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Data Visualization: Tools and Examples for 2022

May 19, 2022
Digital environment flowchart with code behind it

In today’s business, data is king. But when billions and trillions of bits of data are collected, how can we even begin to understand it? And, more importantly, how is anyone supposed to know what to do with it?

From the big data boom, a new business necessity has emerged: data visualization. Ranging from bar graphs and pie charts to scatter plots and heat maps, data visualization tools help us physically see and understand the information our companies gather. Not only does it help us understand the past and present, but it also helps build a solid foundation from which we can make plans for the future.

A Clear Picture of Data Visualization

Much like its name describes, data visualization is the translation of data sets into visual tools to help people better understand their meaning. This can range from displaying data in tables, charts and graphs to word webs and maps. It’s extremely helpful for those who are unfamiliar with reading data in its raw form (spreadsheets, statistics, etc.), are unsure what the data means, or simply don’t have the time to gather, analyze and draw insights from data that they or their company collect.

There are two types of data visualization tools: exploratory and explanatory.1 Exploratory visualizations are best when you’re trying to unearth or answer questions that can’t necessarily be found in the data. For example, you may see that your company’s profit has slowly been decreasing over time. Using exploratory tools, you can start to investigate why they’ve been decreasing, how and by how much. Exploratory tools are also useful in considering what might happen in the future, such as projecting how profits might be affected if you moved your company to a different city.

Explanatory visualizations, on the other hand, tell a story or provide answers to something that’s already been demonstrated in the data or can lead you to a definite conclusion. A good time to use explanatory visualizations is when you’re summarizing past events (“Here’s how we performed financially in Q3”) or you’ve already explored different options and found your solution.

In modern business, the most popular data visualization tools are:2

  • Microsoft Power BI
  • Tableau
  • QlikView
  • Datawrapper
  • Plotly
  • Sisense

Why Is It So Important?

For most companies, gathering and organizing their data is just the first piece of the puzzle. Next, they need to analyze and display it, sometimes in its entirety, to share the insights with others. After all, with all of the time and money spent collecting data and no plan for what it means and what to do with it, it becomes useless.

Simply put, data visualization is a simpler, more interesting way to understand data. Visuals are easier to read, easier to understand and easier to disseminate to others. Instead of limiting the information to data scientists and architects, it makes all of the data universally comprehensible and appealing. If presented with a 7-page spreadsheet full of numbers or a clear-cut infographic, which would you prefer?

Rather than spitting out thousands of numbers, data visualization tools help to tell a story that creates takeaways for stakeholders. With all of the information (or the important pieces of it) gathered in one place, you can see patterns, make predictions and compare and contrast segments. It also creates a single source of truth for disparate teams, rather than each one working off of their own data sets and forming conclusions on their own. So, if a company’s financial department sees that profits have plateaued but the sales team is breaking records, the executives know to look into other areas for how to cut costs and improve profits.

How and When to Use Data Visualizations

The first step of data visualization is, of course, gathering the data. Many tools, like Microsoft Power BI and Tableau, will pull the data itself and let you choose portions or categories with which to work. You want to make sure the data is “clean” (i.e. free of errors and duplicates) before you move to visualization. Business and data analysts are familiar with this work and complete data “cleaning” regularly.

Once you have all of your data cleaned and organized, it’s time to show others what you’ve found. A good time to use data visualization is when you need to share reports with another team, provide an update, show progress or assess the current state of affairs, among other scenarios. Stakeholders don’t have the time (or often, the interest) to try to understand spreadsheets and calculations, so they’ll look to you for the high-level insights they can use to make business decisions.

If you can, it’s helpful to talk through the visuals in person rather than letting everyone interpret for themselves. This way, you can provide background, call out high-level findings, direct their attention to important details and elaborate on specific points. The example above of seeing profits vs. sales numbers is a great scenario of how another layer of context can be helpful in understanding the big picture.

The Most Effective Types of Data Visualizations

The types of visuals that will work best depend on your goal, but there are some general rules you should follow:

  • The simpler and more relevant the graph or visuals, the more engaging the presentation will be for your audience (e.g., bar graphs vs. box-and-whisker plots)
  • Humans tend to see horizontal dimensions better, so lean toward a horizontal bar graph or plotting map when you can1
  • Make sure everything is either printed or displayed in the correct dimensions with the appropriate labeling
  • Keep the colors simple and common. It’s even better if you can use your brand’s colors, but watch out for clashing, bright colors and unreadable fonts (e.g., hot pink and neon green, dark blue script on a dark green background)
  • Include a key that explains what’s being shown, what each color represents, etc.
  • Don’t overcrowd the pages. Allocate blank space and leave room for titles, labels and attributions
  • Unless your visualizations are extremely small, stick to one device per page

Big Data, Big Opportunities

Now that a majority of companies have recognized and adopted the undeniable value of data, they’re focused on hiring people who can help them make the most of it. The Bureau of Labor Statistics notes that data scientists are among the fastest growing occupations through 2030.3 This is because companies need employees who have the skills to work with and leverage data to better drive decision making, budgeting, resource allocation and, ultimately, profits.

If you’re unfamiliar with data visualization, consider earning a master’s degree or taking courses that focus on this skill. The Online MBA at Yeshiva University, for example, offers a specific data visualization elective that teaches students how to develop meaningful graphics and uncover insights that were previously unseen. Building your data-driven skills will equip you to overcome the challenges businesses encounter today, and those they’ll face tomorrow, making you an irreplaceable asset on their team.

  1. Retrieved on May 9, 2022, from sfmagazine.com/post-entry/december-2020-storytelling-with-data-visualization/
  2. Retrieved on May 10, 2022, from towardsdatascience.com/8-best-data-visualization-tools-that-every-data-scientist-should-know-2287c9c45cc4
  3. Retrieved on May 10, 2022, from bls.gov/emp/tables/fastest-growing-occupations.htm