Data visualization is one of the vital components of data analysis, given that they have the capability of summarizing large amounts of data efficiently in a graphical format. There are many chart types known, each with its own strengths and use cases. One of the most misleading parts of the analysis process is choosing the right way to represent your data using one of the visualizations. This blog will dig deeper into everything that needs to be known about data visualization.
What is Data Visualization
Among data visualization resources, n infographic or visual can help us analyze data and hidden patterns in a much more manageable way. These are the common roles that data visualization includes. First of all, showing change over time and showing a part-to-whole composition. Second of all, looking at how data is distributed and comparing values between groups. Then, observing relationships between variables, and finally, look at geographical data.
The kinds of variables that you are analyzing and the audience for the visualization can also affect which chart will work best within each role. Certain visualizations can also be used for numerous purposes depending on these factors.
Data visualization is a way in which you can create a story through your data. When data is complex and understanding the micro-details is essential, the best way is to analyze data through visuals.
Visuals can be used for two purposes. In the first place, there is exploratory data analysis, which is used by data analysts, statisticians, and data scientists to better understand data. As it is rightly called, it is used to explore the hidden trends, and patterns in data.
In the second place, there is descriptive data analysis. Once the analysts understand the data and find their results, the best way to convey their ideas and findings is through visuals. This is used to craft a story that will appeal to the viewer offering deeper insights.
How to Become Great at Data Visualization
If you use a consistent coloring scheme for your visuals, you should consider the following. While color adds meaning and beauty to a chart, it is often best to use colors for highlighting important details and not merely for attractiveness. Too many colors will destroy the purpose of coloring while using a single color or too many shades of one is more likely to confuse viewers. Plus, take into account the visually impaired while designing visuals. Use colors intuitively. For instance, for sentiment analysis, we can use green color for positive emotions, and red for negative emotions.
It is recommendable that you use size, shape, and format to convey semantics. Using size, shapes like circles and squares may add semantic meaning and thus help viewers absorb the data easily. Also, sometimes arranging bar graphs in ascending order makes more sense, in the case of ordinal data, rather than arranging it alphabetically or randomly.
Furthermore, you should use legends, and words to properly annotate data. Use labels wherever required but do not clutter the graph with text. Use text data wisely. Place the visual data in a manner that is easy to grasp. You should also use interactive plots, which means racing graphs, interactive plots add value and help viewers engage with the data in greater depth.
Moreover, you have to remove junk from the chart. Remove the unnecessary junk from the chart that may distract the viewers. Do not combine multiple views in a single visual to such an extent that it makes it difficult to comprehend. Use the scales to tell the real picture. Label the data accurately, so do not over-label. Make sure that the labels are visible and oriented properly. Do not add dimensions to visuals that may lead to skewness.
Finally, craft out a complete story. Focus on the bigger picture that you are trying to capture. Do not provide inaccurate or misleading visuals. Use the visual tools wisely to speak more than the text would do.
3 Common mistakes to avoid while visualizing data
In this section, we will cover three things about data visualization that you must avoid at all costs.
1. Using a visual when it might not be needed
If data can be communicated effectively with statistics, we do not need to create visuals. Visuals make it easier to analyze what numbers cannot convey. In consequence, you must choose wisely when to use a visual tool.
2. Think about what you are trying to convey
Correlation does not imply causation. We need to ensure results are backed up by proper research and experiments before jumping to causes.
3. Use of 3-D visuals
Make sure that the 3-D view does not hide a part of the data or distort the data. Use 3-D graphics with utmost care. Do not add orientations that may fool the viewer and destroy the purpose of visualization.
Data visualization is a big deal within the design field of work. Choosing the right chart for the job depends on the different types of variables that you are looking at and what you want to get out of them. It is possible that breaking out of the standard modes will help you gain additional insights. Experiment with not just different chart types, but also how the variables are encoded in each chart. It is also key to keep in mind that you are not limited to showing everything in just one plot. It is usually better to keep each plot as simple and clear as possible, and instead use multiple plots to make comparisons, show trends, and demonstrate relationships between multiple variables.