What Are the 5 Vs of Big Data?

Knowing how to analyze and interpret big data can provide you and your organization with competitive insights into customer behavior, patient risk, and stock forecasts. In fact, according to a Harvard Business Review survey, 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'll 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.

What is big data?

“Big data” refers to extremely large data sets that are too complex for professionals to analyze through traditional methods. As 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. When analyzed, big data can provide valuable insights into patterns, associations, behavior, and trends. 

Big data is a massive resource that’s growing every second. Fortunately, 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.

Hear more about the challenges facing big data in this lecture from Google Cloud's Google Cloud Big Data and Machine Learning Fundamentals course:

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

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.”

1. Volume

“Volume” refers to the high amount of data points in a big data set. Streaming services like Netflix or YouTube are great examples 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.

2. Veracity

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 you're trying to achieve. 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.

Read more: What Is Data Integrity?

3. Velocity

“Velocity” in big data refers to the speed of data generation and how fast professionals can 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, 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. 

4. Variety

“Variety” refers to the wide range of data types and sources in a big data set, including 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, audio, images, and social media posts. Semi-structured data 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 also 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.

5. Value

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.

Read more: What Is Big Data Analytics? Definition, Benefits, and More

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.

Build big data skills on Coursera

If you're interested in a career in big data, consider taking a course or specialization on Coursera to build the skills you'll need to enter the field.

In IBM's NoSQL, Big Data, and Spark Foundations Specialization, you'll develop foundational knowledge of Big Data and gain hands-on lab experience using Apache Hadoop, MapReduce, Apache Spark, Spark SQL, and Kubernetes.

Through Cloudera's Modern Big Data Analysis with SQL Specialization, you'll learn the fundamentals of Big Data and master using SQL for data analysis on distributed big data systems.