Nothing is as enjoyable as a well-organized data set outlined on a chart. This is where the beauty of data visualization comes into the game. Data visualization plays a critical role in displaying data insights making it easy for organizations to understand the message portrayed in their data.
When your data is randomly outlined in a spreadsheet, it becomes difficult to understand the message displayed. This means that the business stakeholders find it challenging to use the data during decision-making. Note that data offers a better way to stay informed and can contribute to your business’s development.
But do you know that there are good and bad examples of data visualization? Data visualization is a broad topic that needs you to shed more light for a better understanding. Read this blog as we shed light on the good and bad examples of data visualization.
What is Data Visualization?
Many people strongly believe that when you mention data, you refer to numbers and statistics. The reality is that any collected facts can be regarded as data. Data visualization refers to everything you do to make the facts easy to read and digest using different graphic designs.
Now that you understand data visualization let’s look at some of the good and bad examples available.
Good Examples of Data Visualization
Sometimes, news can have information that needs to be placed in context. To place your data in context, you need a reliable data visualization tool to help you transform the information into the required context. Note that businesses need to be updated with the most recent news in the market.
Data visualization makes it easier for viewers to understand the context of their data and generate meaning from it. Failure to involve visualization in news preparation it becomes easier for viewers to understand the message you intend to deliver.
Improving the Workplace Culture
It’s the responsibility of every employer to ensure that all their employees are safe and satisfied. Data visualization gives you everything you need to share resources with your team members. You can use this approach to tackle matters to do with burnout within your job environment and among your team members.
Besides, this is among the best ways that you can use to ensure that your employees are at the best of their minds to remain productive. Ensuring that your employees are satisfied gives you a better chance to transform your business performance and generate more revenue.
Data visualization can help you to entertain, excite, and inspire your market audience. Also, it can greatly save you from repeating mistakes and give you room to prepare for emergencies within your business. Visualization has played a critical role in evaluating the risk caused by the coronavirus pandemic across the globe.
Also, it can help you evaluate the economic risk within your business. It plays a crucial role in ensuring that your business operates in the right lane to attain great heights of success.
Analyzing Consumer Trends
Regardless of your industry, you need to ensure that you stay at the helm of your customer base. This is why many brands across the globe have heavily invested in the use of social media. The more you connect to your customers, the more they feel the positive vibe of your business.
Data visualization offers an excellent approach that you can use to display your message instantly. You can learn more about consumer trends, increasing your chances of securing more customers.
Introducing Your Brand
When introducing your business brand to the market, you need to make it unique. You need to do something that will make your brand memorable in the minds of your target customers. When you pitch to investors or attract new leads, stylish data visualization can make you stand out from the crowded environment.
Data visualization is a key element in helping you break down the entire operational model of your business. It can also enable you to portray the size of your business to prospects. Visualization can enable you to portray every element of your business that you want to.
Breaking down data in a format that makes it easily digestible has an obvious impact on educational purposes. A concept displayed in a visual format gives room for people of all ages to understand a new concept across industries. Visualizing educational data makes it easier for you to learn and draw conclusions from the existing data without scratching your head.
In addition, it saves time since you won’t need to spend the entire day trying to master something. It’s a matter of taking a couple of minutes to read and understand the data.
Bad Examples of Data Visualization
Using the Wrong Chart Type
Charts play a key role in visualizing data. However, there are instances where wrong chart types are used to visualize data. In such a scenario, the meaning of the data gets lost, making it difficult for readers to understand the point you are trying to communicate to them.
Before choosing the chart type, you intend to use it in your data visualization activities, ensuring that it reciprocates your data needs. Once you apply the wrong chart, you automatically mislead your market audience.
Cherry picking refers to a data deception whereby particular sources of information are omitted from the survey, chart, or graph. The ultimate goal of cherry-picking is to clean data by offering predictable results that suit a particular data category. However, when this is done, it does not paint a clear picture of the exact data results making it difficult for you to understand the message.
This mode of data processing is inaccurate since the lines are mostly exaggerated. Also, given that the results are given in percentage, they do not add up to 100%.
Omitting data is one of the most popular bad examples of data visualization. When you omit various data points, you are likely to formulate patterns that do not exist. Besides, when you omit some data points, you will automatically miss out on the context of the data. When you leave out some variables, you will affect how the data needs to be translated.
When trying to explain the meaning of a particular data variable, you need to be extremely keen on its context. If you realize any data point has been omitted, you are likely to miss out on the most important point.
The assumption between correlation and causation is that two different variables change simultaneously, causing one to change the other. These are rumors! With the rise of big data, you must note the higher use of the two terms. However, data scientists mostly care about correlation more than causation when evaluating patterns in lathe data sets.
Many people strongly believe that correlation and causation refer to the same thing. This is wrong! These two are different variables, and each variable has an impact during data analysis. It’s vital to keep all these aspects in mind during data visualization to avoid affecting the final results.
Misleading Pie Chart
When analyzing bad examples of data visualization, a misleading Pie chart should never miss on your list. Pie charts are used to display proportional values that equal 100%. However, when given a mix of choices, the final results can be confusing, thus affecting the accuracy of the data.
When visualizing data on a pie chart, the first thing to do is ensure that all the segments add up to 100%. Anything more than or less is likely to give you misleading results that will affect your final data report. Besides, a pie chart should not be used to present massive amounts of data.
Even though the dual-axis chart has proved to be the most effective option in terms of comparing two sets of informational data, it only shares a unified scale when the data visualization pressure is too high. The chart can be misleading, especially when the markers are not placed accurately.
When using this chart type to visualize your data, you need to ensure that the marker is properly positioned. This will help you to minimize the chances of making errors.
Data visualization is a fundamental aspect of the current world. Surviving in the current business world requires you to make good use of different elements of data visualization to analyze your data and generate actionable insights that will keep you going. It’s no secret that data visualization is applied in different scenarios in our daily lives.
The ultimate goal of using data is to generate sense out of the data we generate. Getting to learn more about the good and bad examples of data visualization is important in ensuring that your organization is moving in the right direction. Also, it’s a key way to help you implement different aspects of visualization in your daily operations to enable you to increase your company revenue.