Great data visualizations require a combination of analytics, design and communication. Master seven key skills for data visualizations to effectively communicate data insights.
Data visualizations can enable actionable insights and superior business outcomes. But building effective data visualizations can be difficult. If you want to create data visualizations, you must have a blend of skills that bridge the gap between data analytics and graphics design.
Data visualizations provide a powerful tool for turning raw data into concise and easy-to-understand concepts. They enable nontechnical audiences to grasp complex information so they can respond quickly to unexpected events, plan short- and long-term business strategies, and better understand the environments they operate in.
People in a variety of professions -- such as data scientists, data analysts, data stewards, BI consultants or specialists who focus exclusively on building visualizations -- create visualizations on a regular basis. Seven fundamental skills are important for anyone who wants to create data visualizations. Data scientists or graphics designers might be proficient in some areas, but it takes all seven skills to build effective visualizations.
7 key data visualization skills
You must develop foundational skills before focusing on more advanced ones. The priority of which skills you focus on depends on what you might already be proficient in and which skills you need to improve.
1. Statistical data analysis
You must be able to work with and understand the data that you use to build your visualizations. You need a strong grasp of statistics, data analytics and their underlying mathematical principles. You must understand the source data itself, including the types of data and their relationships, to discern meaningful patterns and trends. You should know how to conduct exploratory data analysis to gain insights into the data early in the process.
You must be able to access and work with the source data no matter where and how it's stored -- whether it comes from a relational database, NoSQL database, data warehouse, data lake, or set of text files or spreadsheets. You should know how to extract the information you need from the data, as well as cleanse and prepare the data to ensure that your visualizations are accurate and will lead to better outcomes.
To support data preparation efforts, you should know how to use analytical tools such as Microsoft Excel, data mining tools such as KNIME, programming languages such as Python, R or SQL, and any other tools your organization uses to work with data.
2. Storytelling
A data visualization is more than just a collection of graphics and text. It is a narrative that leads to actionable insights and data-driven decisions. Each visualization should show your audience why the data is important and what insights it reveals, using design elements to emphasize the message. Your audience should be able to connect the data points in a meaningful way, so they have the context they need to make informed decisions. To be an effective storyteller, you must have a solid foundation in data analysis because that informs your stories.
Storytelling requires that you present complex information clearly and concisely, transforming the data into visual elements that reveal patterns and trends that people might miss otherwise. For example, a visualization might include intersecting line charts that show how a change in an organization's marketing strategy in the first quarter led to a drop in sales throughout the rest of the year.
The visualization should connect the two events in a meaningful way, providing the audience with the insights they need to take action. Your visualizations should target the people they're meant for and include the elements needed to further the narrative progression.
3. Ability to use visualization tools
To excel at data visualization, you must become adept at using data visualization tools. If you're new to data visualization, you should prioritize this skill over storytelling and visual design so you have the foundation you need to progress with those other skills. You should be able to navigate around the software's interface, integrate the data you need for the visualization and know how to use the tool's features. You should also understand which tools to use in different situations. For example, Excel might be fine for modest efforts, but it lacks advanced capabilities found in other platforms.
You should be an expert in at least one or two of the leading products. Tableau and Microsoft Power BI are two of the more popular tools, along with Excel. Other major platforms include Klipfolio PowerMetrics, Qlik Cloud Analytics, Domo and Observable D3. In addition, you should be familiar with the tools you might need along with visualization tools, such as Adobe Creative Suite. And it's important to stay abreast of the latest visualization products and technologies and how they evolve and compare over time.
4. Visual design
Along with being a data analyst and storyteller, you also need graphic design skills. Design plays an integral role in creating effective visualizations. Good design helps you build more informative, intuitive and aesthetically pleasing visualizations. A well-designed visualization supports the narrative you want to convey.
When creating visualizations, your audience should always be the primary focus. You should make design choices based on your audience to ensure the visualization communicates the necessary information effectively. Designs must be clear and easy to understand, without overwhelming the audience with unnecessary or confusing images and information.
Practice basic design principles such as layout, color combinations, font selection, scale, and image and photo editing. You should also know how to apply branding, combine components effectively and use visual elements to stress the importance of specific information. It's important to communicate information without introducing clutter or confusion.
You must also know how to choose the best visuals for a specific situation. Visualizations offer a variety of options including bar graphs, line charts, bubble charts, scatter plots and histograms. You should fully understand how the visual types differ and when to use one over the other. Tailor your choice of specific chart types, the addition of animations and a careful mix of colors to your audience.
5. Collaboration/communication
A data visualization represents a form of communication that establishes a sense of collaboration between you and the audience. The more successful your visualization, the more successful your communication.
Your audience might include people with different backgrounds, skill levels and experiences. Understanding who the visualization is for and how to communicate data effectively based on their expertise can make or break your visualization. The visualization should communicate the most important details in a way that enables your audience to gain the insights they need to understand concepts and make critical business decisions, without bogging them down with unnecessary information.
Communication and design work together. Design skills communicate the necessary information, but you must also understand what to communicate before you can create an effective design. The right design can help you meet the needs of different types of viewers and ensure the best user experience for everyone. You might need to provide your viewers with information about the data sources you used or include other explanations. Communicating the information requires the same care and effort that you put into creating the visual elements.
6. Critical thinking
You must approach each data visualization project critically and methodically. You'll often face complex business concerns that don't have a simple answer. The source data might be just as complex, coming from different systems and stored in different formats. You must identify the scope and goal of your visualization project, determine what it will take to reach the goal and answer the tough business questions coming from your stakeholders.
Critical thinking pairs with strong problem-solving skills. Problems often come in the form of business questions, such as "why did our fourth-quarter sales drop and what can we do to turn sales around?" To solve such a problem, you should break it down into smaller components and analyze each component. Pull the pieces back together into a comprehensive visualization using a mix of storytelling, visual design and communication.
7. Feedback, evaluation, improvement
A visualization is not complete once it's presented to stakeholders. Feedback from your audience is one of your most valuable improvement tools. You need to know if stakeholders understand the information and if they have the details they need to make data-driven decisions. They should all be able to interpret a visualization in the same way, no matter their backgrounds or experiences. If they're not in agreement or can't make sense of the information, your visualization has a problem.
Rapid prototyping is a feedback strategy where you create multiple visual mockups that apply different design strategies. You can generate quick iterations of the various alternatives and ask for feedback based on the prototypes. The more adept you become at prototyping, the more effective your outcome and the better the results.
Once you've collected your feedback, evaluate the responses and make the appropriate changes. Feedback is the last skill on the list and you should prioritize the first six skills. If you don't have the skills to make a solid visualization, feedback won't provide much benefit.
How to develop skills for data visualization
If you're new to data visualizations or have little experience in creating them, you'll want ways to develop your foundational skills. Study, practice and feedback forms a core loop that should help your visualization skills development.
Study
Research information about how to create effective data visualizations from resources such as books, blogs, articles, tutorials and instructional videos. Research shouldn't end once you know how to make effective visualizations. One best practice is to study examples of effective visualizations. Attempt to understand the business questions they try to address and the stories they try to tell. It's also important to stay up to date on tools and trends within the industry. You might want to sign up for one or more newsletters to get automatic updates for new advancements. Check local libraries for books on your topics of interest.
It's difficult to improve your data visualizations if you aren't creating as many as possible.
Online courses can be an extremely valuable resource to hone your visualization skills. They can help you direct your learning and expose you to knowledge you might not find on your own. Courses can also help you target your efforts more effectively. For example, you might take a course on how to use a visualization tool such as Tableau, one that focuses on a specific aspect of visualization such as storytelling, or a course that's concerned with a certain area of technology, such as virtual reality or AI. It's also worth checking out classes that might be available in your area. You might discover affordable or even free classes from your local library, community college, community center or other institution.
Practice, practice, practice
It's difficult to improve your data visualizations if you aren't creating as many as possible. The more you practice, the better you'll become. Experiment with different types and sources of data and a variety of visual types. Build visualizations that answer a wide range of business questions, focusing on your storytelling skills when communicating your answers.
You should try out a wide range of tools, as well as related tools, such as those used for analytics and data mining. Also experiment with the visualization features built into applications such as Excel or PowerPoint. Try to master at least one or two of the major data visualization tools.
Seek out feedback and constructive criticism
As you build your visualization skills, seek out constructive feedback, especially if you can find people with extensive experience in creating visualizations. You can also benefit from collaborating with other professionals on visualization projects. Collaboration exposes you to new approaches and perspectives and provides immediate feedback on your designs. Networking with other professionals can also be useful when trying to build your skills.
Improving data visualization skills requires a combination of approaches to build a foundation. Remember that visualizing data demands multidisciplinary skills. Be careful not to focus too heavily on one discipline at the expense of the others. The more well-rounded your approach, the better the outcome.
Robert Sheldon is a freelance technology writer. He has written numerous books, articles and training materials on a wide range of topics, including big data, generative AI, 5D memory crystals, the dark web and the 11th dimension.
