The three V’s of big data have become the foundation of today’s data world. These characteristics emphasize particular challenges and opportunities that come with handling big data in a company. As data continues to increase and play an increasingly important part in our digital economy, it is necessary for all companies to fully understand these three V’s in order to extract insights from their data.
The three Vs (volume, velocity, and variety) are three distinguishing characteristics or dimensions of big data. Volume refers to the amount of data, velocity to the rate at which data is processed, and variety to the number of different types of data.
These properties represent the building block when creating the needed big data elements to have a homogeneous architecture that supports your business objective. To maximize the advantages of properly managing big data, you must also implement common guidelines and follow the latest trends.
Let’s get started reviewing these 3 characteristics and explore their implications for businesses.
3 V’s of Big Data with Example
The concept of three V’s was originally coined by Gartner to help us understand how different “big data” is to the traditional data procedure and highlight how organizations should be dealing with huge amount of data.
The 3 V’s of Big Data are:
The most important V in big data is volume which refers to the vast amount of data generated and collected from various sources, typically measured in petabytes, terabytes, or zettabytes. It requires specialized tools and technologies to manage and process, as traditional techniques are inadequate.
According to the latest estimates, 328.77 million terabytes of data are created each day. And this number of certainly going to increase as more and more technologies are being created. For instance, with the introduction of generative AI, we surely are going to see a huge spike in information creation as these technologies have the capabilities to help us write faster, create images and videos quickly, and so on.
The speed at which data is created, gathered, and processed is referred to as one of big data’s velocity characteristics. Depending on the system you’re attempting to create, some may demand real time responses, which calls for a greater degree of data velocity.
Additionally, in order to be able to give real-time insights in a volatile environment, you need specialist tools and systems that can manage the fast-moving data. Data processing efficiency, data transfer rates, and query performance are some of the measures used to quantify data velocity.
A prime illustration of velocity is the chat-like searches performed by search engines. These searches aim to deliver the most relevant information, even if it was created and posted a few hours ago on social media or as an article. Such searches demand significant computing and processing power.
Variety, the third V in big data, refers to the various types and formats of data your organization deals with frequently. Remember that big data also includes semi-structured and unstructured data, including email, videos, and social media posts, in addition to structured data that can be processed and understood by other systems.
Additionally, specific tools and technologies are required to manage the various data types, including data migration, integration, and transformation. To fully utilize unstructured data, advanced analytics methods like machine learning (ML) and natural language processing (NLP) are also necessary.
The variety of data in big data is measured in terms of the number of data types, data sources, and data formats involved.
Are there other V’s?
In order to provide a comprehensive approach to managing enterprise big data, data scientist experts are adding new V’s to the primary three V’s of big data as a result of technological advancement and the need to handle various types of data.
Veracity is a recent addition to the characteristics of big data, referring to the control of data quality and accuracy within an organization. It has become a crucial aspect of big data since the accuracy of business decisions depends on high-quality data. Without high quality data, incorrect decisions can have severe consequences.
The value is based on how many and what kind of insights have been extracted from the data. According to its particular business needs and operational procedures, each organization derives value from big data in a different way.