Sunday, September 19, 2021

What Are The Actual Meaning of 4 V’s of Big Data? – A Detailed Explanation by EduDataOnline

Introduction:

The term Big data are often understood as large databases but in actual they are Compilation, storage, and Exploitation of large databases. In order to understand the difference between regular databases and big data, the data scientists by IBM have broken the big data into four dimensions are Volume, Variety, Velocity, and veracity. These dimensions are known as 4 V’s of Big data.

In this guide, we are describing the 4 V’s of Big data in order to help you understand what Big data actually is.

What are the 4 V’s of Big Data

Following are the 4 V’s of Big data:

Volume

4-v's-of-big-data
Volume in Data Science
  • As the name indicates, a Big data is a database whose size is enormous.
  • By the Volume dimension of big data, we refer to its size which is needed to be analyzed and processed.
  • The volume of Big data should be huge which are frequently larger than tetra bytes and petabytes.
  • There is an important role in the size of data in determining the value of data. Only when the volume of data is very large, it will be considered Big data.
  • Yes, as mentioned, big data is the database having a huge volume of data but every database with a large amount of data is not necessarily Big data.
  • While dealing with big data, data scientists or data analyst needs to consider its Volume characteristic.
  • Example: the global mobile traffic in 216 was estimated to 6.2 Exabytes per month.

Velocity

4-v's-of-big-data
Velocity in Data Science
  • This characteristic of Big data is described by the high speed of the data accumulation or the speed at which data is generated.
  • The high generation velocity of data requires distinct processing techniques.
  • In big data, there is a continuous and massive flow of data. The potential of the data can be determined by the velocity of how fast is the generation and processing of the data in order to meet the demand.
  • Example: People are making more than 3.5 billion searches per day on Google.

Variety

4-v's-of-big-data
Variety in Big Data
  • Variety is another characteristic that determines the nature of the data.
  • It is the dimension that makes big data actually very big.
  • The variety of Big data can be one out of three types that includes: Structured, semi-structured, and unstructured data.
  • The different variety of big data requires distinct processing methods and specialist algorithms.
  • The variety of big data can be understood as the sources for the generation of big data that can be structured, semi-structured, and unstructured.
    • Structured data: This is an organized type of big data. In this data type, there are defined formats and lengths of the data. Example: Excel files, SQL databases
    • Semi-structured data: The data arrived from this source is generally semi-organized. This type of data does not conform to the formal structure of the data. Example: Log files
    • Unstructured data: Unorganized data is referred to as unstructured data. This type of data is those that do not fit into the traditional row and column structure of the relational database. Example: social media posts that express the ideas and thoughts of humans which can’t be stored in the forms of rows and columns.

Veracity

  • The veracity of big data can be referred to as the inconsistency and uncertainty in data. This can be explained as sometimes the company receives messy data that is compromised with quality and accuracy.
  • In general, we can describe this V for both the quality and availability of data.
  • Data with high veracity is valuable to analyze and helps in getting a meaningful result. On the other hand, low veracity data is rich with meaningless data.
  • Example: There might be confusion in bulk data while the less amount of data results in conveying incomplete information.

What are 5 V’s of Big data geeksforgeeks?

4-v's-of-big-data
Five V’s of Big Data Image Credits: Edureka

Recently there were 4 V’s of Big data by geeksforgeeks. But currently, they have added one more dimension of big data that stands for Value. It can be explained as no value of bulk data unless you analyze it and turn it into something meaningful.

In itself, there is no value of data until you make it valuable for extracting so,e information.

What are the 4 V’s of big data analytics in Healthcare?

Big data analytics in Healthcare is not limited to 4 V’s of Big data. Instead, they have many V’s to explain the specific property of big data that is needed to be understood and addressed by the healthcare organization. Along with Volume, Velocity, Variety, veracity, and Value, other V’s includes:

Validity: It refers to the accuracy of data that how accurate the data is. There is a need for much accuracy of data in the field of healthcare.

Viability: This V refers to how relevant the data is to the use case at hand. There is a need to understand the element of data that is actually helpful in predicting the desired outcome for trustworthy results.

Volatility: This V stands for the frequency of changes in data. There are many changes in the healthcare data that raise the question that how long data is relevant. This V answers how long you should keep any data.

Vulnerability: This V describes data protection in a world full of ransomware attacks. It is essential to keep the data secure. And hospitals or other healthcare companies need to invest more to keep data secure and private.

Visualization: This explains the presentation of the data to the users. There is a need to present the complex data in a simple format thus make it easy to understand.

Extracting business value from the 4 V’s of big data

  • Big data offers the company the potential to get superior value from analytics on data at high velocity, high volume, high variety, and high veracity.
  • A higher volume of data helps in achieving a more holistic view of the past and present view of the business and helps in predicting the likelihood in the future.
  • With the high velocity of data, there will be continuous updates and thus help you receive real-time data.
  • A broader variety of data will provide you a more nuanced view of the matter.
  • And the high veracity will make you confident that you are working with the cleanest, truest, and most consistent data.

What are IBM’s 4 V’s of big data?

In order to understand the difference between regular databases and big data, the data scientists by IBM have broken the big data into four dimensions are Volume, Variety, Velocity, and veracity. These dimensions are known as 4 V’s of Big data.

How to successfully manage the 4 V’s of big data?

As mentioned, there are 4 V’s of big data including Volume, velocity, variety, and veracity.

  • In order to manage the Volume dimension of big data, the company can opt for cloud storage to store the huge databases.
  • The high velocity of big data can be managed by using Streaming data solutions.
  • It is possible to manage the variety of big data by recording every transformation milestone which is applied to it along the route of the data processing pipeline.
  • In the case when you are not having high veracity data, it is a good idea to extract only the high-value data instead of collecting all data.

Conclusion

So these are 4 V’s of big data that is used to describe the dimensions of the big data in order to understand the present trend of the market as well as to predict the future trend for successful business operation.

Read our another popular article on Big Data Visualization- The Ultimate Interpreting Heritage

FAQs

What are 4 V’s of operations management?

The four V’s of operation management includes Volume, Variety, Variation, and Visibility.
The volume dimension can be referred to as how much of the specific product is required in order to satisfy the demand.
The variety dimension refers to the variety of goods/ services that need to be produced as per the demand of the customers.
Visibility in Operation management relates to how much of the company’s process is experienced by the customers.

What are 3V’s of big data?

In the beginning, there were three dimensions for Big data. They were known as 3V’s of big data that includes Volume, velocity, and variety.
As per the name, the volume dimension refers to the volume and amount of the big data.
The velocity dimension determines the speed of the accumulation or generation of big data.
Variety is another characteristic that determines the nature of the data.

What are 5V’s of Big data?

In order to make big data a huge business, there are 5 keys known as 5 V’s of big data that includes Volume, Velocity, Variety, Veracity, and Value.
Volume is the amount of big data which is needed to be analyzed and processed.
Velocity is the speed at which the big data is generating.
The source and nature of big data are characterized by its variety.
In general, we can describe the veracity of both the quality and availability of data.
Value refers to the use or analysis of big data which turns it into something meaningful.

What are 6 V’s of Big data?

There are 6 dimensions to describe Big data referred to as the 6 V’s of big data. These include Volume, Variety, Velocity, Value, veracity, and Variability.
The size of big data which is needed to be analyzed and processed is known as Volume.
The dimension used to determine the speed of accumulation of big data is Velocity.
Variety refers to the source and nature of big data.
The quality and availability of big data are described by its veracity.
Big data has no value unless it is processed and changed into something meaningful.
Variability refers to the extent and the speed at which the data is changing.

What Are The Actual Meaning of 4 V's of Big Data? - A Detailed Explanation by EduDataOnline

Working as Freelance Content writer. Have great experience in creative writing for different Niches. Worked as Assistant Editor and Communication Manager for Metro Rail News Magazine. I am also very much interested in data science, data analytics, big data. Thank you.

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What Are The Actual Meaning of 4 V's of Big Data? - A Detailed Explanation by EduDataOnline

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Working as Freelance Content writer. Have great experience in creative writing for different Niches. Worked as Assistant Editor and Communication Manager for Metro Rail News Magazine. I am also very much interested in data science, data analytics, big data. Thank you.