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:
- Pie charts
- Bar charts
- Histograms
- Area charts
- Scatter plots
Other visualization techniques can be used to illustrate large or complex data sets. These include:
- Timelines
- Gantt charts
- Heat maps
- 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.
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.
