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Big Data Analytics – 7 V's of Big Data

Learn about the 7 V's of Big Data (Volume, Variety, Velocity, Variability, Veracity, Visualization, and Value) with explanations.
Submitted by IncludeHelp, on January 08, 2022

The 7 V's of Big Data describes the characteristics of Big Data. The 7 V's of Big Data gives a demonstration to users to get familiar with its environment and various working principles on which it deals. This tutorial describes the 7 V's of Big Data Analytics.

Paper records, files, and storage discs have all but become useless as a result of the exponential expansion in the amount of data. The storage of data in some database systems has now become widespread, however even with the expansion of the internet, new applications, and technological advancements; the available storage space is insufficient.

Big Data is more than just having a lot of data or having a vast amount of it. Big Data is one type of data that comes from a variety of sources and consists of a variety of different types of data in a variety of different formats. In the context of Big Data, the "Big" refers to data sets of such enormous size that standard database systems are incapable of processing the information in a timely and efficient manner. However, there is more to Big Data than simply its sheer size, and this is where things become interesting. Doug Laney, a Gartner analyst, previously described Big Data as consisting of three dimensions: high volume, high velocity, and high variety. However, there are other "Vs" that can be used to better understand Big Data's true nature and ramifications.

There are 7 V's to Big Data:

  1. Volume
  2. Variety
  3. Velocity
  4. Variability
  5. Veracity
  6. Visualization
  7. Value

1) Volume

Large amounts of data are the most distinguishing feature of big data. Big data is defined as "BIG" in this context by the phrase volume. With the tremendous amount of data that is generated every day, we know that gigabytes are insufficient to store such a large amount of data. As a result, data is now stored in terms of Zettabytes, Exabytes, and Yottabytes, rather than megabytes. The amount of the data sets that must be evaluated and processed is referred to as the volume of data. These data sets are now regularly larger than terabytes and petabytes in size and must be processed in real-time. The sheer volume of data necessitates the development of new and distinct processing technologies that differ from standard storage and processing capabilities.

2) Variety

Variety refers to numerous sorts of data sources, which has resulted in an increase in data diversity, which includes everything from stored and structured data preserved in business databases to unstructured data, semi-structured data, and data in various forms. Big data can be classified into three types: structured data, semi-structured data, and unstructured data. In today's world, the data that is generated in large quantities is only unstructured data such as audio files, video files, images, text files, and so on. These types of data are difficult to map due to their nature, i.e., they do not follow any set of rules, which makes it difficult to separate them from the essential information.

One of the most difficult issues of large data is characterized by its diversity. It can be unstructured, and it can contain a wide range of different sorts of data, ranging from XML to video to text messages. Finding a meaningful way to organize data that makes sense is not an easy process, especially when the data itself is constantly changing.

3) Velocity

The speed with which data may be processed and accessible is referred to as velocity. The rate at which data is moved, processed, and captured within and outside of organizations has increased substantially in recent years. Traditionally, business intelligence models took days to process, but today's analytical requirements demand that data be recorded and processed "practically" in real-time, which is made possible by the high-speed flow of information available today.

In the case of social media postings, YouTube videos, audio files, and photographs, which are uploaded in the hundreds every second, the content should be available as soon as possible.

The availability and ubiquity of devices connected to the internet, both wirelessly and through wired connections, allows for data to be transmitted in near real-time. At the moment, information is exchanged at breakneck speeds.

4) Variability

Variability is distinct from diversity in that it is unpredictable. In statistics, variability refers to the fact that the data is always changing. The concept of variability is primarily concerned with comprehending and interpreting the correct interpretations of raw data. When it comes to analyzing impressions, this is critical. Algorithms must be able to comprehend the context in which they operate and decipher the precise meaning of each word in their specific environment. This is a considerably more involved investigation.

5) Veracity

Integrity is defined as the ability to retrieve trustworthy information. Because of the high quality of accurate data, it is possible to make more use of them. This is especially crucial for businesses whose core business is based on the dissemination of information. It is essential to ensure that the information you collect is accurate, as well as to keep incorrect information out of our systems. It also refers to the reliability or quality of the data that a corporation receives and processes in order to gain relevant insights from it. Some people, however, argue that, given the vast amount of information already available, veracity is really a secondary characteristic of Big Data.

6) Visualization

The ability to convey data to management for decision-making purposes is referred to as "visualization". This refers to making the data that has been collected and evaluated comprehensible and simple to comprehend. It is impossible to utilize and leverage raw data unless it is presented in an appropriate manner. Data can be displayed in a variety of formats, including excel files, word documents, graphical charts, and so on. What matters most is that the information is easy to read, comprehend and obtain regardless of the format; for this reason, data visualization is so vital.

7) Value

In big data, valuable data has a known value, which is reflected in the return on investment from data management. It is important for every user to recognize that the company needs some kind of value after the efforts and resources have been spent on the above-mentioned V’s. It is possible for Big Data to assist a user in providing value if it is collected and handled effectively.


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