The 5Vs of Big Data and Key Characteristics of Big Data 3v, 4v,5v, 6v, 8v

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The definition of “big data” is based on five key characteristics. Initially, big data only had three characteristics, hence the name “3v big data,” but it has since expanded to include four characteristics, five characteristics, six characteristics, seven characteristics, eight characteristics, ten characteristics, seventeen characteristics, and even forty-two characteristics. To simplify the first characteristic, we will discuss the meaning behind the characteristics of big data.

Big data exemplifies the following characteristics

What exactly are the characteristics of big data? The characteristics of big data are the features, qualities, or criteria that establish beyond a reasonable doubt that a set of information has been classified as big data.

3v Big Data 

What does the term “3v” mean in the context of big data?

The 3 characteristics of big data are often referred to as “big data 3v,” which stands for “volume,” “variety,” and “velocity.”

1. Volume

What exactly does “volume” mean in the context of “big data?”
The term “volume” in the context of big data is defined as the amount or quantity of data resulting from a large number of transactions and a large amount of stored data.
What, precisely, is this data sample? User activity records can take many forms, including browser and e-commerce transaction records, information about Indonesian citizens and residents, information about bank customers, and much more.
Big data is typically measured in Terabytes (per 1000 Gigabytes) or Petabytes (per 1,000,000 Gigabytes); for example, according to Facebook’s published data at’s-top-open-data-problems, Facebook generates a total of 400 petabytes per day, or 400.000.000 Gigabytes; obviously, data of this magnitude is already classified as “big data

2. Variety

What does the term “variety” mean when applied to big data in the context of the “3v” variable?
This means that the data can have a wide range of characteristics, from being highly structured to being semi-structured or even completely unstructured.

  • What is structured data, also known as “Big Data”?

Data that is stored in a predetermined format, such as a relational database or a SQL database, is considered structured. This type of data typically includes elements such as keys (primary key, relational key, foreign key) that allow for analysis.

  • Semi-structured data

Data that isn’t stored in a relational database but has a pattern or is organized quickly enough that it’s easy to analyze can be imported into a relational database with a little bit of work; examples include XML and CSV files, which are frequently used for database export.

  • Unstructured data

Information or data that is poorly organized due to its intrinsic characteristics, or that lacks a defined data (such as an image, audio, video, or text file) may include the following

3. Velocity

What does the term “Velocity” mean in the context of “big data”?
In the context of 3v big data, “velocity” refers to the rate at which new information can be created, accessed, and processed. Big data platforms and analytics software must be able to process large amounts of data as quickly as possible in response to requests. One example of such high speeds is Google’s search engine, as evidenced by data from On average, Google must process 40,000 search queries every 24 hours.

Big Data 4v

What is the meaning of big data in 4 perspectives?

It’s been said that the four most important characteristics of big data are volume, variety, speed, and accuracy; in this case, we’d add reliability to that list.

4. Veracity 

What does accuracy mean in the context of big data? When we talk about “veracity” in the context of big data, we’re referring to the truthfulness, dependability, quality, and accessibility of the data, or, to put it in Indonesian, to the fact that we can trust the data as it is.
One example of business integrity is the fact that, as a company’s database grows, it becomes more difficult to ensure that all of the information contained within it is up-to-date and accurate. This is especially true of dynamic databases, such as those containing information about customers, business partners, and family members.
Discuss the reasons why the company’s data must be accurate. Since this information is used for big data analytics, it is essential that the data be accurate; otherwise, the resulting analysis will be flawed. For this reason, many large businesses have departments devoted to updating and archiving their data, one of which is customer information. Companies will contact their customers and ask them to update information like age, address, phone number, email address, and social media profiles if any of this information changes. This will ensure that the companies’ analyses are accurate and that their marketing campaigns, which include sending out text messages and emails offering products, reach the right people at the right time.

5v Big Data

What exactly is a “5-vector” in the realm of “big data?”
Volume, variety, velocity, veracity, and value make up the “5v” of big data, with value being an additional feature.

5. Value

This value is the apex of the 5v big data and the most important characteristics in business analysis. Value in this 5v big data context refers to the importance of the data itself; this importance, in turn, depends on the quality of the data itself and the expertise of the data analysts who process it; and with the right information and proper processing, big data can yield crucial data for making important decisions.
One example of the value that can be generated by big data is the data collected by the Indonesian government through the initiative. This initiative allows the government to collect data from a variety of ministries and agencies, such as the ones responsible for the country’s food security programs.
In this example of a food safety program, the government is able to analyze food safety in Indonesia by drawing on data from the agriculture ministry, the trade ministry, and other relevant institutions to learn about the country’s production capacity, stockpile size, and consumption needs for rice and chicken. Using this information, the government can predict when Indonesia will face food shortages.


After discussing the 3v, 4v, and 5v versions of big data, it became clear that there were other versions with different characteristics, including those with 6, 7, 8, 10, and even more; different sources listed the same characteristics in different numbers Each of its characteristics is unique, making it difficult to generalize about the source’s reliability; nonetheless, this article will discuss those features in order to broaden readers’ understanding of them.

6v big data 

6v plus 1 in big data = Variability

6. Variability

In this context, “variability” refers to a used variable that affects how far and how quickly changes occur in a company’s data structure, as well as how often the meaning or shape of the company’s data changes. For example, consider a company that offers novel-based services, such as the ones listed below.

  • Prices for digital novels and app versions start at 5$ per month.
  • novel prices in the form of a sealed bid of 10$
  • Internet service provider monthly fees average 5$

The company will create a simulation of the above option in the following quesioner :

The majority of customers are likely to choose two of the three options—print novels and online reading—because they provide the most value to readers. However, if customers are given the option to narrow their choices, they are more likely to choose print novels and online reading.

7v big data

visualization in big data 7 v increased


The purpose of data visualization is to make large, complex datasets more digestible to readers by presenting them in the form of charts, graphs, and other visual representations, as opposed to scattershot spreadsheets or word documents crammed with numbers and formulas.

8v big data 

Finding 8v Big Data in 17v Big Data

10v big data

All of the big data from 10v is included in 17v. A lot of data.