By now, you must have already known that data visualization is a blend of information and data pooled from different data sources with rich visual representation identifying trends and patterns to arrive at comprehensive business decisions.
Sifting through a complex and vast amount of data is often time-consuming and tedious. Data visualization tools help organizations analyze any amount of data in no time to empower effective data-driven decision-making.
Advantages and benefits of good data visualization
The human eye has the power to consume visually rich graphics like graphs and charts when compared to a giant spreadsheet of data. According to science, we can quickly identify red from blue or square from a circle.
The art behind data visualization captures our interest and keeps our focus on key metrics. Graphical representation helps us identify trends and patterns quickly. It is like telling stories with a purpose.
Big data is here to stay, and it’s time to know what it enunciates.
As the “age of big data” enters full swing, visualization is an increasingly essential tool to extract a meaningful decision out of the trillions of rows of data generated each day. Data visualization helps tell stories by curating data in an easier-to-understand way, highlighting trends and outliers. Good visualization tells a story, removing data noise and highlighting helpful information.
Effective data visualization strikes a balance between form and function. However, it’s not as simple as embellishing a chart to make it look better or pasting an infographic’s “informational” part. The simplest graph might be too dull to grab the audience’s attention or get them to say anything meaningful; the stunning visualization could fail to convey the right message or say too much. Data and visuals need to work together, and there is some art to combining great analysis with great storytelling.
Why is data visualization important for any job?
It is hard to think of a professional sector that would not have a benefit from breaking down existing and historical data to extract meaningful patterns and decisions. Every industry and business department add their bit to gathering data and benefits from understanding data aligning with business vision.
While we’ll always talk poetically about data visualization, there are undeniably real, practical applications. Whether it’s a dashboard or a slide deck, the better you can convey your points visually, the better you can leverage that information. And, because visualization is so prolific, it’s also one of the most valuable career skills to develop.
Skill sets may vary from the data warehouse or data visualization tools to machine learning or AI, but the core of data science, which is the art of analyzing data to accommodate a data-driven world, remains intact. The concept of data scientists is at its peak. Professionals use data and use visuals to tell stories with data to report the why, what, how, and where of a business challenge. Formal education typically marks a distinguishable line between creative storytelling and technical analysis; the professional world values those who can interweave between the two: data visualization sits right at the heart of visual storytelling and analysis.
Few examples of data visualization in action.
With public data and data visualization galleries everywhere on the internet, it can be overwhelming to know where to start. We’ve covered the top 10 best data visualization examples of all time, with examples depicting historical conquests, analyzing movie scripts, revealing hidden causes of mortality, and more.
The Tableau Public Gallery shows tons of visualizations made using the free Tableau Public tool. We feature some common business dashboards as usable templates to get you started, and Visualization of the Day collects some of the best creations from the community. In addition, there are hundreds of great blogs and books on data visualization that contain great examples, explanations, and information on industry/domain knowledge.
The different types of visualizations
Ideally, when you think of data visualization, your first thought probably immediately goes to simple bar charts or pie charts. While this can be an integral part of data visualization and a common baseline for many data graphs, the correct visualization must be paired with the correct set of information. Simple graphics are just the tip of the iceberg. There is a selection of visualization methods to present data effectively and interestingly.
Common general types of data visualization:
More specific examples of methods for visualizing data:
- area chart
- Bar graphic
- Box and whisker plots
- bubble cloud
- Bullet chart
- circle view
- Point distribution map
- Gantt chart
- heat map
- highlight table
- polar area
- radial tree
- Scatterplot (2D or 3D)
- flow chart
- text tables
- Tree diagram
- stacked pie chart
- word cloud
- And any combination of all this that appears in a single user interface!
Want to create your own data visualization? Check this.
If you feel inspired or want to learn more, you can take advantage of many resources. Data journalism and experts are full of enthusiastic knowledge-sharing practitioners eager to share their tips, tricks, theories, and more.
Data visualization blogs are a perfect place to start.
Check out our list of great data visualization blogs packed with examples, inspiration, and educational resources.
Experts who write books and teach classes on the theory behind data visualization also tend to maintain blogs where they discuss the latest trends in the field and discuss emerging trends. Many will provide critiques of modern graphics or write tutorials for creating compelling visualizations.
Others will collect different data visualizations from across the web to highlight the best ones. Blogs and videos are an excellent source for learning more about specific subsets of data visualizations or finding inspiration for projects that are well done.
Learn about historical examples and book theory
While blogs can keep you updated with the changing trends of data visualization, books focus on where the theory remains consistent. Human beings have been trying to present data in visual form throughout our existence. One of the first books on data visualization, originally published in 1983, laid the groundwork for data visualization to come alive and remain relevant today.
Still, more current books deal with theory and techniques, offering timeless examples and practical advice. Some even take finished projects and present the visual graphics in book form as an archival exhibit.
There are tons of free courses and paid training programs.
Many great free and paid courses and resources on data visualization, including right here on the Tableau website. There are videos, articles, and reports for everyone, from beginners to data superstars. However, we will not provide specific third-party course suggestions in this article for the time being.
A Final Note
There are numerous tools for data visualization and analysis. They range from the simple to the complex, from the intuitive to the obtuse. Not all tools are suitable for everyone looking to learn visualization skills, and not all tools can scale for industry or business purposes. If you’d like more information about the options, feel free to read here or dig into detailed reviews from third parties, such as Gartner’s Magic Quadrant.
Data visualization isn’t going away anytime soon, so it’s essential to build a foundation of analysis, storytelling, and exploration that you can take with you regardless of what tools or software you use. Also, remember that good theory and data visualization skills will transcend specific tools and products. When learning this skill, focus on best practices and explore your style around visualizations and dashboards.