WHAT IS DATA VISUALIZATION: Techniques, Tools, and Importance

what is data visualization
Image Source: IBM

Data visualization is used for demonstrating performance, communicating trends, understanding the impact of new strategies, showing relationships, and beyond. These representations can be powerful tools for communication and collaboration, bringing greater value to reports, journalism, apps, or any context where sharing information is required. In essence, data visualization concepts are the method of translating information into a visual context, such as a map or graph to make data easier for the human brain to comprehend and draw understandings from. Stick around to learn about the example of data visualization and its types.


Data visualization is the method of designing and creating an easy-to-communicate graphic or visual presentation of a large amount of complicated data and information from a certain specialization of expertise with the help of fixed, active, or interactive visual items for the target audience to help them visually explore and discover,  understand, analyze, and gain insights into global patterns, trends, variations, constancy, and clusters.

Further, data visualization provides a quick and effective way to communicate information and identify which factors affect customer behavior, and point out areas that need improvement or attention. Hence, making data more memorable for stakeholders to understand when and where to place specific products, and predict sales volumes. Let’s now take a detailed look at the concept of data visualization,

Data Visualization Concepts

With so many data visualization tools readily available, there has also been a rise in unproductive information visualization. Visual communication should be simple and purposeful to ensure your data visualization concepts helps the target audience understand your intended insight or conclusion. The following is one of the most overlooked yet vital data visualization concepts around,

#1. Set the Context

It’s important to provide general background information to ground the audience around why this particular data point is important. Hence, to drive action, the audience needs to understand how current performance compares to something tangible, like a goal, benchmark, or other key performance indicators (KPIs).

#2. Know Your Audience(s)

Think about how the concepts of your data visualization are designed to make sure your data presentation fits their needs by asking these questions What is that person trying to accomplish? What kind of questions do they care about? Does your visualization address their concerns? but if you’re unsure if the visualization is clear, present it to one or two people within your target audience to get feedback, hence allowing you to make additional edits before a large presentation.

#3. Choose the Right Chart Type

One of the most effective data visualization concepts on our list is to succeed in presenting your data effectively, but you must select the right charts for your specific project, audience, and purpose.

#4. Choose an Effective Graphic

The principles of color theory have a significant impact on the overall success of your visualization model. That said, you should always try to keep your color scheme consistent throughout your data visualizations, using clear contrasts to distinguish between elements (e.g. positive trends in green and negative trends in red). Also, ensure that the visuals assist the audience in understanding your main takeaway. 

#5. Keep it Simple

Data visualization tools can make it easy to include all sorts of information in your visual. Hence, design your data visualization example for maximum impact by eliminating information that may distract your target audience.

Why is data visualization important?

It is important because it provides a quick and effective way to communicate information in a versatile manner using visual information. Additionally, the practice also helps businesses identify which factors affect customer behavior and areas that need to be improved.

Data visualization Example

By using data visualization tools, different examples of charts and graphs can be created to illustrate important data. Below are a few examples of data visualization:

#1. Area Map

An example of geospatial data visualization is area maps used to show specific values set over a map of a country, state, county, or any other geographic location. Two common types of area maps are choropleths and isopleths. 

#2. Bar Chart 

Bar charts represent numerical values compared to each other. The length of the bar represents the value of each variable. 

#3. Gantt Chart

Gantt is typically used in project management, Gantt charts are bar chart portrayals of timelines and tasks.

#4. Heat Map

 A type of geospatial visualization in map form that displays specific data values as different colors (this doesn’t need to be temperatures, but that is a common use). These graphical representation displays help visualize behavioral data by location. This can be a location on a map or even a webpage.

#5. Highlight Table

 A form of table that uses color to categorize similar data, allowing the viewer to read it more easily and intuitively. 

#6. Histogram

This graph plots a distribution of numbers using a bar chart (with no spaces between the bars), representing the quantity of data that falls within a particular range. This visual makes it easy for an end user to identify outliers within a given dataset.

#7. Treemap

This is a type of chart that shows different, related values in the form of rectangles nested together. which display hierarchical data as a set of nested shapes, typically rectangles. Treemaps are great for comparing the proportions between categories via their area size.

Types of Data Visualization

A data visualization tool is a form of software that’s designed to visualize data. Each tool’s capabilities vary but, at their most basic, they allow you to input a dataset and visually manipulate it. Most, but not all, come with built-in templates you can use to generate basic visualizations. There are several types of data visualization tools out there to suit your needs to choose from,

#1. Table

A table is one of the simplest examples of visualization. It consists of columns and rows and arranges the data into a neat structure. Tables show a great deal of information in a structured way, but they can also overpower users that are simply looking for high-level trends.

#2. Word cloud

A word cloud is a cluster of terms displayed in varying sizes and formats depending on their frequency or relevance in a set. Businesses often use word clouds to visualize audience feedback and public opinion or to brainstorm new ideas.

#3. Gantt chart

A Gantt chart is a variation on a bar chart that shows activities and the time required for each. Project managers commonly use Gantt charts to create comprehensive schedules of tasks.

#4. Matrix diagram

A matrix diagram compares multiple elements and allows the viewer to understand the complex relationships between them. It’s a versatile type of data visualization that can help identify causes, connect requirements and specifications, compare elements, and determine resource needs. 

#5. Heat map

A heat map visualizes behavior-related data, usually within a geographical area or concerning web page activity. Colors represent the degrees of activity or exposure, usually with deeper colors indicating the highest concentration or frequency.

#6. Google Charts

Google Chart Tools allow you to visualize live data on your website (and mobile) with the help of several interactive, customizable charts and data tools. The most common way to use Google Charts is with simple JavaScript that you embed in your web page. And by using the DataTable class, you can easily switch between chart types

#7. Tableau

Tableau is one of the most popular examples of data visualization tools on the market for two main reasons: It’s relatively easy to use and incredibly powerful. The software can integrate with hundreds of sources to import data and output dozens of visualization types—from charts to maps and more. Tableau boasts millions of users and community members, and it’s widely used at the enterprise level.

#8. Chartio

Chartio’s data visualization tools offer users 15 types of charts to choose from with multiple variations, and even more options if you know how to use data programming languages.

This implies, that with Chartio, you can bring all your data together from locations like Amazon Redshift, browse your data with the Visual SQL solution, create and manage customized charts and visuals, and easily share them (via a web page, Slack, PDF reports for email, etc.).

#4. Zoho Analytics

Zoho analytics is a data visualization tool specifically designed for professionals looking to visualize business intelligence. As such, it’s most commonly used to visualize information related to sales, marketing, profit, revenues, costs, and pipelines with user-friendly dashboards. More than 500,000 businesses and two million users currently leverage the software.

#5. Datawrapper

Datawrapper is a tool that, like Google Charts, is used to generate charts, maps, and other graphics for use online. The tool’s original intended audience was reporters working on news stories, but any professional responsible for managing a website can find value in it 

#6. Infogram

What sets Infogram apart from the other tools on this list is that you can use it to create infographics (where its name comes from), making it especially popular among creative professionals. Additionally, the tool includes a drag-and-drop editor, which can be helpful for beginners.

#7. Dashboards

Dashboards can bring together multiple types of data visualizations into one interface. They enable anyone to track, analyze, and display data insights, and they can be customized to meet the needs and data governance requirements of individual organizations, departments, and users.

How to choose the right data visualization tool

Apply these tips to help you select the best visualization tool for your data set:

#1. Evaluate the Size and Variety of Your Data Set 

If you have a simple data set with few variables or items to organize, you might consider simpler options like line graphs, bar charts, and pie charts.

#2. Choose the Central Concepts You Want to Emphasize

 Use tools that highlight the right elements, like growth over time or customer preferences.

#3. Find out How Your Audience Sees Visual Information

Assessing the features of your audience can help you decide which types of visualization are appropriate. For example, public audiences may be more accustomed to simpler visualizations, whereas colleagues may be familiar with a broader range of types.

#4. Opt For the Simplest Visual

Once you determine who your audience is and what you want to say with your data, try charting your data set with every choice of visualization you’ve deemed viable. Then examine the visualizations you’ve made and select the one that’s easiest to understand.

#5. Know the Emotional Impact You Want the Audience to Have

Some visualizations share the same message with equal clarity, but they may have different emotional impacts. For example, if you want to convey the excitement of financial growth, you might use a line graph that continues charting upward instead of a bar chart.

#6. Obtain Feedback on Your Visualizations

Ask a colleague to review the visualizations you’re testing. Assess how clearly they understand each option and whether they have the emotional reaction you’re hoping to evoke.

In conclusion:

Bringing visual clarity to the story told within your data helps you identify insights that lead to better decision-making, planning, strategies, and actions. How is your business performing, what needs to be modified, and where should you focus your resources? This leads to the ability to understand the significance of your data to drive more effective operations.


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