"The 5 V’s of Big Data is one of the methods that many companies must do today. Lets find out more!"

Data became very important after the idea came up that every piece of data is important to the business in the final scheme of things. This led to the study of data in the business ecosystem, and the thought arose that proposed to collect all the data that runs through the business. Now, this can be applied to the 5 V’s of Big Data.

Along with the arrival of Big Data architecture that proposes a system that collects, stores, and analyzes this huge amount of data. In this article, we will learn about the 5 V’s of Big Data. Big Data is the stuff that deals with large amounts of data at high speed passing through businesses and in the various ways it presents itself.

The entire amount of data that businesses create and the tools to collect, store, and analyze data can be called Big Data. Big Data indicates several characteristics that include challenges such as collecting, analyzing, storing, visualizing data that comes in at various speeds and in various formats.

What is 5 V’s of Big Data

Of the many characteristics of big data, there are 5 V’s of Big Data that characterize the nature of big data the best. In this section, we will look at each of these V's in more detail. Here are the 5 V’s of Big Data you should know.

#1 Volume

The most characteristic of Big Data properties, Volume, highlights the amount of data that passes through the workday day in and day out and how each item of data needs to be captured to understand the business holistically to gain value from it. Data here will refer to anything that can be collected, structured, unstructured, semi-structured, arriving in batch or real-time.

Without large volumes, it is unfair to call the data ecosystem as Big Data, even though it covers every aspect of business. The entire premise of value in Big Data is based on this first V, Volume. The year 2016 saw global mobile traffic of 6.2 exabytes per month. It is estimated that by 2020, this will easily reach 40000 exabytes.

#2 Velocity

Velocity in big data refers to the essential characteristic of capturing incoming data at any speed. Today, data is almost stateless. It comes and leaves the business ecosystem at high speed. Big Data systems are equipped to capture this data at the speed it comes in. If the speed does not match the rate at which data is coming in, there will be frequent backlogs, which eventually choke the system.

Big data systems are designed to handle huge, continuous streams of data-methods such as sample collection help in dealing with speed issues in big data systems. As an example of the speed that big data systems have to endure, more than 3.5 billion searches per day are made through the Google search engine. With the ever-increasing number of active accounts on Facebook, the number of likes, updates, shares and comments coming into Facebook is increasing by 22% every year.

#3 Variety

It is characteristic of Big Data to capture anything and everything of value in the business ecosystem. This includes data with no direct value to pass down but can be further processed with advanced tools to gain insights on building intelligence into the system.

Apart from the structured data that a business works with, unstructured data sets such as images, videos, sounds, flat files, email bodies, log files, and more. It contains data that can be mined with advanced tools. Big Data systems are designed to capture unstructured and semi-structured data that passes through the business in a timely and efficient manner. This also means that in addition to storing various data or heterogeneous data, Big Data systems must connect these different types of data sources efficiently without slowing the speed.

#4 Veracity

With the volume, variety, and velocity that Big Data enables, models built on data will not have true value without these characteristics. Veracity is the trustworthiness of the source data, the quality of the data obtained after processing. The system must allow mitigation against data bias, abnormalities or inconsistencies, volatility, duplication, among other factors.

#5 Value

The most important V as far as business goes is Value. If a Big Data system cannot obtain value from the entire practice in a reasonable amount of time, it is not a worthwhile practice to engage in business. Big Data is theoretically supposed to give you value. How big or small is that value for the analysis team and research team to think about, design, build and deliver. Value is one of the first properties discussed in business, and a certain level of value will be projected at the beginning of a Big Data project.

Big Data helps build the infrastructure on which Machine Learning and Artificial intelligence are based. Businesses that start today becoming Big Data tomorrow can easily turn to Machine Learning and Artificial intelligence to improve their decision-making processes.

The 5V characteristics of Big Data initially included the 3 core V's of Velocity, Volume, and Variety. The other three v's of Veracity and Value were added later with the evolution and prevalence of Big data across industries. All of these 5 V’s of Big Data are critical to understanding Big Data architecture. Big data analytics is at the vanguard of the journey towards an increasingly data-centric world. Being a powerful intellectual resource, companies go to great lengths to hire and retain them. Want to find out more and use Comprehensive Analytics' Big Data 5V services? Check it out here!