What is Data Visualization?

Data visualization, a method of presenting data through charts, is everywhere. Governments have used charts to show various data points about the COVID-19 pandemic, while businesses use charts to show sales information, customer growth, and market sizes.

By using charts, data can be visible — accessible — to anyone, not just those who have analyzed and prepared that data. Charts are so useful for showcasing data that businesses and governments ask that data is put into charts after the data has been analyzed.

This has given rise to the field of “data visualization”. In this guide, we’re going to chat about what data visualization is, why it is useful, and some of the ways in which data is visualized.

What is Data Visualization?

Data visualization refers to any technique that makes insights from a dataset easy to view. A data analyst will look through a dataset and draw up charts that are relevant for the insights they have gleaned from their data. Data visualizations include bar charts, heat maps, maps of the world, line graphs, and any other chart that shows results from data.

Data visualization is usually the final stage of analysis. This stage happens after an analyst has drawn conclusions from the dataset with which they are working. If charts were drawn up before then, they would be unlikely to make much sense.

The charts drawn up at the end of analyzing a dataset may end up in reports, slideshows, or on interactive websites. For instance, the U.S. Census Bureau has charts that show how many people have graduated high school or attended further education between 2015 and 2019 (who are over the age of 25). This chart is much easier to analyse than a list of numbers.

Why is Data Visualization Important?

Data visualization is important because understanding data in a program — where most analysis goes on — is not accessible to everyone. While you can understand some of the data you see in a spreadsheet or the result of a programming script, you may struggle to find exactly the data you are looking for. Charts, however, present data clearly and plainly for anyone to see, even if they have not been involved in analysing the data.

Analysts can use data visualizations to draw attention to trends and important points they have identified. For instance, say a business is tracking the number of clicks they have received on their website. An analyst could draw a trend line on a graph to show the direction in which data is going.

When is Data Visualization Used?

Data visualization is not exclusive to careers in technology. Marketers, sales workers, managers, directors, payroll workers, data analysts, financiers, and accountants may all use data visualizations. These are only a few of the many jobs that involve data visualization.

Data visualization is about making raw data — data that is just numbers — into something that other people can read. A finance department may use data visualizations to show trends in sales for top products. Or a payroll department may show how salary costs are projected to increase over the next year, assuming a certain rate of hiring.

You can see data visualization used in a range of industries. We have compiled a list to show when some industries may use data analysis:

• Governments. A government may use data analysis to show economic statistics, results from a census, how taxes are spent, or public health statistics.
• E-commerce. These businesses may use charts to show the percentage of sales over a certain amount, how much money was earned over a period of time, and the impact of discounts on sales.
• Finance. People in the finance industry use charts to analyze financial assets (i.e. stocks), identify trends in financial assets, and, ultimately, make decisions about which assets to buy or sell.
• Energy. Energy companies may use charts to show how demand changes in their services at certain periods of the day.
• Insurance. Insurance providers may use charts to show the number of people who have a policy, money coming in through premiums, and payouts.

These are only a few of the many industries that use data analysis. If an industry collects data, it is almost certain that the data is presented in a visual form in some way. Making a diagram as simple as a bar chart is an example of data visualization.

Data Visualization Techniques

To prepare a visualization, an analyst first needs to consider the audience for the graph or chart they are going to create. Suppose a marketer was preparing a graph that showed how a recent marketing campaign boosted sales for a product. If that diagram was for the CEO of the company, it may need to be structured a bit differently than if it was for the marketing manager.

Once the audience for a graphic is considered, the person preparing the visualization must decide how the data will be presented. Common charts include:

• Bar charts
• Tables
• Maps of the world
• Heat maps
• Line graphs

The type of graph a person chooses depends on the nature of their data. Data for six months of revenue should be presented in a line chart or a bar chart. Data on how many customers have signed up to a certain tier of a website may be represented in a pie chart. Data showing the spread of customers around the U.S. may be displayed as a map of the country.

After a proper method of displaying data has been chosen, the person producing the visualization must make sure the chart is presentable. This involves asking questions like:

• Does the chart have a title?
• Do any sources need to be attributed?
• Do particular colors need to be used (i.e. to draw attention to a statistic, or to adhere with a company’s style guide)?
• Is the data definitely presented in the right way?
• Do any trend lines need to be drawn?
• Is there any data that is on the chart that does not need to be there?

Overall, the person producing a visualization must ask whether the final chart shows exactly what they want it to show. The ideal chart is clear, concise, and relevant. A chart that does not have a title and has data that has very little to do with the purpose of the chart is not helpful to anyone.

Tools for Data Visualization

There are plenty of tools used for data visualization. Some tools are designed to create charts for a specific purpose, like viewing financial data in a payroll app. Other tools, like spreadsheet apps, are more general, allowing you to create charts from any dataset.

Below are a handful of tools you can use for data visualization:

• Microsoft Excel. Excel has an extensive range of chart and visualization features. You can create bar charts, pie charts, line charts, and scatter plots. This tool is popular for data visualization because many people already use Excel for other purposes, such as analysing data.
• Tableau. Tableau was designed specifically for data visualization. This tool lets you create your own charts, visualizations, and dashboards which aggregate multiple visualizations. This tool is capable of handling very large datasets which is important for a number of visualization use cases.
• FusionCharts. FusionCharts lets you create charts, maps, and dashboards to display your data. You can also use their time-series charts to display stock information.

These are three of many tools used for data visualization. Some people who work with data will opt not to use a pre-built tool but rather a programming language, too. Python, R, and Julia are commonly used for processing data and creating visualizations. This approach is useful if a data analyst is producing charts from data that is already in a program.

Conclusion

Data visualization plays a key role in a range of occupations and industries. The goal of data visualization is to make numbers and statistics accessible to a particular audience, which is hard to achieve just by listing a set of numbers.

Data visualizations can take many forms: any graphic that represents data is a visualization. The most common ways to display data include pie charts, line charts, scatter plots, and maps. Many data visualizations are created with programs like Excel and Tableau although some charts are also generated using programming languages like Python.

Data visualization is used by businesses and governments around the world to make data accessible and readable. Without charts, many people would fail to draw the right conclusions from a dataset because not all data is easy to read or interpret.