The 5 V’s of Big Data

Everyone talks about Big Data, but no one knows what it’s like and what are its essential elements. It is necessary to study its characteristics and definition in detail to understand the transition of ‘data’ into ‘big data’ and its impact on businesses. The characteristics are just words that explain the notable potential of Big Data.

In recent times, 3Vs were used to define Big Data based on the 3-V model from the big data expert at Gartner. While the model is accurate and vital, now two crucial factors need to be added. So, now there are ‘5Vs’ of Big Data, also called characteristics of Big Data, and they are as follows- Volume, Variety, Veracity, Value, and Velocity. This zenith of Software Engineering is designed to handle the enormous data being generated every second, and the 5 Vs we discuss here are interconnected.

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What is Big data?

Big Data is a new branch of data science. It refers to the massive volume of data not capable of being stored or processed by any traditional data storage device or processing unit. The field of technology explores how this data can be broken down and analyzed to gain insights and extract information. The conventional data processing solutions are less efficient in capturing, analyzing, and storing big data. Thus, the value of big data isn’t fully utilized because of traditional Business Intelligence solutions. Many multinational companies are now using Big Data at a large scale to uncover insights and improve the business.


There is an unimaginable amount of information generated from various sources, such as social media, credit cards, images, video, and so on, in different formats, including documents and PDFs. Facebook alone generates a billion messages and 350 million posts every day. Around 2012, companies began collecting more than 3 million data pieces every day. It doubles every 40 months and is expected to grow up to 40000 ExaBytes this year. The first V of big data- volume defines the ‘amount’ of produced data dependent on the size. If the data is vast, then it is considered ‘Big Data.’ Thus, if big data is a pyramid, then the volume is the base.

Due to the explosion in data production, enterprises cannot process and store it using traditional methods. To save such an unprecedented amount of data in real-time, organizations need to deploy Big Data technologies. With the help of big data hadoop training, distribution systems can be used to store data in several locations and brought together by a software framework.


The second V of Big Data is Variety. Variety refers to heterogeneous sources and the nature of data. The origins of a company can be in-house devices to smartphone GPS technology or social media. The data is in the form of phone numbers, addresses, photos, audio, video, etc. The importance of the source of information depends on the nature of the business. The data has many layers with different values. The 80% of data from these sources is chaotic and unstructured, making structured data just the tip of the iceberg. All in all, the variety means the arrival of data from inside and outside sources.

Big data can be classified into:

  • Structured data: It is organized data with a clear definition of length, format, and volume.
  • Semi-structured data: This is a semi-organized data that partially conforms to a specific data format. An example is log files.
  • Unstructured data: It is the unorganized data that cant conform with traditional data formats. Data generated via digital and social media is an example.


The third V- Veracity or Validity is the authenticity and credibility of the collected data. Big data is difficult to control because of the uncertainties and irrelevance. The reason for this is that unreliability is a multitude of data dimensions from multiple sources. When veracity of data is known, the collected data can be trusted because it is clean and accurate, and decisions can be based on it. Thus, while processing, it is crucial to check the validity of data, and for this, Big Data needs to find an alternative to filter and translate it into essential data. Big data needs to work with all levels of quality because the volume factor results in its shortage.


The fourth V- Value is the primary concentration point and sits on the top of the Big Data pyramid. To find insights, not an enormous amount of data but reliable, valuable, and trustworthy data needs to be stored.  Value amounts to the worthiness of data for a positive business impact. Data in its natural form is not useful but needs to be molded into something valuable. This makes ‘value’ the most critical V and also because it can transform a tsunami into electricity. This is where big data analysis comes into perspective. Data aggregation and storage infrastructure alone cannot add value to the company without advanced data analytics. On comparing the ROI with the total processing cost of big data, a cost vs. benefit analysis can be carried out.


The final V- velocity refers to the speech of data generation, collection, and analysis. Data growth is torrential in today’s data-driven business atmosphere. It is important that continuously flowing data through channels like social media, and mobile phones are captured in real-time, thus making data available at the right time. The data accessing speed is directly proportional to accurate business decisions. Therefore, a limited amount of data in real-time is beneficial. Big data technology allows this with the help of sampling data. Thus, velocity plays an essential role in providing data on demand is a significant aspect of Big Data.


Data is the oil to 21st-century machinery. Data insights add to the value to the decision making of the company. As you now know Big Data and its characteristics, check out big data hadoop certification by Global Tech Council, which helps a learner become a big data expert.