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Big Data: Definition, Benefits, and Use Cases

By Aditya Rana, Oct 16, 2023

big data: definition, benefits, use cases

What if there was a way to make better business decisions? A way to understand your customers better?

With 2.5 quintillion bytes of data generated each day, processing and analyzing data in real-time is critical to remain competitive.

But this is the realm of data scientists and big data management teams, right?

Wrong. Even though big data is wrapped in technical jargon like internet of things (iot), and big data tools and technologies can be confusing, the average business user is expected to handle large datasets and present it in an appealing format.

Hint: (Think infographics, charts, and diagrams!).

In this post, I’ll explain what big data is, what it does, and how you can use it to present insights in a way everyone can understand.

big data infographic

Click to jump ahead:

What is big data?

Big data refers to a large amount of data, structured, semi-structured, or unstructured, that can be mined for insights that drive better business decisions. These datasets hold a large volume of data that requires special hardware and software to process and store.

This is revolutionizing how companies tackle business problems.

For example, Amazon uses big data to generate product recommendations for its customers.

When a customer views a product on Amazon, the company uses purchase history and browsing behavior to recommend other products they might be interested in to improve user experience.

Note: Big data has been around since the 1990s, but it was not until the late 2000s and early 2010s that it really began to take off, much like how artificial intelligence or AI is today.

By 2027, the big data industry is expected to be worth $103 billion, growing 10% each year from now.

Big data is generated from a variety of sources such as:

  • Social media
  • Smart devices
  • Sensor data

For example, data collected on social media platforms like Facebook, Twitter, and Instagram such as demographics, content, and device details is stored in relational databases. In addition, leveraging data clean rooms offers a secure environment to combine and analyze social media data from multiple parties without compromising user privacy. This enables advanced analytics that wouldn’t be possible with siloed data.

You can also leverage the Customer Data Platform to drive business value from the customer data. To be more specific, this platform will assist you with data collection, unification, and activation.

This data helps with the product development process, but can also be sold to third-parties who use it for advertising and/or market research.

Big data can be separated into four categories or the four Vs; volume, variety, velocity, and veracity.

This definition of big data was popularized by Doug Laney, a research vice president at Gartner, in a 2001 report titled “3D Data Management: Controlling Data Volume, Velocity and Variety.”

The 4 Vs Of Big Data List Infographic Template
  • Volume

Big data sets can be petabytes or even exabytes in size (and this value keeps increasing).

This makes it difficult to process and analyze data with traditional tools. Instead, organizations need technologies like Hadoop or Spark.

  • Variety 

Variety refers to the range of data types in big data. For example, structured data includes database records, unstructured data covers text, images, video, and semi-structured data is usually JSON or XML. 

This variety makes it challenging to process and analyze information.

  • Velocity

Velocity refers to the speed at which big data is generated and processed. 

Since big data sets are generated in real time, companies require real-time data processing and analysis capabilities.

  • Veracity 

Veracity refers to the quality and accuracy of big data.

Since big data sets are noisy, reliable data is important because it impacts the quality of decisions made by a business.

Understanding types of data

Traditional big data concepts include terms like data lakes, and structured and unstructured data.

But this really just a fancier way to say qualitative and/or quantitative data.

Dark Visualizing Data in LMS Infographic

Qualitative data describes qualities, characteristics, or attributes, while quantitative data is numerical or measurable.

Qualitative data can be structured, semi-structured, or unstructured. For example, customer feedback in the form of text comments is unstructured qualitative data.

However, quantitative data is typically numerical-based structured data such as customer purchase data, product data, and financial transactions.

Both types of data can be used in data analysis.

Benefits of big data

Big data has many benefits that range from uncovering valuable insights, improving the decision-making process, streamlining business operations, and enhancing research and innovation. 

Big data projects span across sectors like healthcare, finance, and manufacturing, who all benefit from the insights derived from large datasets.

Let’s look at these characteristics of big data in detail:

Improved customer insights

Raw data generated in near real time covers customer interactions, purchasing behavior, and feedback.

One of the key benefits of big data is its ability to generate insights, particularly about customer behavior which can provide a basis for decisions.

For example, Netflix analyzes streaming data to understand customer viewing habits and recommends movies and TV shows people are likely to enjoy.

The result?

Netflix’s recommendation system is so effective that it accounts for over 80% of the content that people watch on the platform.

This also helps the company save over $1 billion per year on customer retention.

Better decision-making

Big data helps organizations base decisions on actionable insights rather than guesses.

For example, data analysts may find new markets and customer segments in data they may not have been aware of otherwise.

Ecommerce giant Amazon is a great example of how companies leverage big data to run highly targeted marketing campaigns.

Operational efficiency

Operational efficiency is the ability of a business to produce goods and services at the lowest possible cost while maintaining a high level of quality.

Businesses can use big data to improve their operational efficiency in a number of ways such as process optimization or inventory management. It’s crucial for them to also invest in robust systems to manage and secure sensitive data, ensuring that valuable information remains protected from potential threats and breaches. Turn on screen reader support

Walmart is a great example of a business that uses big data technologies to analyze customer purchase patterns and optimize inventory levels.

Reduce costs 

Cost reduction is one of the best use cases of big data for businesses.

By leveraging big data analytics, companies can identify areas where businesses are spending too much money.

For example, a business could use big data to identify which marketing campaigns are most effective and to eliminate those that are not.

The 4 segments of big data

Earlier I shared details about the four Vs of big data.

Now it’s time to understand how big data actually works. Generally, big data covers storage, processing, integration, and analysis and visualization.

There is no shortage of tools, including open source, to help organizations in each segment, but I’ll focus on the use case that matters to most people, visualization.

Data storage

Data storage is the foundation of big data. It covers the infrastructure required to manage and store large amounts of data.

Note: Data management is key to handling the sheer volume of data generated each day.

As volumes of data grow, organizations require storage solutions to meet data storage needs.

Big data storage solutions typically involve a distributed architecture, meaning that the data is spread across multiple servers or even multiple data centers.

This allows for better scalability and performance, as well as increased reliability.

Storage solutions include:

  • Google Cloud
  • AWS
  • Microsoft Azure

Data processing

Data processing is at the core of managing big data and plays a crucial role in risk management.

It involves the methods and technologies used to analyze and transform vast datasets. Efficient data processing is critical if you want to extract meaningful information in real-time.

Storage solutions include:

  • Google DataFlow
  • Amazon EWR
  • HD Insight

Data Integration

Data integration is the process of combining data from different sources into a single view.

This is a critical step in the big data process as data often comes from disparate sources and in a variety of formats.

Storage solutions include:

Data analysis and visualization

Big data analysis and visualization are integral components of handling big data.

Big data analytics involves extracting insights from big datasets, and often involves advanced analytics techniques to uncover hidden patterns and trends.

Machine learning models are commonly used in this process.

Meanwhile, data visualization refers to how you present the data.

Since most people prefer visual learning (65% of all people to be exact), you’ll need to visualize the data somehow.

This can be a challenge if you’re not a designer.

But Venngage makes it easy to create professional-looking data visualizations with a drag-and-drop editor. No design experience necessary!

We have a range of infographic templates, charts, and diagrams to choose from to help you visualize datasets.

Also, our editor supports accessible design! That means you can create visuals that everyone can understand, regardless of their abilities.

Simplify data visualization with Venngage

For most business users, analyzing big data means taking complex data and making it presentable.

The data visualization process can be a headache and/or time-consuming task.

Venngage provides a user-friendly platform for creating and sharing data visualizations. The tool offers pre-made and professionally-designed data visualization templates, such as infographics and charts.

To visualize your data using Venngage, here’s what you need to do.

Step 1: Sign up for Venngage (it’s free!)

Start by logging into your Venngage account or creating a new account if you don’t already have one.

This will give you access to all of the tools and resources you need to visualize your data.

venngage sign up

Step 2: Pick a template that fits the story your needs

Venngage offers infographic templates, chart templates, diagram templates, and more to meet diverse data visualization needs.

infographic templates

Step 3: Replace the content with your own

Like the template but not the data it presents? No problem! Just replace the data with your own using our drag-and-drop editor tool.

The Science Behind Natural Disasters: Earthquakes, Hurricanes, and Tornadoes

Step 4: Add, replace or remove visual aids like icons, illustrations or photos.

Changing and/or replacing icons is really simple too!

We offer free icons/illustrations and stock images, but with a paid plan you’ll have access to our full collection of 40,000+ icons/illustrations and 3+ million stock photos.

And if you sign up for a Business plan, you’ll also get access to My Brand Kit which makes it easy to add your visual branding to a design. In just a click, you can customize any template using your logo, brand color palette and fonts.

Step 5: Share your design

All Venngage designs can be shared for free.

If you want to download your visualization, you’ll need a premium plan or higher.

Big Data visualization examples

Now that you understand what big data is and why transforming large datasets into visuals is important, let’s look at some examples!

This infographic presents a broad overview of smartphone preferences based on a variety of factors. A visual overview like this can help with product development or just be a unique way to share information with your users.

Smartphone Statistical Infographic Template

This infographic presents multiple insights on leukemia in a visual storytelling format. This can help researchers figure out where to focus efforts; developing better diagnostic tools, treatment protocols, or personalized medicine approaches.

How Leukemia Affects The Body And Statistics

This infographic provides an overview of key statistics and information about domestic violence. Without presenting it in the format, spotting patterns and trends would be difficult with traditional methods alone.

Domestic Violence Facts and Statistics At A Glance

This infographic provides an overview of some of the most popular products purchased during Black Friday. Presenting information like this can help businesses make better decisions about what products to stock, how to price them, and how to market them.

Black Friday Sales Statistics

This infographic provides a comprehensive overview of food consumption patterns in different regions of the world.

Global food consumption patterns: a comparison of diets around the world

Want to see even more designs? Check out our statistical infographic templates or browse our infographic templates, charts, and diagrams.

Use cases of big data

Today, big data is being used in a range of industries such as healthcare, finance, retail, manufacturing, and more.


Healthcare organizations use big data to improve patient care, patient education, and develop new treatments.

For example, hospitals use big data to identify patients at risk of developing certain diseases or to develop personalized treatment plans.

Financial services

Financial institutions use big data to detect fraud, manage risk, and make better investment decisions. For example, banks can use big data to identify patterns of fraudulent activity or to predict market trends.


Retailers use big data to analyze customer purchase data, product data, and inventory data with eCommerce customer data platforms. This data can be used to improve product recommendations, optimize pricing, reduce fraud, and manage supply chains more effectively.


Manufacturers use big data to improve product quality, optimize production processes, and reduce costs. For example, manufacturers can use big data to identify patterns of product defects or to predict when machines are likely to fail.


Transportation companies use big data to optimize routes, reduce fuel consumption, and improve safety. For example, airlines can use big data to plan flight paths that avoid bad weather or to identify maintenance issues with aircraft.

Frequently Asked Questions

What is big data analytics?

Big data analytics refers to examining, processing, and deriving meaningful insights from large datasets to find patterns and trends that guide business decisions and outcomes. Generally, these large datasets contain massive volumes of structured, semi-structured, and unstructured data.

What is the difference between data and big data?

The main differences between data and big data lie in their volume, velocity, variety, and complexity. Generally, data refers to a set of information, facts, or observations that are collected and stored. It can vary in size, from small datasets that can be managed by common software tools. On the other hand, big data refers to massive volumes of data. It includes data on a scale that is too large to be managed and analyzed using traditional data processing tools. Big data can encompass petabytes, exabytes, or even larger volumes.

Where is big data used in real life?

Big data is used in various real-life applications across different industries. A few examples include; analyzing e-commerce purchasing behavior to provide personalized product recommendations; monitoring sensor data sensor data to predict equipment failures and conduct maintenance before breakdowns occur; tracking user behavior to send target ads to specific demographics and interests; and studying data to optimize delivery/shipping routes for efficient delivery and to minimize fuel consumption.

In Summary: Big data is no longer the future; it’s already here and requires you to take data visualization seriously

Don’t be fooled into thinking that big data is only for data scientists and tech teams.

Business users are now expected to handle large datasets, make sense of it, and be able to convert it into actionable insights.

While some aspects of big data do require technical expertise, data visualization is something that should be accessible to everyone.

Venngage helps you turn your big data strategy into a strategic asset that takes your business forward.

About Aditya Rana

Adi is a word slinger and loves to fiddle around with SaaS. When not living online, you'll find him outside in nature with a book.