Why Data Scientists Need to Be Better at Data Visualization

• By Ryan Erwin
• 14-09-2022
• Big Data

Analyzing complex data sets is an art that requires one to learn and master technical jargons to be able to read and interpret data in a meaningful way. However, researchers from the University of Minnesota have revealed that the brain can process visuals 60,000 times faster than basic content. Many people prefer viewing data visualization to grasp the intended message.

Data visualization makes it easier to get a grip on complex content that has been presented in an organized manner. Visualization helps you get an instant understanding of the concept presented in data values that might be difficult to evaluate when the data is presented in a row format.

Clear interpretation of data is one of the uses of data visualization for data scientists. It is evident that data scientist deals with vast sets of data that are complex in nature and needs you to use the third eye to generate meaningful insights from it. When Data visualization is introduced into play, it becomes easier for data scientists to create and present data reports after analysis.

Still, there are some uncertainties about why data scientists need to be better at data analysis and visualization. Below are some of the explanations that clear the doubts and support this reality. Read on!

Data Visualization Explains a Data Process in Details
Data scientists use data visualization to explain and demonstrate a particular data process from the first step to the final stage. The process can be easily explained using different tools in a more visualized format that brings every bit of the process to the limelight. The mechanism enables viewers to save time while reading the content since everything is simple.

Few people can easily understand when a data process is explained in a complex format since not everybody understands technical concepts. Besides, reading poorly arranged data is time-consuming, thus sending away potential customers. Viewers cannot understand your data concept by looking at the provided tables.

When visualization is put into practice, the presented data attracts viewers since it is arranged in an orderly way that viewers can read and interpret without wasting much of their time.

Discovering Trends and Patterns in Data
The essential function of data visualization is to help data analysts identify patterns and trends in the data they are dealing with. It is pretty simple to uncover patterns and trends when you have all the data laid before you in a visual format rather than a plain text format. It is a matter of looking at the nature of the data and comparing it with the other values available.

Once you have identified possible trends and patterns, it becomes easier for researchers to use the information more appropriately in decision making. When the data is presented to the viewers, it is vital to mark similar patterns and trends to enable the audience read through the lines easily and without wasting much of their time.

Telling Data Stories
When you want to grasp your audience's attention, you need to get a unique way of making them engaged. Telling data stories is one of the best ways that you can adopt and put into practice. Once the data scientists are done analyzing and processing data, they need to present it to the audience in a more convincing manner.

Data visualization offers the best medium that you can use to tell a data story to your market audience. The visualization you choose such as a heat map, tree map, bar chart, or a Sankey diagram can showcase data facts in a simple format that you can understand or tell a story that leads the viewer to a defined conclusion. The data story needs to be similar to any story with an exciting beginning and a great ending.

In addition, you don't need to do it blindly. Begin by creating a plot for your story to make it engaging and exciting. For instance, if you are a data scientist and are working on data for a company executive detailing the profits and losses, you can begin your story by analyzing the profits and losses of the company before moving to recommendations.

Presenting Data in a Beautiful Manner
Let's face reality; raw data can be classified as informative and not beautiful. Data visualization offers data scientists a unique way of presenting informative data in a beautiful way that attracts viewers to analyze the displayed concepts. When working on raw data sets, always remember that human beings are visual beings who can understand visual information much faster.

However, it is essential to ensure that you are not sacrificing function for beauty when using visualization. The primary goal of the data is to impact the viewers and not to impress them. However, if you can make all these aspects come true, then you stand a better chance of being on the winning side.

When viewers get impressed with your mode of data presentation, you are in a better position to get your message delivered within the shortest time possible.

Placing the Data into the Correct Context
Many people find it challenging to uncover the context in which data has been presented. This results in an inaccurate understanding of the intended message. Remember that the context carries the meaning of the entire data circumstance. Viewers cannot grasp data by reading the displayed numbers without further interpretation. An example of this thought process is not understanding the full picture when it comes to security measures—if you’re only viewing quantitative data of office security system events without security camera footage, you might not accurately understand what security events occurred.

Data scientists need to choose a data visualization tool that is in line with the context of the data. This is because the visualization tool you choose adds weight to the data making it easier for viewers to grasp all the displayed elements correctly. Reading and understanding raw data is difficult because the context in which the information showed is hidden.

When data scientists have clear information about the context of the data before them, it is easier to choose the best data visualization tool to present the data and accord it with the right tool to envision data.

Bottom Line
Researchers have certified that approximately 93% of communications are non-verbal. This is due to the fact that humans are visual beings, and they can easily understand visuals than content. Data visualization offers data scientists a more friendly way of displaying data in a way that viewers can easily read and understand.