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You’ve just made a presentation using beautiful data visualization tools.
You read or heard somewhere that visual content has more ‘persuasive effect’ than plain words and numbers. So you incorporated attractive and colorful charts to help drive your point with a BANG!
However, your presentation happens to leave your audience less convinced than you hoped. You end up creating more confusion than you found.
What could be the problem?
There are multiple data visualization mistakes that could rock your highly informative presentation. You burnt a lot of midnight oil rehearsing for the whole thing only to be brought down by these silly mistakes.
This blog post will walk you through these data visualization mistakes, some of which affect you on-daily basis. And proven solutions that actually work.
These tips have helped our clients polish up their data visualization for presentations. In fact, you’ll improve clarity in your presentation and reports by more than 70%.
They may seem simple. Ignore them at your own risk!
Whether you’re new to data visualization or a seasoned veteran, we hope you get a lot of value from the tips in this blog post.
In this blog you will learn:
Following points you should have in mind:
Deciding on the type of visualization can be challenging since there’re so many “pretty” but uninformative visualizations. Always strive for clarity and simplicity. Great rules of thumb for this are:
Take a look at the pie chart above. Does it feel crowded?
Simply put, all charts aren’t created equal.
There are more than 10 data attributes in this pie chart visualizing the U.S. government budget ( hypothetical ).
Graphs, such as pie charts, are strategically positioned to visualize the composition of your data.
Looking at the various color and size combinations for each pie slice becomes an unwelcome hurdle.
What is this chart trying to convey?
Arguably, you may be better off sharing actual numbers without any visualization at all. It’s best to limit your pie charts to no more than five “pie” slices.
Trust us. Your audience will thank you.
Including over 12 data variables in a pie chart is an overkill. It makes it difficult to effectively compare and communicate the share of total data in the graph.
And the color and size combinations for each pie slice are unwelcome hurdles for your audiences. If you face this problem constantly, continue reading.
The Solution is on the way.
So how can you improve the pie chart above?
Check the diagram below.
You can overcome the challenge mentioned earlier by using visualization diagrams, such as bar and column charts. These charts handle relatively massive data.
While you’re at it, remove all unnecessary distractors, such as low contrast colors and more extended data labels.
Consider substituting a pie chart for a donut chart if you’re working with no more than 2–3 data points. Remember: Your audience will quickly lose interest if they come across yet another chart that’s challenging to read and interpret.
For you to choose the correct chart, ask the following questions.
The charts are perfect for comparing one or many value sets. And they can easily show the low and high values in the data sets.
To create a comparison chart, use these types of graphs:
Composition charts show how individual parts make up the whole of something, such as:
To show composition, use these charts:
Distribution charts visualize with clarity outliers, the normal tendency, and the range of information in your values.
Use these charts to show distribution:
If you want to know more information about how a data set performed during a specific time, there’re particular chart types that do exceptionally well.
You should choose a:
Relationship charts are suited to showing how one variable relates to one or numerous different variables. You could use this to show how a variable positively affects, has no effect, or negatively affects another variable.
When trying to establish the relationship between variables, use may use these charts:
Survey data can easily overwhelm you, especially when in raw form. You need specialized charts to get the most from your data. These charts include:
Stick a little longer to discover how you can access these charts at highly affordable rates.
Remember, data visualization methods aren’t one-size-fits-all.
Don’t get us wrong. 3D images are visually appealing.
However, what could be worse than using the wrong charts in visualizing your data?
That is 3D.
Besides, nothing in a data presentation screams “rookie” more than a 3D chart. It is one of those things where “just because you can doesn’t mean you should.”
3D charts are visually heavier, difficult to read, and sometimes downright misleading. Their only claim to fame is that they tend to be more visually interesting than 2D charts.
Don’t be that person who pursues a visually appealing graphic at the expense of conveying a concrete message.
Let’s use hypothetical revenue data for Amazon.
Do you think the graph above is the best way to visualize Amazon’s net revenue by product?
Can you confidently say that the AWS segment earned more revenue than subscription services based on the data presented in this pie chart?
Chances are, probably not.
3D charts can create false representations since our eyes perceive triple dimensional objects as closer as and more extensive than they genuinely are.
You can improve this graph’s value and appearance by changing it from a 3D pie chart to a Bar Chart Horizontal and reordering the data, as shown by the screenshot above.
Visualizing correlations between datasets gives you a broader understanding of a topic. Overlaying datasets on the same graph is one way of showing correlations.
When you take into account correlations, overlays lead to an “Aha!” moments. Conversely, when overlays are excessive in number, it’s difficult for viewers to draw connections.
A famous example is linking increased ice cream sales to surges in violent crime when both result from warm weather.
Your line chart may show the correlation between ice cream sales and violent robbery during warm weather. However, is it pragmatic to conclude that ice cream sales cause a surge in theft?
The critical takeaway is data visualization charts may show correlation, but it doesn’t equal causation.
People prefer visuals over text.
It’s a fact.
For instance, bright and colorful graphics can spice up an otherwise boring report. Nevertheless, beauty should never supersede the core messaging when presenting your chart’s data.
The visual charts you see around lack the correct scale. This distorts the core message.
Some people prefer beauty over communication. Remember, you’re visualizing data to enhance clarity and fill in gaping holes in your presentation story.
Please, avoid using inconsistent scales, such as starting at 30 instead of point ZERO. This will distort your presentation.
Colors vastly improve your ability to read and interpret a chart quickly. Using vivid, high-contrast colors for different categories is an easy way to deliver outstanding data visualizations.
Yes, you read that right.
In practice, this means that you should avoid placing colors that appear beside each other on the color wheel next to one another in your charts.
Instead, colors that are opposite each other on the color wheel provide the maximum possible contrast. Try to get as close to this ideal as possible while considering variables, such as branding.
Check the diagrams below. Can you feel the vibe?
A high vs. low contrast rendering of an otherwise well-designed pie chart. Notice the difficulty with reading the low-contrast chart.
Am sure you’re asking yourself: how can you avoid some if not all the mistakes highlighted above?
Well, there’s an affordable and easy-to-use data visualization tool that comes with over 50 different chart templates. This is to ensure you can visualize your data in every unimaginable way.
This tool is called ChartExpo (my personal favorite).
ChartExpo is an affordable and easy-to-use data visualization tool that comes as an add-on for Google Sheets and Excel.
Why Excel and Google Sheet?
This tool was designed with small-scale users in mind. Most people use Google Sheets and Excel to analyze and visualize their data. As an ardent user of either of the 2 spreadsheets, you encounter various problems, primarily when visualizing the data.
Some of the problems include:
ChartExpo is designed to ensure your charts are simple, easy-to-read, and, most importantly, communicates your intended message.
This user-friendly tool has its charts organized into six main categories that can guide you to select the appropriate charts.
The screenshot below shows ChartExpo’s user interface with 6 main chart categories.
The names of the chart categories, as shown above, can guide you to choose the relevant chart to visualize your data.
For instance, you need to click on the Specialized Survey Charts tab to visualize your survey data.
General Analysis Charts tab comes with commonly used charts, such as:
ChartExpo requires no coding at all.
All you need is to organize your data in Excel or Google Sheets. Let the tool do what it does best, i.e., visualizing data instantly.
Essentially, if you’re not satisfied with the tool within a week, you can opt-out as quickly as signing up for a trial.
We are sure you’re asking yourself, “How can I install ChartExpo in Google Sheets or Excel.”
Don’t worry.
Installing ChartExpo is not rocket science. It’s incredibly easy to install and use. Once you have the tool in place, some of the data visualization mistakes highlighted earlier will be a thing of the past.
Let’s jump to ‘How to Install Steps’ without wasting much time.
To Get Started with ChartExpo for Excel add-In, follow the Simple and Easy Steps Below.
You’ll be installing a cloud-hosted add-on on your Google Sheets.
There are two methods to installing the ChartExpo add-on for Google Sheets.
The first method is to visit the Google Workspace Marketplace and enter “ChartExpo” into the search bar.
Click on the ChartExpo tool and then press the blue Install bar on the resulting page.
This will begin the installation process. You may have to log in to your Google account and accept the plugin’s permissions. Once that is done, the add-on will install and be ready for use the next time you open Google Sheets.
Alternatively, you can download ChartExpo directly from the Google Sheets App. To get started, click on Add-ons in the top toolbar.
In the small menu that appears, press the option to Get add-ons.
Search for ChartExpo in the bar and click the ChartExpo tool when it appears in the results.
Press the blue Install button. Again, you will have to accept some permissions and you may have to confirm your Google account.
Each bar in the Likert scale chart represents a survey question. On each survey question, there’s a rating scale (1-5). The Likert Bar segments have customizable default colors.
Take a look at the sample survey data for a hospital, as depicted by the screenshot above. Each of the survey questions has a rating scale (1-5).
Each rating has a count, which is the number of people who gave a particular rating. For instance, 324 people provided a 1-star rating on the cleanliness of the hospital.
The data set makes sense in the format shown above. The green color denotes a higher rating, while red denotes a lower rating.
Overall, the hospital had higher ratings in the key areas respondents were queried during the survey.
The questions: “how do you rate the quality of the hospital staff, and how do you rate your overall experience?” received higher ratings than other questions in the survey.
The most upper bar – the overall bar – represents the summary for all the survey questions. Each bar has a weighted mean. The chart axis represents the overall percentage.
It feels like data is everywhere these days.
And it is. Few reports are complete without some form of data presentation. Eye-catching visualizations can be the difference between a dreary report and one that is both easy and informative to read.
Sometimes, an excellent presentation can be ruined if the wrong means of depicting information is selected. Some of the data visualization mistakes you face daily include:
Well, there’s a solution to some of the problems mentioned above. There’s an intelligent data visualization tool that comes with an expansive chart library for you to choose the relevant graphs. This tool is called ChartExpo.
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