Big data is a field of data science that tells us how to break down large data sets and analyze them. We need data analysis to get valuable insights and information for the decision-making process of any organization. Unfortunately, traditional data processing methods are not efficient enough to capture, store and analyze big data. If you want to know what is big data exactly, you need to understand 4 Vs of big data.
In the past years(2001), big data was defined by an analytic firm Gartner(Meta Group, then), with the help of three 3 Vs (volume, velocity, variety). But later, two more Vs were added to define the big data clearly.
So these days, additional Vs are coming such as 10 Vs, 17 Vs, but 4 Vs of Big data are most important.
These Vs define the innate features of big data.
Let’s discuss these Vs-
4 Vs of Big Data
The speed of data generation, collection, and analysis are known as the velocity. Multiple sources are there through which data flows continuously, such as computer systems, networks, machines, mobile phones, social media, etc. In the present scenario speed of data accumulation is feasible in real-time(within a fraction of seconds).
The fast processing or analysis of data affects decision-making. The main purpose is to get the valuable information present in real-time and transmit it back to the organization to make effective decisions regarding their operations.
The produced ‘amount’ of data refers to the volume of the data. The huge volume of data is known as big data. Generally, data is produced in different formats, such as structured and unstructured, by various sources.
The examples of various data formats are word, excel documents, PDF, and reports, along with videos and images.
In addition, data is continuously produced in large chunks due to digital and social media platforms. This data is difficult for all organizations to store and process with traditional data analysis methods.
Therefore, these organizations should focus on implementing up-to-date tools and techniques to collect, store and analyze such large and massive data fastly.
The only collection of the large volume of data is not useful. This data should be used to add value to any organization with insights. In terms of big data, value defines the data which is useful or useless for an enterprise. Hence, we need data analysis techniques to accomplish this task.
Although numerous organizations have established data estimations and storage bases, they cannot understand the difference between data estimation and value addition.
By using modern data analytics, we can draw important insights from the gathered data. These insights or information is the thing that adds value to the decision of any organization.
Along with the volume and velocity of data, variety is also one of the 4 Vs of big data. We collect the data types from various sources and process them diversly. These data sources can be an external or internal units of any organization.
Mainly big data is divided into 3 categories-
Structured Data- This type of data has a clearly defined format, length, and volume.
Semi-structured Data- The format in this data is partially clear.
Unstructured Data-This is unorganized data that involves images, videos, and other content from social media platforms.
This large amount of data is an arduous task to collect and analyze. In addition, almost 80% of data, including photos, videos, mobile, and social media data, is unstructured by nature.
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What are the 7 Vs of big data?
If somebody asks you what is big data?
What will be your answer?
Just saying big data is a large amount of data is not enough. To define big data, you need to cover above 4 Vs of big data. But other than these Vs, 2 more Vs will help you describe data clearly. So it would be best if you define the big data with all 7 Vs.
Are you collecting relevant and credible data?
The validity or veracity is the affirmation of the quality or credibility of gathered data.
If you know the veracity of data, you will get to know if your data is credible or you can trust this data.
When the data sets are processed, the validity of data is important to check before processing. The main purpose of checking it is to filter the reliable and unreliable data.
4 Vs we have covered above, let’s talk about other 2 Vs.
The insights and valuable facts of big data rely on its context, especially in processing natural language. Different meanings we can get from a single word. With the acceptance of new meanings, old meanings get obsolete with time.
For Example, clarifying connotations is crucial to measure and respond to buzzes on social media.
So, the variability of big data beneficial when you go through the decoding challenge offered by it.
Graphical representation of data is known as visualization. The main task of any data analysis tool is to convert this big data into an easy to understanding form.
The best way to transform this complicated data into a comprehensive and actionable form, graphs, and charts is the best way. Graphs and charts give us a clear picture of any organization’s actions.
In this blog, we have discussed big data and how this data is important. I hope you will found this blog very useful to understand 4 Vs of big data.
Big data analysis is very important for any organization. The 4 Vs of big data helps us to know about the data. We can extract valuable information from the raw data, which is of high volume, velocity, validation, gathered from various sources, and add value to any organization in decision-making. Get the best do my excel project for me services at nominal charges.
Frequently Asked Question
Which V is more important amongst 5 Vs of big data?
Among 5 Vs of data, veracity is most important. Because it helps to filter out useful and useless data. At the end of the process, it creates a deep understanding of data and converts it into a contextual format to make decisions.
Which companies are using big data?
Many global companies are using big data in order to rule the world, such as