What Are the 5 Vs of Big Data?

Written by Coursera Staff • Updated on

Explore what big data is, including defining characteristics of big data and why each one is important.

[Featured Image] Two data scientists discuss the 5 Vs of big data.

Knowing how to analyze and interpret big data can provide you and your organization with competitive insights such as customer behavior, patient risk, and stock forecasts. Data has become so important for organizational insights that 91 percent of global executives say effective data analytics strategies are key for business growth and transformation [1].

In this article, we will explore big data, including the “five Vs” of big data and what each means. Discover how you can learn fundamental big data concepts and apply them to your field.

Read more: What Is Big Data? A Layperson's Guide

What is big data?

The term “big data” refers to extremely large data sets that are too complex for professionals to analyze through traditional methods. As our technology advances, big data continues to be an important source of information and analytical insights across professional fields. Data is constantly generated through wearable sensors, smart devices, genomic technologies, and more. Big data can provide valuable insights into patterns, associations, behavior, and trends when analyzed. 

Big data is a massive resource that’s growing every second. However, artificial intelligence (AI) models are useful tools for generating insights from big data. For example, AI algorithms can read ongoing information, such as social media activity, product reviews, and consumer behavior metrics, to create data-driven insights that help inform business decisions. In the health care space, AI can take electronic health care records and patient metrics to identify at-risk patients as targets for earlier interventions.

The 5 Vs of big data

Understanding the qualities of big data can help you find the right tools for analysis and interpretation. In general, you can characterize big data by the “five Vs.”

Read more: Big Data Examples: 6 Ways Big Data Can Change Your Business


“Volume” refers to the high amount of data points in big data. Streaming services like Netflix or YouTube are a great example of this. These platforms cater to millions of users who stream videos, which generates an enormous amount of data. Netflix not only has to store this colossal volume of streaming data but also user preferences, search histories, and interactions. 

The volume of data generated helps Netflix use sophisticated algorithms to recommend shows and movies, leading to a more personalized user experience. While the large volume of data generated leads to more targeted consumer recommendations, analyzing and managing this information requires advanced storage and processing capabilities.


The term “Veracity” refers to the trustworthiness and quality of the data. With such a high volume of data generated daily, it remains a challenge to ensure the data you work with is unbiased and correctly represents what it’s supposed to. In this case, verifying and validating your data at each step of the collection and analysis process is imperative. 

Depending on the nature of the data, missing values, noise, model approximation, ambiguity, and bias can influence the veracity of the data. The acceptable veracity of the data will depend on what type of data you have and what your purpose is. For example, when it comes to medical data, the veracity of the data acceptable for research purposes is much different than the veracity for clinician decisions.


The “Velocity” aspect of Big Data represents the speed of data generation and the speed at which professionals collect and process it. This varies depending on the data source. For example, millions of posts are sent out daily on social media sites like X, while wearables like Apple Watches collect health metrics continuously. 

However, the velocity is not just about the rapid arrival rate. In many fields, professionals make snap decisions as the data arrives. For example, financial institutions trading in the stock market use high-velocity data to make split-second decisions that could involve millions of dollars. 


“Variety” represents a wide range of data types and sources in big data. This variety includes structured and unstructured data. Structured data includes more defined data types, such as databases of names and numbers. Unstructured data, on the other hand, includes data types like text, sounds, images, and social media posts. Semi-structured is a mix of the two. For example, in health care, patient data may include structured records such as age, diagnosis, and treatment history and unstructured data such as medical notes, health images, and even genetic information.

When dealing with variety in big data, you must aggregate and analyze your data to preserve its meaning while driving more impactful insights. This requires you to employ complex processing techniques and advanced analytics. While single data points may have biases, a benefit of the variety in big data is that you get multiple reference points on the topic of interest to build a better picture.


The “Value” of big data comes from the insights and patterns you can find. Because big data incorporates data from diverse sources and formats, you can build insights into metrics of interest, such as customer behavior, how the market moves, business performance, and more. For instance, while structured data can reveal numerical trends and patterns, unstructured text data from social media posts or customer reviews can uncover sentiments and opinions that drive human behavior.

How to begin learning big data

You can learn how to work with big data by becoming familiar with data types, common data analytics strategies, and tools to analyze complex data sets. Some steps you can take to start include:

Understand data basics. Start with the fundamentals. Familiarize yourself with key concepts related to big data, such as what it is, why it matters, and its various dimensions, such as the five Vs. Books, online articles, and introductory courses can provide a solid foundation.

Learn about big data tools. You can explore common technologies and tools used in big data environments. Focus on understanding databases (SQL and NoSQL), data storage techniques, data processing frameworks like Hadoop and Spark, and cloud technologies, including AWS, Azure, and Google Cloud.

Take an online course. Enroll in online courses and certifications specifically tailored to big data. You can explore various classes and programs on platforms like Coursera that cover different aspects of big data, from introductory to advanced levels.

Learn more with Coursera.

You can begin learning about big data with online courses on Coursera. If you are just starting, courses such as Introduction to Big Data by the University of California San Diego can provide you with a solid foundation to understand what big data is and how you can take your next steps to apply it in your field. Upon completion, gain a shareable Professional Certificate to include in your resume, CV, or LinkedIn profile.

Article sources

  1. Harvard Business Review. “UNDERSTANDING WHY ANALYTICS STRATEGIES FALL SHORT FOR SOME, BUT NOT FOR OTHERS, https://clouddamcdnprodep.azureedge.net/gdc/gdc6hMYUV/original.” Accessed March 21, 2024.

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