Data Visualization

What is data visualization?

Data visualization is the process of turning raw data into visual representations. Typically, those visualizations are in the form of charts and graphs. Data visualization aims to make data easier and faster to understand, even by people who are not trained in analytics or typically good with numbers. 

Why is data visualization important and so effective?

To someone not trained in big data analytics, a wall of numbers or the statistical speak that comes with written correlation conclusions can seem daunting. But a bar chart, map, or graph converts these daunting facts and figures into something that almost everyone can understand. 

Visualization is a shared language. Even without formal training, most people can decipher the basic message behind something like a bar or pie chart. And when you couple that innate understanding with the knowledge of the business process or a small bit of explanation from the analyst or presenter, the result is usually a “click” moment when suddenly the transition of the number from the abstract. They begin to tell a story that the viewer understands and can respond to. 

People are trained by culture to look for these types of visual clues. Colors mean things. On the news, they might depict which political party is ahead in the race. The store’s colors tell you which discount applies to a particular product. Looking for patterns in colors, lines, and dots is natural when presented with charts and graphs. Plus, visual data is typically more eye-catching and interesting than rows of numbers and letters, so that helps keep the audience engaged in the story being told with the data. 

How is data visualization used?

Data visualization can be used in a variety of ways. Many times, data visualization storytelling is only limited by the availability of good data and the resources (whether people or software) to convert that data into pictures. Some ways data visualization is used include: 

Understanding all of the above types of data helps businesses discover root causes for issues, identify winning scenarios, and make decisions that lead to more positive outcomes. 

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Data visualization types

Infographics are an extremely common form of data visualization. In fact, high-quality infographics are 30x more likely to be read than plain text. Online marketers and content producers use these popular images to quickly convey information. Components of infographics often include bar and line graphs, pie charts, and even color-coded maps. 

Dashboards are an organizational answer to the infographic. Dashboards, which can typically be customized to fit the needs of various companies, departments, or teams, deliver important business intelligence reporting to the computer and mobile screens of managers and other decision-makers. Often, the data displayed in charts and graphs on a dashboard is real-time or at least very recent, which lets people keep an eye on processes throughout the day. Unlike infographics, which are usually static, dashboards may come with some built-in data analysis tools. They may allow the user to tap or click to delve more deeply into metrics or see different views of the same data visualization story. 

In a business setting, data visualization usually includes a wide variety of charts and graphs. These are embedded into dashboards and other process software, displayed on monitors or bulletin boards in common areas, or inserted into presentations for the boardroom. Some examples of the types of visuals common in data analytics and visualization are summarized below. 

Charts and Graphs

Charts and graphs are typically the first tools considered when discussing data visualization storytelling. A line chart can tell the overall story of data trends over time faster than a narrative or numbers, and a bar chart can let the viewer quickly compare counts or performance among different categories. But lines and bars aren’t the only graphical tools in the data analytics and BI reporting toolbox. Here are some other charts and graphs that are used in data visualization: 


Pictures may be worth a thousand words, but sometimes charts and graphs don’t quite do the job on their own when it comes to data storytelling. When you want to accompany your visuals with a more specific look at the data behind them, tables are typically the best way to display that information. That’s partly because everyone knows how to read tables’ column and row structures. 

You might include tables in your data visualization reports when you know stakeholders will want to see more granular information. You can also include them if you want to specifically point out how one piece of data is skewing the conclusions that might be drawn from a chart. 

When data analytics are embedded into software, they often come with options for viewing tables. For example, if someone can see a bar chart on their dashboard, they may be able to click on it to see the table and data behind it. This can be helpful if the bar chart seems concerning and the manager of the process would like additional information before acting. 


Maps are an ideal way to display data that are linked to location. Which states do you ship to most? Which neighborhoods have the oldest houses and thus might need certain types of services? Where in your facility are temperatures the coolest? These are all questions that might be best answered with map data. 

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Data visualization benefits

Data visualization comes with plenty of benefits. Not only that it can transform raw data into actionable insights that anyone can understand, but it could also speed up the decision-making process, identify patterns and trends, and ultimately boost your revenue.  

Here are our top benefits of data visualization:  

It lets us absorb large amounts of data at a glimpse of an eye

The human brain is programmed to think visually. It can process visuals 60,000 times faster than text. Moreover, our brains can effectively process an image in just about 13 milliseconds. Think of how powerful it is to see a graph, chart, or other visual representation of data. It is way easier for the brain to process the data that way instead of if you’re looking at a spreadsheet with rows of numbers. 

It speeds up the decision-making process

When your brain can process the data from a visual representation so fast, that means that you can also make a data-driven decision faster. According to the Wharton School of Business, data visualization can increase the ability to reach consensus quickly and move towards action by 21%.  

It easily shows relationships between operations and results

Finding a correlation between business operations and market performance is vital in the competitive space, so that’s why one of the main benefits of data visualization is that it allows users to track the connection between both and act accordingly whenever necessary. 

It can boost your revenue

Data visualization is all about finding the right information to help decision-makers make the right business decisions. With the help of real-time data visuals, you and your team will have the ability to perform advanced predictive analytics for different aspects of your business. For instance, you’ll have access to up-to-date sales data that can help determine marketing strategies or product popularity among target customers. 

Data visualization best practices

Understand the user first – The first thing that we want to do when creating a data visualization is to understand the user first, the person who is going to analyze your data story. Here are a few questions that you can ask yourself or your users to help you get started:  

Use the right chart type – Before you decide what chart type to use, ask yourself: What data story are you trying to tell with your visualization – do you want to compare data or show data distribution? Are you doing trend analysis or something else? After you have the answer to that question, you’ll easily choose the chart type that will best tell your data story.  

Proper use of color & text – Colors speak to us louder than words and communicate with us on an emotional level. We may not realize it most of the time, but on a subconscious level, every color triggers a different emotion in people. 62 – 90% of a first impression is based on how someone is recognizing color in the situation. So, the challenge here is to use the power of color effectively to communicate the message you want to send. 

Avoid chart junk – Edward Tufte, the father of data visualization, says, ‘’Above all else show the data’’. Don’t confuse your audience by adding unnecessary information or graphics such as background imagery, heavy grid lines, shading, etc. Always remember that the simplest way is usually the best way to show your data.  

Be clear with your data – Use available visualization features to ensure clarity in your data story.  

Highlight what’s important – Focus people on what’s important about the story that you’re trying to tell.  

Use effective interactions – Using effective interactions is also part of the data visualization best practices. Some of the things that users must have the ability to do include filtering data and linking dashboards and visualizations to others to give users deeper insights and drill down into the comprehensive analysis.  

Use 3D wisely – 3D visualizations have a place if you are doing surface analysis, volatility analysis, or doing terrain research. Avoid 3D for standard business use.  

Use the right level of detail – Don’t use excessive details that don’t contribute to the data story.  

Use the right scale – Avoid changing the scale of the Y-Axis, as this tends to tell a different story than the data should.  

Data visualization example in action

Data visualization can’t always stand alone

It’s important to realize that, as powerful as data visualization is, it’s not the only data analytics tool your company should be using (consider an analytics tool such as a self-service embedded analytics software). Visual representations of data can’t always stand alone, which is why so many business intelligence reporting tools and dashboard options let you drill down into deeper levels of the information. 

A prime example of when data visualization doesn’t stand well alone is when you’re trying to determine if something has a correlation. You can use a correlation chart, which plots two sets of data points in different colors. If the dots from both data sets hover along the same line, it can indicate a relationship. But there are some pretty hefty statistics at work behind this, and the visual doesn’t always give you enough detail to make a call on whether the two sets of data are statistically related. Plus, if you publish the visual alone, without any narrative explanation, there’s a chance that people unfamiliar with statistics will assume that cause and effect are at play. But correlation does not necessarily mean causation. 

Ultimately, visual data storytelling makes it easy for people throughout your organization to understand data more clearly. But you still probably need the right analytics experts or tools to steer the ship when it comes to complex data analysis and presentations.