Understanding the 5 Vs of big data: volume, velocity, variety, veracity & value

Big data is a field of data science that explores how different tools, methodologies and techniques can be used to analyze extremely large and complex data sets, break them down and systematically derive insights and information from them. But to truly get the grasp of how complex big data is, we need to understand the 5 Vs of big data!

Volume, velocity, variety, veracity and value are the five keys that enable big data to be a valuable business strategy. Let’s dig deeper into each of them!


Today, an extreme amount of data is produced every day. For example, in 2016 the total amount of data is estimated to be 6.2 exabytes and today, in 2020, we are closer to the number of 40000 exabytes of data. That is why we say that big data volume refers to the amount of data that is produced.

Data is generated by countless sources and in different formats (structured, unstructured and semi-structured). Due to its rapid production in extremely large sets, companies that want to incorporate big data into their business strategies are beginning to substitute traditional tools and methods used for business intelligence and analytics with custom software and systems that enable them to effectively gather, store, process and present all of that data in real-time.


Big data velocity refers to the high speed of accumulation of data.

The flow of data in today’s world is massive and continuous, and the speed at which data can be accessed directly impacts the decision-making process. The main goal is to gather, process and present data in as close to real-time as possible because even a smaller amount of real-time data can provide businesses with information and insights that will lead to better business results than large volumes of data that take a long time to be processed.


Big data variety refers to a class of data — it can be structured, semi-structured and unstructured.

  1. Structured data is data that is generally well organized and it can be easily analyzed by a machine or by humans — it has a defined length and format.
  2. Semi-structured data is a form that only partially conforms to the traditional data structure (e.g. log files) — it is a mix between structured and unstructured data and because of that some parts can be easily organized and analyzed, while other parts need a machine that will sort it out.
  3. Unstructured data is unorganized information that can be described as chaotic — almost 80% of all data is unstructured in nature (e.g. texts, pictures, videos, mobile data, etc).


Big data veracity refers to the assurance of the quality or credibility of the collected data.

Quality and accuracy are sometimes difficult to control when it comes to gathering big data. Since big data involves a multitude of data dimensions resulting from multiple data types and sources, there is a possibility that gathered data will come with some inconsistencies and uncertainties. That is why establishing the validity of data is a crucial step that needs to be conducted before data is to be processed.


Big data value refers to the usefulness of gathered data for your business.

Data by itself, regardless of its volume, usually isn’t very useful — to be valuable, it needs to be converted into insights or information, and that is where data processing steps in. By using custom processing software, you can derive useful insights from gathered data, and that can add value to your decision-making process.

If you want to read more about the value of data, we have an entire blog covering that topic.

What can you learn from this?

Data is incredibly important in today’s world as it can give you an insight into your consumers’ behavior and that can be of great value. Once you start processing your data and using the knowledge you gained from it, you will start making better decisions faster and start to locate opportunities and improve processes — which will eventually generate more sales and improve your customer satisfaction.