The term big data is often understood to mean a large database, but actually, it is the compilation, storage, and operation of large databases. To understand the difference between a typical database and big data, IBM researchers divide big data into four dimensions: volume, diversity, speed, and reliability. This dimension is known as 4V big data.
In this guide, we cover the 4 V’s of Big Data so you can understand what Big Data really is.
What are the 4 V’s of Big Data
- As the name suggests, big data is a very large database.
- What we mean by the volumetric dimension of big data is its size, which is required for analysis and processing.
- Large amounts of data must be large, often larger than tetrabytes and petabytes.
- It plays an important role in the amount of data to determine the value of the data. Only when the volume of data is very large is it considered a large amount of data.
- Yes, as I said, big data is a database with a large amount of data, but any database with a large amount of data is not necessarily big data.
- When dealing with big data, data scientists or analysts must take volume characteristics into account.
- Example: Worldwide mobile traffic of 216 is estimated at 6.2 exabytes per month.
Velocity
- This feature of big data is described by the high rate of data accumulation or the rate at which data is generated.
- High-speed data generation requires different processing techniques.
- With big data there is a continuous and massive flow of data. Data potential can be determined by the speed at which data is generated and processed to meet demand.
- Example: People perform more than 3.5 billion searches on Google every day.
Variety
- Diversity is another characteristic that determines the nature of the data.
- This is the dimension that actually makes big data big.
- A lot of big data can be one of three types, including: structured, semi-structured, and unstructured data.
- The different diversity of big data requires different processing methods and special algorithms.
- The diversity of big data can be understood as a source of big data generation, which can be structured, semi-structured and unstructured.
- Structured data: It is an organized type of big data. There is a specific data format and length for this data type. Example: Excel file, SQL database
- Semi-structured data: The data obtained from this source is usually semi-organized. This data type does not fit into the official data structure. Example: log files
- Unstructured data: Unorganized data is known as unstructured data. This data type does not fit into the traditional row and column structure of a relational database.
- Example: social media posts that express people’s ideas and thoughts that cannot be stored in rows and columns.
Veracity
- The reliability of big data can be described as inconsistency and uncertainty in the data.
- This can be explained by the fact that sometimes companies receive scattered data, the quality and accuracy of which is compromised.
- In general, we can describe this V for data quality and availability.
- Highly reliable data is invaluable for analysis and helps achieve meaningful results. On the other hand, low-reliability data is rich in meaningless data.
- Example: In the case of bulk data, confusion may occur, while less data leads to the incomplete transmission of information.
What is 5V from Geekforgs Big Data?
Recently there was 4V Big Data from Geeksforgeeks. But now they have added another dimension to big data, namely value. It can be declared as worthless group data unless you analyze it and make it meaningful.
By itself, data has no value until you make it valuable to extract the information.
What Are 4 Vs Big Data Analytics in Healthcare?
Big data analysis of health data is not limited to 4V big data. Instead, they have multiple V’s to describe the specific characteristics of big data that healthcare organizations need to understand and address. In addition to volume, speed, variety, reliability and value, the other V’s are:
Validity: It refers to the accuracy of the data and the accuracy of the data. There is a need for highly accurate data in healthcare.
Viability: This V has to do with how important the data is for a particular application. It is important to understand which data elements are actually useful in predicting the desired outcome for reliable results.
Variability: This V indicates the frequency with which the data changes. The number of changes in health data raises the question of how long they are relevant. This V corresponds to the time it takes to save the data.
Vulnerabilities: This V describes data protection in a world full of ransomware attacks. It is important that data is protected. And hospitals or other healthcare companies need to invest more in keeping data safe and confidential.
Preview: This describes how the data will be presented to the user. Complex data needs to be presented in a simple format for easy understanding.
Extract business value from 4V big data
Big data offers enterprises the potential to extract superior value from data analysis with high speed, high volume, high diversity and high reliability.
The greater amount of data helps in gaining a broader view of the past and present for the company and in predicting the possible future.
With high data rates, there are constant updates to help you get real-time data.
A greater variety of data gives you a more nuanced view of the problem.
And high reliability gives you the assurance that you’re working with the cleanest, most accurate, and most consistent data.
What is 4V in IBM Big Data?
To understand the difference between a typical database and big data, IBM researchers divide big data into four dimensions: volume, diversity, speed, and reliability. This dimension is known as 4V big data.
How do you manage to successfully manage 4V Big Data?
As mentioned earlier, there’s 4V big data, including volume, speed, diversity, and reliability.
To manage large amounts of data, companies can opt for cloud storage to store large databases.
High-speed big data can be managed with the help of data streaming solutions.
It is possible to manage the diversity of big data by recording each stage of the transformation applied to it along the data processing pipeline.
If you don’t have high-reliability data, we recommend extracting only good-quality data rather than collecting all the data.
What is 4 V operation control?
The four V’s for operational management include volume, diversity, variety, and visibility.
The size of the volume can be determined as part of a particular product required to meet demand.
Diversity refers to the variety of goods/services that need to be produced based on customer demand.
Operations visibility relates to how much of the company’s processes the customer experiences.
What is 3V Big Data?
Initially, big data had three dimensions. This is known as 3V Big Data, which encompasses volume, speed and diversity.
Volume size, by name, refers to the volume and amount of large data.
The size of the speed determines the speed of accumulation or generation of large amounts of data.
Diversity is another characteristic that determines the nature of the data.
What is 5V Big Data?
To turn big data into big business, there are 5 switches known as 5v big data covering volume, speed, diversity, reliability and value.
Volume is the large amount of data required for analysis and processing.
Speed is the speed at which big data is generated.
The sources and types of big data are characterized by their diversity.
In general, we can describe the reliability of both data quality and availability.
Value refers to the use or analysis of big data, which makes it meaningful.
What is 6V of Big Data?
There are 6 dimensions to describe Big Data, which is referred to as 6V Big Data. These include volume, variety, speed, value, reliability, and variability.
The large amount of data required for analysis and processing is called volume.
The measure used to determine the rate at which large amounts of data accumulate is speed.
Diversity refers to the source and nature of big data.
The quality and availability of big data is explained by its reliability.
Big data has no value unless it is processed and turned into something meaningful.
Variability refers to the rate and speed at which data changes.