The Complete Guide to the 4 Vs of Big Data

Did you know that 2.5 quintillion bytes of data are created every day around the world? Even more shocking is the fact that 90% of the data we have today was created in the last few years. This is the rate at which data is generated. When data scientists capture and analyze data, they discover that it is categorized into four sections. These are the four Vs of Big Data.

There are hundreds of sources where the data comes from. Digital images, videos, social media posts, and phone signals are just a few data-generating sources that we may be familiar with. There are many others, such as purchase records, climate information sent by sensors, government records, etc. Big Data means that data is generated in colossal volumes.

A more distinct definition of Big Data reads as data not captured, managed, or processed with commonly used software tools within a reasonable time frame.

There are two types of Big Data. A small part of the big data generated is classified as structured data. This type of data is secured in databases distributed over different networks.

Unstructured data accounts for nearly 90% of Big data information. Unstructured data typically includes human information such as emails, tweets, Facebook posts, online videos, tweets, Facebook posts, text messages and cell phone calls, posts, content from conversation, website clicks, etc.

Many may believe that Big Data is all about size. In a sense, yes, but Big Data is also an opportunity to discover insights into new and emerging types of data and content. Big Data can help organizations make their business more agile and their processes more efficient.

Data accumulation and analysis are difficult tasks because data is mostly available to users in an unstructured form. Here are the four Vs of Big Data.

Volume

The first of the four Vs of Big Data refers to the volume of data. It means the size of the data sets that an organization has to analyze and process. Data volume is usually larger than terabytes and petabytes. Big Data requires a different approach to conventional processing, storage and capacity technologies. In other words, you need specialized technology and configurations to handle the vast volumes of data generated in Big Data.

Organizations can scale or expand to handle large volumes of data.

Scaling involves using the same number of systems to store and process data, but migrating each system to a larger structure.

Scale-out consists of increasing the number of systems. But, they are not migrated to larger systems.

Speed

Speed ​​means the speed at which data should be consumed. As volumes increase, the value of individual data points can decrease rapidly over time. Sometimes even a few minutes can seem too late. Some processes, such as data fraud detection, can be time-sensitive. In such cases, data should be analyzed and used as it flows through the business to maximize its value. An example of such occurrences is the scrutiny of millions of trading events every day to identify potential fraud. It could also include analyzing millions of detailed call records daily within a fixed time frame to predict customer churn.

Variety

Variety makes Big Data a colossal entity. Big Data comes from a number of sources, but is generally one of three types.

  • Structure
  • Semi-structured
  • Unstructured data

Frequent changes in the variety of data streams require distinct processing capabilities and dedicated algorithms. The flow of data from unlimited resources makes the speed complex to manage. Traditional analytical methods cannot be applied to Big Data. Also, new information is detected when analyzing these types of data.

With Variety, you can monitor hundreds of live video feeds from surveillance cameras to focus on a specific point of interest.

Veracity

Veracity refers to the accuracy and reliability of data. It is actually a measure of data quality. Many factors influence data quality. But a key factor includes the origin of the data set. When an organization has more control over the data collection process, veracity is likely to matter more. When they are confident that the dataset is accurate and valid, it allows them to use it with a higher degree of confidence. This trust will allow them to make better business decisions than those from an unvalidated dataset or an ambiguous resource.

Data veracity is a key consideration in any data analysis due to its close connection to consumer sentiment. One of the most accepted social media analytics capabilities used by large companies is to analyze consumer sentiment based on the keywords used in their social media posts.

Analysts consider the accuracy and reliability of the particular platform when taking a call on how to perform analysis on big data. This ensures sound output and value for the end customer. A dataset must score high on the truthfulness front to enter the Big Data classification.

Conclusion

There is a fundamental principle that guides the use of Big Data. An organization must be able to decode data behavior patterns. They can then accurately and effortlessly predict how people will behave in the future. This has huge business implications for all sorts of industries.

Big Data is no longer a series of numbers on large spreadsheets. Data today can enter an organization from a variety of sources, some fully reliable, some not. Businesses need state-of-the-art big data analytics techniques to accept and process large amounts of data quickly and efficiently. Hopefully covering the four Vs of Big Data will help your business thrive.

More Stories
Pabst Blue Ribbon and Entenmann’s to produce “ Hard Coffee & Donuts ” collaboration