Data visualization is the process of representing data using visual depictions like plots, charts, animations, or graphs. Using software such as Tableau, Data Analysts visualize data to impart complicated data insights and relationships in a manner that’s accessible to a range of audience members. If you are unsure that learning data visualization is right for you, consider reading on to find out more about the myriad uses of data visualization tools and techniques. Not only will learning data visualization open up a number of new career opportunities, but it also has many potential personal uses.

What is Data Visualization?

Data visualization uses graphics like plots, charts, or animations to represent data findings. These visual depictions communicate complicated data relationships in an accessible and engaging format to an organization’s internal and external stakeholders, even for those who don’t come from a technical background.

Data visualization has applications across industries and professions, from data analytics to business intelligence. The data visualization process is typically broken into four goals: to generate ideas, to illustrate concepts, to provide avenues into visual discovery, and to convey data in the form of visualizations.

Read more about what data visualization is and why you should learn it.

Professional Uses for Data Visualization

Data visualization is a sought-after skill set in a range of professions. Those with the training to visually convey data findings draw from critical thinking, problem-solving, technical training, and communication skills, allowing them to find key insights within datasets and share their findings with relevant stakeholders. Data visualization skills are in high demand in many professions, such as data visualization engineering, data analytics, data science, and business intelligence. The following sections will briefly explore some of the ways professionals in these fields rely on their data visualization training to perform professional tasks.

Clean Data

The data analytics and visualization process begins with data wrangling, also known as data cleaning. This early, important step requires preparing and validating the data. This process involves more than simply deleting erroneous data. It also pertains to the data professional deleting rouge data and working to correct it (if possible). Data may be considered rogue if it’s incorrectly formatted, inaccurate, incomplete, irrelevant, or corrupt in any other way. Data cleaning deduplicates data, which requires merging or deleting any identical data points.

The data cleaning process involves using various tools to correct any such errors that appear in a dataset. 

Those who perform data cleaning do so to ensure that the results of subsequent data analysis will be as accurate as possible. One of the main goals of the data cleaning process is to keep the majority of the dataset intact while at the same time eliminating any errors that would affect the integrity of the analytics process.

Analyze Data 

Once data has been gathered and cleaned, it can then be analyzed. Data analytics is the act of extracting relevant information from raw data so trends, patterns, and insights can be found within these numbers. The data analytics process draws from disciplines such as statistics, math, and computer programming. There are four primary types of data analysis: descriptive, diagnostic, predictive, and prescriptive. They can be performed separately or in concert with one another. Those with data analytics skills help their organization by providing a comprehensive overview of customers, marketing campaigns, product development, and other relevant factors. This information can ultimately be included in data visualizations and presented to relevant stakeholders to streamline operations, save time, and increase revenue.

Forecast Trends 

Trend forecasting involves creating a picture of a company’s future based on what has transpired in the past. It can be used to study customers’ purchasing preferences and habits so a business can create items more targeted to their needs. Long-term forecasting focuses on more macro trends, such as societal shifts that can impact customers’ purchasing patterns. Short-term forecasting, also known as seasonal forecasting, studies a specific timeframe to offer insights into customers’ expected purchasing habits for the upcoming months. Trend forecasting is essential for businesses in that it helps them avoid wasting money or resources on a product or service that may not be successful with their intended customer base. It also helps them account for fluctuations in market demand.

The process of forecasting trends involves first identifying past data trends. Then, data professionals seek patterns within the data to see how popular a previous service or item was. These patterns can be linear, exponential, or constant. The difference between prior forecasts and the actual behavior of customers is examined next so that any errors or aspects of consumer life that were overlooked can be addressed in subsequent analyses. A hypothesis can then be created, which may account for factors like operations, economic shifts, technological progress, or demographic fluctuations. A forecasting model is then generated based on the hypothesis, which can help data professionals visualize how long it may take for a trend to become adopted or how a target audience may respond to a new product.

Tell Data Stories

Once the data have been cleaned and analyzed, and a hypothesis has been made that forecasts data predictions, it’s time for the Data Analyst to bring their findings together in the form of a data story. A data story is a sort of narrative that’s built around data that situates it in the larger context and illustrates its broader implications. The data storytelling process involves leveraging data to shape a narrative. This story is then shared with an audience. Successful data stories draw from statistics, contextual and qualitative analysis, data visualization, and clear communication skills.

Although data stories are often assumed to be synonymous with data visualizations, visualizing data is just one component of a data story. Visualizations are useful in communicating different narrative points within a story. The larger story, though, is involved with specific expertise in a topic or industry. Successful data stories transform data analysis findings and the data visualizations that reflect them into a framework that tells the who, what, why, and how of a larger story.

Other Uses for Data Visualization

You don’t have to be a Senior Data Analyst to visualize data. Whether you wish to work with tools like Tableau or Excel to create data visualizations, the ability to depict data visually has a range of applications outside of professional tasks. The following sections will briefly examine some pragmatic uses for data visualization.

Build Your Own Creative Projects

You don’t have to be a Senior Data Analyst to visualize data. In fact, creating basic bar charts, infographics, or animations is a skill people of all ages can acquire. Tableau Public is a free platform where users can create and share their data visualizations with a large online community. This tool is an excellent resource for those who wish to get started with data visualization for their own creative projects or personal interests. 

Enhance Academic Projects

Students of all ages who complete school projects can use data visualization to enhance and support their research. You can effectively illustrate your argument by adding even a basic pie chart or bar graph created in Excel. Learning to tell data stories is useful at all stages of academia, from elementary to doctoral study. It provides an effective and engaging way to situate relevant findings or statistics into a format anyone can access and understand.

Improve Your Writing

If you want to improve your writing, you can use data visualization to do so. Wordle is a free tool that allows users to design stunning, customizable word clouds. You can paste any text into this tool, such as a cover letter, research article, or novel chapter, and select “Go.” A word cloud will be instantly created that indicates the words you use most often. This is a great resource if you worry you overuse certain words in your writing. 

Study Your Social Media Presence

In addition to professional, academic, and creative applications, data visualization can also be used to help you better understand and personalize your social media experience. TweepsMap allows users to analyze and visualize their Twitter network. It shows each of your followers and how they’re distributed on a world map. In addition, Twitonomy can provide you with visual analytics on anyone’s Twitter activity, such as their tweets, replies, and hashtags. It also allows you to monitor various interactions with other Twitter users and gain actionable insights into your Twitter followers.

Learn Data Visualization Skills with Noble Desktop

Noble Desktop has in-person and live online data visualization training. Tableau for Data Visualization Bootcamp instructs students on how to use Tableau Public’s visualization tools. Noble’s Data Analytics Certificate teaches core business intelligence, statistics, data analysis, and data visualization concepts. 

Aspiring Data Scientists can enroll in Noble’s Data Science Certificate to study machine learning, automation, SQL, and Python. The Python for Data Science Bootcamp provides instruction on how to use statistics to make machine learning models. Noble’s Excel Bootcamp teaches fundamental spreadsheet functions, including macros, formulas, and PivotTables. Those interested in working with SQL can enroll in Noble’s SQL Bootcamp.

Key Takeaways

• Data visualization is the act of representing information by creating visuals like charts, graphs, or maps using software like Tableau or Excel. These visual depictions are then presented to a range of non-technical audience members as data-driven stories.
• Professionals such as Data Analysts, Data Scientists, and Data Visualization Engineers commonly perform data tasks, such as cleansing, analyzing, forecasting, and storytelling, to convey data findings to a larger, non-technical audience.
• Data visualization isn’t only used in a professional setting; it has personal uses as well. Students can use basic data visualizations in the academic environment to enhance projects and reports. Tools like Wordle can visualize your writing to help you see which words you may be overusing. You can even visualize social media analytics to learn more about your followers’ demographics.
• If you want to learn how to visualize data, Noble Desktop currently offers comprehensive training in the in-person and live online formats.