A clear visualization of data helps people extract meaning from the data, and distill it into a clear message. Applying effective data visualization techniques and practices is especially important for companies that manage extremely large datasets. Read on to learn how to apply data visualization techniques to Big Data.

What Is Big Data?

The term Big Data refers to extremely large datasets with high velocity and variety, which require specific analysis methods. Big Data is too difficult to process using common database techniques, which often can’t provide the necessary processing capacity. The term Big Data also refers to the technology used to process massive datasets. 

Organizations collect data from a myriad of sources such as emails, Internet of Things devices, smartphones and applications. Big Data technology cleans and processes this data in order to extract actionable information for companies. A Big Data dataset can consist of petabytes (equivalent to 1,024 terabytes) or exabytes (equivalent to 1,024 petabytes).

What Is Data Visualization?

Data visualization is the representation of information in a visual way. It can be in the form of a chart, a diagram or a word cloud. Data visualization helps users easily make sense of the data. 
According to a study by the Social Science Research Network, nearly 65% of people are visual learners. Data visualization is a key tool for Business Intelligence (BI). Instead of sifting through rows of data in a spreadsheet, users can quickly understand the message behind it.

Why Is It Important to Visualize Big Data?

There are a number of benefits for Data Visualizations. Some of the advantages include:

• Getting critical insights—companies can get insights about processes or performance.
• Identify trends—visualizing the data in a graphic helps analysts to quickly detect patterns in it. This enables analysts to identify patterns, opportunities or threats. 
• Improve decision making—analyzing big data helps companies make data-driven decisions.

Use cases

Analyzing big data is useful for a number of cases. For example, following brand impact on social media by tracking customer’s comments and feedback. This can include Facebook comments or Instagram pictures featuring the company’s products.

Companies use sentiment analysis to understand people’s inclination for their brand. Sentiment analysis identifies and extracts subjective data from the web by monitoring online interactions. Marketers and data scientists analyze the collected data, applying data visualization techniques to present it in a graphic way. The visualization shows the pattern of feelings users express towards the brand. 

Companies also use Big Data visualization to detect patterns in consumer behavior. For example, e-stores can identify seasonal demands for a given product.

Data Visualization Types for Big Data

Some visualizations are better suited for big data. Below, you’ll find an overview of popular visualization methods.

Word Clouds

This type of visualization presents the words used in a text or network where the size of the word represents its frequency. You can use this technique to check for sentiment analysis. For example, the image below shows the consumer’s perception of Internet usage.

Symbol Maps

These visualizations represent geographically located data. The map allocates a value to each point, based on its range on the database table. For example, in the image below, the circles represent the percentage of approvals and rejections of the Washington Referendum 72.

Connectivity Charts

These charts show how phenomena or events are linked to each other. For instance, the image below shows how Twitter users that mentioned “Social CRM” are connected. This type of graphic can indicate trending searches.

Data Visualization Best Practices

Leveraging data visualization can help you make the most of your big data, effectively increasing your ROI. Below, you’ll find six best practices for effective data visualization.

1. Know your audience

Content should always meet the end-user’s level of technical knowledge. Present the graphic in an engaging and clear manner. What you to the general public should be markedly different than what you present to the scientific community. In the first case, the content should be clear, with the message displayed in an obvious way. For the latter, you can present a more detailed or interactive chart. Experts can then explore the data and do their own analysis. 

2. Determine your goals

What do you want to tell with the visualization? You should define what is the focus of the chart and what is the desired take away. There is a story behind every byte of data. You should define what story you want to present, and then create the visualization accordingly.  

3. Clean the data

You should ensure your data is clean of misconfigurations and duplicated records, before choosing the visualization type. There are a number of open source solutions that can help you refine the data, such as OpenRefine.

4. Choose the right visualization type

The visualization type you choose can present a clear or confusing message. When choosing the visualization, you should take into account the type of data and your purpose. For example, if you want to display a variable over time, a line chart can be the right option. If you want to present the distribution of wages per industry and decade, a scatter plot would be a good fit. 

Keep it lightweight

Big Data visualizations tend to be naturally heavy when displayed online. It is important to optimize the images to reduce the file size before uploading or sharing it. Nothing annoys a user more than waiting for the image to load. This also makes it easier to deliver visualizations across platforms and devices. 

Use color and layout to prioritize data

A contrasting color scheme can help you guide the attention of the user through the visualization. You can prioritize the data by color-coding. Arranging the layout to showcase the most important parts can create a clear hierarchy, which guides the user through the data. 

What’s Next?

Big Data Visualization is here to stay and the model is evolving, thanks to applications of artificial intelligence. For example, Visual Analytics. The term refers to the process in which a human queries the data in a system, and then receives the response in visual form. 

A good example of visual analytics is the open-source data visualization platform OpenCompass. It enables users to query terms to find the latest visualizations featuring technology trends.

 Advancements in machine learning also help improve big data visualization, enabling companies to create user-friendly data visualization tools. The aim is to use an interface that uses natural language, which enables non-technical people to create data visualizations.