This article explains how to add a chart in Excel and includes information on the types of charts available and how each is best used. The instructions apply to Excel 2019, 2016, 2013, 2010; Excel for Microsoft 365, and Excel Online.

Add a Chart in Excel

Charts and graphs are visual representations of worksheet data. These graphics help you understand the data in a worksheet by displaying patterns and trends that are difficult to see in the data.

The best way to learn about the various charts in Excel is to try them out.

Open an Excel file that contains data.

Select the range you want to graph. To select a range, drag from the first cell to the last cell.

On the ribbon, go to Insert and select the desired Chart Type to see a menu of available chart formats of that type.

If you don't know what type of chart will work best with your data, select Recommended Charts to see your data formatted in different charts and graphs.

Hover over a chart in the list to see a preview of the chart in the worksheet.

Select the chart format you want to use. The chart is added to the worksheet.

To make changes to the chart, use the Chart Tools (located to the right of a selected chart) or right-click the chart to select the data, choose a different chart format, or format the gridlines and axis.

Graphs are used to illustrate trends over time, and charts illustrate patterns or contain information about frequency. Select the Excel chart or graph format that best fits your needs. Excel has many types to select from.

Compare Values With Pie Charts

Pie charts  (or circle graphs)   compare values and display results in percentages. The entire circle of a pie chart represents 100 percent. The circle is subdivided into slices representing data values. The size of each slice makes up part of the 100 percent it represents.

Here are a few examples:

• A company's profits for each month can be shown in a pie chart with each piece of the pie representing a month as a percentage of the year's total profits.
• A baseball player's batting average can be shown with a pie chart because it represents the percentage of hits compared to the total number of at-bats for a season.
• The percentage of your total daily calorie count that one cheese and bacon hamburger represents.

Show Comparisons With Column Charts

Column charts , also known as bar graphs, show comparisons between data. These charts display the differences between groups of data.

The values are displayed using a vertical bar or rectangle, and each column in the chart represents a different data group. For example:

• In a school class, a bar graph shows and compares the number of students with different grades. The more students with a particular grade, the taller the bar for that grade is on the graph.
• Use different colored columns to illustrate the calories in a cheese and bacon hamburger compared to the calories in a bowl of beet greens and the calories in a bowl of mac and cheese.

Compare Data With Bar Charts

Bar charts are column charts that have fallen over to one side. The bars or columns run horizontally along the page rather than vertically.

The axes change as well. The Y axis is the horizontal axis along the bottom of the chart, and the X axis runs vertically along the left side.

Track Changes Over Time With Line Charts

Line charts , or line graphs, show trends over time. Each line in the graph shows the change in the value of one item of data.

Similar to most other graphs, line graphs have a vertical axis and a horizontal axis. When plotting changes in data over time, time is plotted along the horizontal or X axis, and the other data is plotted as individual points along the vertical or Y axis. When individual data points are connected by lines, they show the change in the data.

For example, use a line chart to show changes in your weight over a period of months as a result of eating only vegetables every day for lunch. Or, use a line chart to plot daily changes in specific stock prices.

Line charts are also used to plot data recorded from scientific experiments, such as how a chemical reacts to changing temperature or atmospheric pressure.

Show Trends With Scatter Plot Graphs

Scatter plot graphs show trends in data. These graphs are useful when there are a large number of data points. Like line graphs, scatter plot graphs can be used to plot data recorded from scientific experiments, such as how a chemical reacts to changing temperature or atmospheric pressure.

Line graphs connect the dots or points of data to show change in data over time, while a scatter plot represents a "best fit" line. The data points are scattered about the line. The closer the data points are to the line, the stronger the correlation or effect one variable has on the other.

If the data points cluster closer to the best fit line, the scatter plot shows a positive correlation in the data. If the data points are further from the line, there is a negative correlation in the data.

Display Two Charts in One With Combo Charts

Combo charts   combine two different types of charts into one display. Typically, the two charts are a line graph and a column chart. To accomplish this, Excel makes use of a third axis called the secondary Y axis, which runs along the right side of the chart.

Combination charts display average monthly temperature and precipitation data together, manufacturing data such as units produced and the cost of production, or monthly sales volume and average monthly sale price.

Represent Data With Pictographs

Pictographs   or pictograms are column charts that use pictures to represent data instead of columns. For example, a pictograph with hundreds of hamburger images stacked one on top of the other shows how many calories one cheese and bacon hamburger contains compared to a tiny stack of images for beet greens.

View Financial Data in Stock Market Charts

Stock Market charts  show information about stocks or shares such as their opening and closing prices and the volume of shares traded during a certain period. There are different types of stock charts available in Excel. Each shows different information.

Newer versions of Excel also include Surface charts, XY Bubble (or Scatter) charts, and Radar charts.

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