What Is Data Visualization?
Data visualization is the process of turning raw data into images. Typically, those images are in the form of charts and graphs. The purpose of data visualization is to make data easier and faster to understand, even by people who are not trained in analytics or typically good with numbers.
Examples of Data Visualization
Infographics are an extremely common form of data visualization. Online marketers and content producers use these popular images to convey a lot of information quickly. 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 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:
- Box-and-whisker plots are a great way to quickly see whether there are outliers dragging a process up or down. They're also valuable when comparing averages, standard deviations, and means of a process — analysis factors that are helpful when answering questions such as "Are these two processes statistically the same?"
- Gantt charts provide a quick visualization of projects or processes across time. They're a great way to determine whether a complex schedule is realistic as planned, discover where one process may run into or overlap with another, and make predictions about (or see when) processes are running behind.
- Scatter or dot plots use dots to provide a visual indication of every data point being considered. These let you see potential trends, outliers, and groupings. For example, if you're tracking employee efficiency by day, each employee might be represented by different colored dots. That makes it easy to see if one employee is performing significantly above or below the pack. The plots also let you see if some other element, such as day of the week, impacts overall performance.
- Histograms show the distribution of your data. Histograms are statistical tools that help in drawing probability conclusions. But as a visualization storytelling tool, they can quickly demonstrate whether a process is hovering around the right mean or whether outliers are skewing results for data or outcomes.
- Pie charts are a quick way to illustrate what factors are at play in a process. Various versions of pie charts are good if you need to see whether each part of the whole is pulling its weight, or you want to see what factors are most important in a process or outcome.
- Control charts are specific types of line or dot charts that track the changes in a process over time. These are statistical process control tools that can provide information quickly about whether a process is "in control" or not. Control charts are often parts of analysis dashboards because they can be used to determine if a process is working as designed or if some manual intervention may be required to make an improvement or correct an issue.
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 the column and row structure of tables.
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's 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.
Why Is Data Visualization So Effective?
To someone not trained in big data analytics, a wall of numbers or the statistical speak that comes with a written correlation conclusion 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 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 numbers transition 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. At the store, colors tell you which discount applies to a particular product. It's natural to look for the patterns in colors, lines, and dots 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 Can Businesses Use Data Visualization?
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:
- To identify trends, such as whether sales are going down or certain processes are not as productive as they were
- To understand complex information quickly, such as when people view dashboards to conduct an overall process health check
- To identify patterns, such as whether the first Wednesday of the month always has a spiked call volume
- To identify relationships, such as whether the night production processes flounder whenever a certain person is in charge
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.
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. 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 correlation. You can use a correlation chart, which plots two sets of data points in different colors. If the dots from both sets of data hover along the same line, it can be an indication of 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.