The 21st century is the age of big data. With the advent of high-tech data-gathering technology companies, everyone around the globe now has access to endless sources of information. Unfortunately, all that data is completely useless if you don’t know how to analyze it. Get to know the ins and outs of data visualization with these examples.
According to Wikipedia, data visualization is a method for representing data with graphics. These images range from pie charts and scattered data charts to creating infographics and videos.
Data visualization provides an effective means to interpret huge chunks of data and a way to present valuable information in a visually attractive form. An article published by t-sciences.com indicates that the human mind captures more information through audio-video stimulus than written words and data.
With today’s explosive growth in the machine learning and data science fields, aptly presenting statistics that are both aesthetically pleasing and informative remains the foundation of today’s data visualization processes. Just decades ago, analyzing information required immense resources. With the advent of today’s more advanced computing systems, data visualization has become faster, better, and easier.
Let’s review to most common types of data visualizations with these examples:
These data visualization approaches involve the use of time as an independent variable to visualize a trend. This graphic is often used to show changes in temperature, stock prices, and financial indices.
This charting approach involves the use of bars to make comparisons between several categories and parameters over time, similar to line graphs. However, bar charts best indicate significant changes over time.
Pie charts are used to represent parts of a whole. They are one of the most common data visualization approaches that you will come across and display data in the form of a percentage.
This approach involves using two or more variables to see the continuation/repetition of that variable. For example, this chart demonstrates the blood pressure status of patients.
The most common example we see in the news today is a geographic approach to data visualization. This image represents the number of coronavirus fatalities by state.
This data visualization approach involves using a meter to determine the direction of a trend or a social, political, or economic parameter.
Pivot tables are often used to condense large amounts of raw data to compile and compare the bottom-line results.
The bullet chart presents information consistent with a bar chart or histogram. However, the difference lies in the amount of information these visualizations present over the more rudimentary bar graph.
Matrix data visualizations are best used to make hundreds of comparisons between a variety of different parameters such as countries, export/imports, and consumer products on the same graph.
Contemporary data visualization programs advance with the speed of technology. Many platforms, such as Tableau, FusionCharts, and HubSpot, serve as effective tools. However, the efficacy of these software packages lies in the hands of its users and requires careful analysis and attention to detail to present your information.
Developers have released numerous data visualization programs that enable us to create charts, videos, and infographics. Now, we also see the emergence of augmented reality and virtual reality in the field of data visualization that has made it quite an intuitive and enjoyable mode of data communication.
It’s also important to mention that different data visualization software is meant for different tasks, so it is better to know what you are looking for or the nature of your business before opting for a particular software. Data visualization tools are mostly paid subscriptions, so you must make an informed decision. Different types of data visualization software are:
While there are many tools for data visualization, we will go through seven of the most popular. This ranking is based on analytics from third-party user reviews.
Companies use SAP Analytics Cloud for simplifying business analytics and making predictive analyses. Its integrated infrastructure allows the user to apply insights into the business process for assured engagement. It is rated at number one by Finances Online owing to its 100% customer satisfaction rating.
SAP Analytics’s cloud-based architecture enables users and companies to access information easily and at any time. Its collaborative workflow allows a senior executive to use relevant information despite their geographic dissimilarities. This data visualization platform is compatible with Macs, PCs, as well as web-based devices. Due to its lower price of $24 per month, SAP Analytics Cloud can be used by businesses of any size, from start-ups to Fortune 500 conglomerates.
Tableau is one of the best data visualization tools on the market, according to its 57,000-plus active users. It’s a business intelligence system that helps medium- and large-sized companies to visualize and analyze their data. The biggest attribute of Tableau is its intuitive interface and user-friendly programming features. It’s beneficial for analyzing data sets used in bid data operations, such as artificial intelligence and machine learning operations.
Tableau’s compatibility across several devices, such as PCs, Macs, and iPads, allows users to access information in both static and on-the-go situations. TrustRadius reviewers gave it a score of 8.3 out of 10 and top marks for its features that allow companies to visually display their stories behind its underlying data in a way that’s easy to understand. Tableau also enables you to connect to multiple data sources, like spreadsheets and cloud servers.
QlikView is another business intelligence data visualization tool that enables users to seamlessly guide the analytics app using data and insights. Due to its customizable features, QlikView requires a greater range of understanding and practice to utilize all of its robust features.
This program is frequently used alongside its sister package, QliKSense, which manages data exploration and discovery. QlikView can be used on a wider variety of electronic products and is suitable for any type of business, whether small, medium, or large.
Sisense is an analytics tool that allows users to visualize data easily using the drag and drop feature. In a dashboard comparison by Business Intelligence Market, Sisense ranked among the top picks for companies to put their data to work. All the information can be unified easily on your dashboards for presentation to management and team members. The “REST API” feature of Sisense allows you to integrate it with other applications quickly.
Zoho Analytics turns your data into intuitive visualizations, providing valuable insights to check the strength of your business’s marketing efforts. Programmers recently upgraded it into robust self-service business intelligence, data analytics, and an online reporting platform.
Its drag-and-drop feature provides an effective means of condensing your data into reasonable chunks, and it’s easier for non-technical users to fish out the information they need quickly. The interoperability of Zoho analytics allows users to gain required information from the office or on the road with cloud-based solutions.
This powerful data visualization software is used primarily for database management and consolidation. Its ability to integrate with other database software allows users to streamline their workflows and mitigate errors. It can be operated on multiple platforms and is intuitive for a wide range of users, from freelancers to big corporations.
For the best data visualizations, consider incorporating these five proven data visualization practices:
Knowing your audience is the first step to presenting data visualization. The who and the what of your audience are the keys to this step. Keep your audience in mind. Whether they are corporate executives, government officials, or shoppers, knowing your readers will help you craft a targeted message. Consider what action you want the audience to take by empowering them with the information on your fact-based data visualization.
It’s important you select the right visual style for your data and your audience. For financial data analysis and visualization, a line graph presents the information clearly and concisely. However, if you’re looking for relationships, a scatter plot may be more appropriate.
The contextualization of data provides stakeholders with the background of the data. It suggests to the audience how the current metrics stand out against the threshold. The image below provides an excellent example of contextual visualization of data to give people a better understanding of the whole picture.
Simplicity is the utmost sophistication. This holds true not only in social life but in the realm of data visualization as well. Following the principle of simplified data, visualization allows your audience to instantly comprehend large amounts of information, effortlessly answer questions, and to further strategize effectively.
The main purpose of data visualization is to keep stakeholders engaged with your data without involving them in the complex steps you took to calculate the information within your graphic. A good subject-based data visualization having the maximum amount of information but fewer complexities to understand will help enhance user experience with data sets.
Graphics are a direct result of the user’s expertise. Depending on their skills, the final product can either be good or bad. Let’s review examples of good data visualization projects, such as:
Transparency International published this simple yet informative data visualization example. In this small presentation, you can visualize a region’s geographic presence, and the orange locations present the factors that are being analyzed. In addition, the use of icons at the bottom specifies which parameter is being discussed in the image. With this sleek and minimalist data visualization model, the audience can easily identify the region with the highest corruption levels.
The following chart represents the political landscape of the United States and how different types of mainstream media channels help shape the politics in America. Pew Research Center’s data visualizations show that people who watch Fox News and shows like “Sean Hannity” or “Friends with Fox” tend to fall on the conservative/right-wing of the political spectrum. Others who watch CNN, ABC News, New York Times (NYT) tend to fall on the liberal/left side.
With this static data visualization, the audience gets to digest important but self-evident information. This can also help those people who don’t know which side of the political spectrum they fall into to learn about U.S politics. The graph is an effective means of letting people understand their ideologies, political affiliations, and realize how their personal ideologies fit in with those of media news channels they watch.
This beautiful gold-colored Starbucks outlet in Milan is something entirely out of a sci-fi movie. The popular coffee company commissioned Accurat to build the wall in 2018, and in 2019, it won a Gold Kantar award for the unusual category. If you point your smartphone camera towards the wall, it will display the entire history of Starbucks in a web of data layers. These data visualizations add both art and augmented reality to any Italian’s morning routine.
National Geographic created this stunning visualization using Tableau. It represents the distribution of bioluminescence on the Eastern coast of Australia. The depiction is not only aesthetically pleasing, but it also provides a great deal of information. The map legend and other details present additional information to the audience.
Federica Fragapane created this visualization of space junk for the BBC. The graph clearly shows the distribution of waste in three categories:
The data visual in this image is meticulous, informative, pleasing to the eye, and easily attracts the reader’s attention. Through the master of her skills, Federica has provided a very beautiful visualization of a pervasive problem for humanity.
Thanksgiving is one of the most commonly celebrated annual holidays in America with 54.3 million people traveling across the country to spend time with their loved ones. In 2015, around 46.3 million people traveled far and wide for Thanksgiving.
Google used this opportunity to create one of the best collaborative data visualization examples. Using only time as flight metrics, viewers can see which portions of the day are the most popular for different flights. Powered by Google Trends, this visually impressive piece of interactive content offers a view of the flights moving to, from, and across the country.
This striking piece represents the widespread use of plastic worldwide. This data visually represents the massive build-up of plastic in New York City along the banks of the East River. It’s an effective means of representing pollution crises that severely impact our ecosystem. Climate awareness organizations commonly use these visuals to inform communities and world leaders.
Not every data visualization is an effective tool. Whether you try to include too much information or too many colors or graphics, it could distract the reader. Avoid certain poor examples, such as:
While presenting something in a three-dimensional state may seem engaging, that’s not always the case. These types of visuals are often difficult to read and comprehend. That’s one of the primary reasons why most engineering blueprints are created in two-dimensional space. Nor do 3D graphics communicate information in the way they are intended.
While it may also be tempting to add a variety of colors to your graphic, excessive use of color can negatively affect your audience. Color is a powerful psychological tool. You don’t want to clutter your data visuals with this approach. In addition, too many words and letters may also impair the user’s experience. This example may make your readers feel like you are bombarding them with information that instantly turns your readers away from your message.
No matter what line of business you’re in, data visualization allows you to present information in the most effective way possible. As one of the vital steps in the business intelligence practice, this process:
Data visualization augments decision-making in so many ways that it becomes difficult to comprehend its limits. Some of its additional benefits include:
Most individuals believe that things follow a pattern, whether they are in finance, technology, business, or education. With terabytes of data available for processing from both open and paid sources, data visualization allows corporate executives to make informed decisions on investment, corporate mergers, acquisitions, and investing in new technologies. All these are data visualization results created through careful combing of information using AI-powered tools.
Data visualizations allow investors and businesses to make co-relations among different independent market parameters. This enables businesses to make better choices, take calculated risks, and visualize multiple scenarios in future trends based on historical information.
Does a particular stock increase in value over time? If so, what is the frequency of this increase? How frequently does the state of California face environmental catastrophe? All these questions are defined in terms of frequency.
By examining the data regarding several issues, one can calculate how many times a particular trend continues to happen. This, in turn, helps stakeholders to make the necessary preparations for a particular event. In case of an earthquake, data visualization will help a disaster relief agency put emergency services measures and precautions in place.
While combing through such huge chunks of raw data may seem counter-intuitive to most of us, it helps businesses to strategize effectively and efficiently. In the age of IoT and blockchains, huge quantities of data are being created, which, unlike in the past, were considered a byproduct. Now, corporations use this metadata to add value to their services, increase the quality of their products, and gain significant insights into new and emerging trends.
With so many data visualization samples at your disposal, the process has become much more streamlined and easier to understand than ever before. With the help of this handy guide and a little training, you can teach your team to create the best data visualizations in the marketplace.
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