What to Keep in Mind When Creating Data Visualizations in Excel

What to Keep in Mind When Creating Data Visualizations in Excel

Data is one of the most valuable and abundant resources available to businesses. The insights gleaned from the vast amounts of data generated each minute of every day can guide virtually every business decision, frequently leading to positive outcomes.

There are numerous data visualization tools available for creating business-related illustrations. Fortunately, your computer probably already has one of the most widely used and straightforward options: Excel data visualizations.

For a similar, albeit limited, experience, you could use free alternatives like Google Sheets if you do not have access to Microsoft Excel.

Excel is a powerful tool for professionals of all levels who want to analyze and illustrate datasets, despite the fact that it is not software for visualization. Here are the kinds of information perceptions you can make in Succeed and the means engaged with doing as such, alongside certain tips to help you en route.

Types of Data Visualizations in Excel

Depending on the data you have at your disposal and your objectives, you can use the following Excel data visualization tools:

  • Pie charts, bar charts, histograms, scatter plots, and area charts are just a few examples of other visualization methods that can be used to show large or complex data sets. These are some:
  • Timelines, Gantt charts, heat maps, highlight tables, and bullet graphs are all examples of more complex visualizations that may not be possible to create in Excel or require additional tools, such as geographical heat maps.

How to Make Data Visualizations in Excel

Depending on the kind of graph or chart you choose, different steps need to be taken to make data visualizations in Excel. The procedure for basic visualizations is largely the same. Additional steps may be required for illustrations and datasets that are more complex.

To begin creating a data visualization in Excel, organize a spreadsheet. Labels and your final dataset ought to be included in this.

Then, highlight the labels and other data you want to include in your visual. From the main menu, select “insert,” then select the kind of graph or chart you want to make. The visualization will automatically appear in your spreadsheet after you select it.

To edit specifics like the title, labels for the axes, and colors on the graph or chart, right-click on it. When you do this, a pop-up or side panel will open with options to change font sizes and styles, add a legend, and adjust the scale.

How to Make Visualizations in Excel 1: Some Tips Choose the Right Kind of Visualization To make a good data visualization, you need to pick the right kind of graph or chart. Think about your intended audience, the size of your dataset, and the kind of data you’re using.

It can be detrimental to viewers’ comprehension of the information if there is a mismatch between the kind of data being utilized and the visual that is used to present it. How you present the data, for instance, is influenced by whether you are working with qualitative or quantitative data.

1. The complexity or simplicity of your illustration is also influenced by your intended audience. For instance, if you’re giving a presentation to a large group of people or to high-level stakeholders, it helps to focus on key trends and insights rather than individual data points.

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2. Remove Data That Is Irrelevant or Inaccurate In the end, the quality of your visualization is only as good as the data you use. Because of this, cleaning the data after it has been collected is crucial to get rid of any incorrect or irrelevant information. Data wrangling and data cleaning are terms used to describe this procedure frequently.

Data visualizations that are inaccurate or misleading can result from a lack of thorough data cleaning prior to use.

3. Provide Context for the Visualization If necessary, provide additional context and a key or legend to assist viewers in understanding your illustration.

Take, for instance, a heatmap that depicts the rate of COVID-19 infections in a location over a predetermined time period. Viewers need to be aware of specifics like the time period being examined, the data source, and the significance of each color in order to gain a clear understanding of the information that is being presented.

Because it assists viewers in understanding the information that is being displayed, this context is crucial. For instance, the heatmap would be useless without a key that clearly defines the coloring system because it would be nearly impossible to know what each color means.

4. Using data to tell a story is the final key to creating a compelling visualization. Your visualization ought to be clear if the data supports a hypothesis or illustrates a trend. After all, the goal of visualizations is to make the findings easy to understand and digest for viewers.

In addition to making your visualization more interesting and engaging, telling a story also helps you make decisions based on data. Additionally, it aids stakeholders in comprehending the essentials of your findings and, as a result, assists them in making decisions.

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