The sheer quantity of information data visualization provides to the viewer in such a tiny space and without any explanation is astonishing. Data visualization helps the trends and patterns in the data come alive, making it very easy to express a message. Not only that, but because of how the human brain works, it also helps the audience to get an understanding in the quickest and most straightforward manner possible. As a result, it goes without saying that good data visualization is critical for analysis in every domain!

Every day, though, hundreds more visualizations are made. Some are well received by the audience, while others are flatly refused. Why is this the case? The solution, of course, is found in creation.

Data visualizations aren't as simple as they appear. It takes a lot of time and effort to complete. All of the visual components must be in the appropriate proportion. Your visualization will never have an impact if you do too little or too much. To make a meaningful representation, all of the relevant parts must be present in the proper proportions while also avoiding certain errors.

This post will provide you with crucial data visualization recommendations to help you enhance your visualizations as well as some common pitfalls to avoid.

So relax, since it'll undoubtedly improve your data visualization skills!

A good data visualization should be targeted at a specific audience and meet a specific demand.

This is the first of the data visualization suggestions. When producing data visualizations, it's crucial to understand the chart's purpose and target audience. These two factors alone may help you go from zero to hero in your visualization. This ensures that you not only produce a visualization with a strategic aim that responds to a specific query, but also one that the audience can understand.

If your audience does not have a scientific background, for example, don't make a visualization that is full of scientific data. Similarly, cramming your chart with several trends will most likely divert the viewer's focus away from the visualization's goal.

• Recognize the visualization's requirements. 

This enables you to construct a chart that clearly and concisely expresses a point. It also ensures that you aren't overburdening your chart with unneeded information that may cause the viewers to become confused. As a result, know what the visualization needs to do and keep it basic by highlighting a single point. This will have a long-term effect on the audience.

• Know who your viewers are.

Before you begin creating the visualization, consider what the viewer will be searching for in the chart. Recognize your audience's requirements and preferences. Get to know their history. Have they allotted enough time for a thorough visualization? What level of awareness do they have of the visualization's context? What are they seeking for in addition to the information they already have? Are they aware of having employed the graphs? And so forth. When it comes to designing successful and attractive data visualizations, your audience's information needs should be your guidance.

Select the most appropriate data visualization for your data.

This is the most important of the data visualization suggestions. There are several visualization graphs available. However, picking the proper one is critical for successfully highlighting the data's main trend. Additionally, selecting the appropriate graph for your visualization helps to ensure that the message is clear in order to attract people attention to your work. Each graph serves a distinct function, therefore it is important to understand when to utilize which graph.

Let see the different genres of visualization graphs and which contexts these should be used

• Bar graph: One of the most used methods of data visualization. They provide a lot of information in a short length of time. It's great if you compare a few figures from the same category. Comparing the sales of two distinct items over time, for example.
• Line plots: Useful for visualizing a numerical value's trend over a continuous time interval. They may be used to compare numerous values and efficiently capture trends and patterns in data. Showing the trend in a company's monthly income over the previous several months is an example of such a data visualization.
• Scatter plots: Can be used to display the relationship between two variables. Scatter plots make it easy to discover any correlations between variables or outliers in the data. It can be taken as example of comparing how the price of a property fluctuates depending on the size of the living room.
• Pie charts: Useful for illustrating the proportionate distribution of objects within a single category. However, they must be taken with caution, or they will cause more harm than benefit. The ratio of Android users to iOS users in a country, for example.
• Histograms: By segmenting data into distinct bins, histograms display the distribution of numeric values across a continuous interval. They're ideal for displaying data dispersion. Visualizing the amount of orders for a product over time, for example.

And so forth. Also, don't be scary to use many graph types in your visualizations. It occasionally allows the viewer to delve further into the data.

Keep your visualizations as simple as possible.

It's all too simple to cram too much data into a display. However, getting rid of useless data is more difficult. A minimalist visualization that is free of distractions and needless patterns is more likely to successfully communicate the content to the observer.

Edward Tufte refers to all visual features in a graph that aren't necessary for the user to understand the information in the graph as Chartjunk. These can include things like extra gridlines, confusing visual patterns, redundant axes, and shadows, among other things. Viewers find chartjunks to be an eyesore.

Good data visualizations need a title.

Labeling your visualization is an essential data visualization method. This better communicates what the graphics are attempting to communicate. They're easy to overlook when developing a visualization, so make sure you double-check for labeling before releasing it.

• Labels should be legible

‍ It's useless if it's not obvious. As a result, ensure the labels are simple to read and understand.

• Give the graph a title‍

When you give your graph a fitting title, viewers may quickly get a sense of what it's about.

• Make good use of a legend

‍The distinction between the various lines in the graph is simpler to identify with the help of a legend. When utilizing line charts, however, make an effort to indicate the directions. This makes it easy to distinguish between lines.

• Lable Axes

‍The meaning of the axes may not always be obvious from the title. As a result, you might wish to label your axes from time to time.

• Pay heed to the axes' labeling‍

It's not always necessary to mark all of the ticks on the axes. If they still communicate the correct information, you can name them at intervals.

Recognize the significance of text in charts

It's not just about statistics when it comes to data visualization. The text gives vital context for the viewer to understand the message. The headers, subheadings, and annotations you provide to the graphs help to clarify what's going on in the visualization. However, repeating the same topic in each text and using excessive content might backfire. It has the potential to cause more harm than benefit. As a result, it's advisable to employ text sparingly.

• Wherever possible, use simple sentences. The goal is for the image to be able to speak for itself.
• Only keep the annotations that are related to the topic. Annotations for each data point will distract the observer and clutter the image needlessly.
• You may need to use bold or italic text to highlight crucial areas of the graph, but don't overdo it or the contrast between ordinary and stressed text will be lost.
• Avoid Text that repeats the same message. For example, repeating the same message in the header and subheading might not be a good idea.
• Avoid using distracting fonts that are hard to read. The viewer should be able to grasp the message in the graph instantly without much work.

Make good use of colors in data visualizations

Everyone understands the importance of color and the influence it can have on the spectator. It's one of the most significant data visualization strategies you may use in your presentation. It might give your imagery just the perfect amount of zing to grab onlookers. However, if colors are used incorrectly, the viewer may be misled. As a result, the data visualization approach necessitates thorough scrutiny.

• Use the same color for the same kind of data. For example, a bar graph indicating sales for cars over the year can be indicated in one color while sales of bikes can be depicted in a separate color.
• Text annotations should be the same color as the bar or line they represent. This ensures that the viewer can quickly determine which data is being represented by the text.
• To illustrate the diminishing intensity of data, utilize shades of the same hue. This is how choropleth maps show patterns.
• Use only a few distinct colors. Using too many colors in your visualization might result in a cacophony.
• Use colors that the viewer will recognize. Even if no explanation is offered, utilizing red for hot temperature and blue for cool temperature may be clearly understood by the spectator.

Don’t deceive with your visualizations

While we make every effort to generate a good data visualization, the spectator can easily be duped. And sometimes we are completely unaware that we are fooling the spectator. Small factors like cherry-picking data, ignoring the baseline, and information overload, among other things, can lead to deceit. As a result, when developing visualizations, one should avoid making such stupid blunders.

• In your graphs, include baselines. The most fundamental kind of deceit is avoiding baselines. This produces a fictitious graphic that exaggerates the disparity between two data sets.
• If you don't include all of the data in the graph, the viewer will get an incomplete picture, which will lead to poor decision-making. For instance, viewing only a tiny portion of sales data that shows an increasing trend while the whole data shows a decreasing trend!
• Keeping hidden important information. This ensures that it may divert the viewer's attention away from the most important area of the graph.
• Putting an overwhelming quantity of information in the graph is one really clever approach of fooling the audience. This perplex viewers since they are unable to focus on any one pattern.
• Going against the norm. Using a green color to signal something incorrect and a red color to show something right, for example.

Visualizations of the stock market are a classic example of deceit. You might receive a misleading impression of how a firm is performing if you don't show the whole picture.

Create data visuals that are easy to interpret

The final of the data visualization pointers is that the visualization's interpretability is more important than its aesthetic appeal. All of the elements we've discussed so far should make the picture easier to understand. Visuals, such as pictures, patterns, and colors, are only useful provided they do not alter the viewer's message. Finally, if a basic line graph can effectively convey the idea to the viewer, you don't need to include beautiful logos or graphics in your visualizations!

Data visualization is a skill that takes time to perfect. Although these data visualization strategies and approaches aren't thorough, they will undoubtedly assist you in the proper way. The key to designing a successful and effective data visualization is to understand the viewpoint of the end user. Always strive to figure out what the final viewer wants to know.

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