What is Data Visualization - Importance | Tools | Examples

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In today's world, where we rely a lot on data, it's essential for everyone, including employees and business owners, to be able to look at data efficiently and understand what it means. Every year, more and more jobs require people to know about data and how it affects things. That's where data visualization tools come in. It uses pictures and charts to help us see and make sense of data. One popular way to do this is through dashboards, which are like special screens that show data in a simple and easy-to-understand way. Businesses use dashboards a lot to study and share information.

What is Data Visualization?

Data visualization means showing data in pictures and drawings. It uses charts, graphs, and maps to help us effortlessly see trends and patterns in the data. These tools are great because they make it simple for anyone, even if they're not experts, to understand the data. These tools are necessary for businesses dealing with Big Data. They help analyze immense amounts of information and make savvy decisions based on the data.

Importance of Data Visualization

Data visualization is important because it helps people understand data better by showing it in pictures and charts. It doesn't matter if the data is easy or hard to understand; a suitable visualization can make it exact for everyone, no matter their expertise. Almost every industry benefits from understanding data, from science and technology to government, finance, marketing, history, and more. Being able to visualize data is a valuable skill because it helps professionals make better decisions and tell compelling stories with data.

In today's world, professionals who can use data and visuals to explain things are in high demand. Blending technical analysis with creative storytelling is pivotal for success in many fields. Data visualization is adequately at the center of this, bridging the gap between analyzing data and telling engaging stories.

Science of Data Visualization

Data visualization is based on how our brains work. There are two main ways our brains process information:
 

• System 1: This is fast and automatic, like reading a sign or riding a bike.
• System 2: This is slower and more logical, like solving a complicated math problem or understanding complex social cues.

Both systems help us make sense of the world around us, and data visualization taps into these processes to make information more effortless to understand.

Pros and Cons of Data Visualization

Even though using graphics to show data seems easy, there can be problems if the wrong type of visualization is used. It's important to remember both the good and bad sides of using data visualizations.

Benefits

Data visualization has several benefits:
 

• Better Decision-Making: It helps top management make smart decisions faster by spotting patterns and trends in data. As a result, it stays ahead of competitors.
• Improved Customer Service: By visualizing data, businesses can understand customer needs better, improve products or services, and fix operational problems.
• Engaged Teams: Visualizing data helps teams work together towards common goals, track progress, and stay motivated. For example, a sales team might use visual charts to boost sales and stay motivated.

Challenges

Data visualization can sometimes be tricky and lead to misunderstandings:
 

• Too Simple Data: If data is made too simple in charts or graphs, critical details can be missed. For example, saying video games cause lower grades without considering other factors like demographics is too simple.
• Bias: People can unintentionally show data that fits their opinions, which can be unfair. It's significant to have diverse teams to avoid this.
• Exaggeration: Sometimes, people use data in graphs to show connections that don't exist. However, this can lead to bad decisions, like overspending on equipment, because data shows unrelated improvements.

Overall, using data in charts and graphs is helpful, but it's essential to be careful and not jump to conclusions based only on what you see in visualizations.

Our Learners Also Read:- What is the Difference between Machine Learning and Deep Learning
 

DV Types

Data visualization has been around for a long time and has evolved to help us understand information better. One famous book about this topic is Edward Tufte's 'The Visual Display of Quantitative Information.' Today, companies use dashboards to show their performance in real-time.

Dashboards use different ways to show data:
 

• Tables: These are grids with rows and columns, valuable for comparing things but can be overwhelming with too much detail.
• Pie Charts and Stacked Bar Charts: They thoroughly show parts of a whole. As a result, it compares the sizes of different parts.
• Line Charts and Area Charts: These show how things change over time, applicable in predictive analytics.
• Histograms: They show how data is distributed, making it easy to spot unusual things.
• Scatter Plots: These show relationships between two things, often used in data analysis.
• Heat Maps: They show behavior by location, like on a map or webpage.
• Tree Maps: These show hierarchical data using shapes, valid for comparing categories based on their size.

Best Data Visualization Tools

Accessing tools for visualizing data has always been challenging. We’ve some open-source libraries like D3.js, ECharts, Vega, and deck.gl. These libraries help analysts create interactive and dynamic visualizations that can be easily shared and viewed by a wider audience:
 

• D3.js: This library uses JavaScript to create interactive visualizations in web browsers. It's versatile and allows for custom interactions and animations. In addition, it is one of the free data visualization tools. 
• ECharts: A powerful library for adding customizable charts to various platforms, including web pages, presentations, and research papers. It's based on JavaScript and ZRender.
• Vega: Described as a "visualization grammar," Vega helps customize visualizations for large datasets accessible on the web.
• Deck.gl: Developed by Uber, deck.gl is used for exploring big data through high-performance GPU-powered visualizations on the web.

Here, we mentioned top Data Visualization software. Now, we will elaborate on the best examples. 

Some Common Examples of Visualizing Data

The following section will discuss some examples. As a result, it will be worthwhile for you to understand it in depth.
 

• Data Science: Experts use tools like Python or R to understand data patterns using colors, plots, and shapes, making their work more efficient.
• Marketing: Marketers track web traffic and social media data to understand customer trends and preferences, using charts and graphs to visualize this information. Moreover, we’ve also explained some other examples of data visualization. 
• Finance: Investors analyze stock prices over time using line graphs to decide on buying or selling investments for short or long periods.
• Health Policy: Policymakers use maps with colors to show health trends like cancer or Ebola rates in different regions, helping them make informed decisions. 

Conclusion

To sum up, using visuals to show data is useful for understanding and making decisions. By using the proper methods with visuals, businesses can make the most of their data and stay ahead in today's data-driven world. Moreover, we have detailed the entire concepts of data visualization. You just need to read and understand the following article.

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