5 V’s of Big Data

Being in the generation that is driven by the data it mandatory to grab a fair share of information about the same. Big data is now the big thing that helps most of the ventures and organizations to use this data wisely for their benefits. The whole market and economy are based upon this massive source of data that keeps the power of making somebody. The effective use and critical analysis of this data include the use of different analytical tools. In a nutshell, you cannot solve or analyses this much of data with the help of traditional techniques. The inclusion of technology is the best thing that happened to this generation.

When it comes to getting an understanding of the Big Data, it is just the bulk of information that is analyzed and calculated with the help of machine learning tools. The foremost aspects of this Big Data are the five V’s that define the whole structure. There is a very significant place of these V’s that includes volume, veracity, value, variety, and velocity. Every aspect keeps a specific place in the importance of Big Data.

Here we are discussing the pillars that keep the mansion of Big Data intact. Though these can be interpreted as core features of Big Data as well. Getting information about these cabs to help you efficiently in understanding the core productivity of Big Data. Let’s delve into the information about the 5V’s that make Big Data an efficient boon.

5V’s of Big Data

The important characteristics of big data are briefed as 5 V’s. We are going to discuss the significant meaning of 5 V’s about big data.

• Volume

Volume refers to the amount of data. The term big data is related to its volume that is enormous. The presence of a huge amount of data is inferred to volume. To calculate or analyses the data value one needs to know the size. When the amount of data is very large then only it is considered as big data. This simply means that to know whether a dataset qualifies in the category of big data or not one should know the volume of data. Therefore, it is necessary to consider a characteristic of big data named volume. The volume of data contributes to determining storage.

For instance, according to a survey in the year 2016, the size of global mobile traffic was a monthly estimated as 6.2 Exabyte (Exabyte means billion GB). This much of data is way effective and it’s not possible to analyze these with the help of the traditional approach. The technological advancement is way necessary for this much data.

• Variety

Variety simply alludes to a type of information from different sources. This feature of big data has arrived from the types of sources from where data has been retrieved. The nature of the data can be divided into three sub-categories:

  1. Structured data
    As the terminology identifies the structured data are those types of data that are already organized in a particular length or format. Most of the Big Data analysis helps to construct the unstructured data in a structured manner.
  2. Semi-Structured data
    The particular data set that is semi-organized comes under this category. Data is not fully formally structured. The best example of this type of data in log files. Numerous Big Data reserves are present in semi-structure format. The analytics tools are specific solutions for analyzing them.
  3. Unstructured data
    All the unorganized data is known as unstructured data. This type of data that is not arranged in a traditional row and column method in a typical database comes under this category. Examples can be texts, audio, video, images, and many more. While knowing the variety of data one can have the ease to audit the presentation of data.

• Velocity

This characteristic of data alludes to the rate of data accumulation. The speed at which data flows can help us to determine whether it comes under the category of big data. All those massive and rapid source of data generation determines the velocity of data. Velocity helps in deciding the capability of data at what speed it is being produced as well as handled to fulfill the needs. The velocity of data can help in processing and analyzing to produce results.

For instance, approximately more than 4 billion searches per day have been recorded on Google.

• Veracity

Veracity simply means the presence of all noise and abnormality in the data. This characteristic is simply related to data mining. It sometimes refers to all the inconsistencies as well as uncertainty in the dataset. One can understand this by assuming the presence of errors and abnormalities available data resulting in decreasing quality as well as deprecated accuracy. As big data is generated from different data dimensions leading to multiple levels of data types. All the noise and abnormality is related to data mining in which we have to sort the relevant data according to our requirement.

For instance, assume a data file full of different types of data which may create confusion.

• Value

Lastly, comes one of the most important characteristics of big data. The enormous amount of value list data is of no use unless one can turn it into something useful for the enterprise. All the bulk data sometimes can hold no value until it can be used to extract valuable information from it. The data should be significant to utilize. Subsequently, this characteristic makes enormous data-size valuable. The value of gazillions of data cannot be denied as this is ample for making and destructing anything.

With the grasp of these core aspects, it is very easy to get an understanding of the core attributes that are the crux of Big Data. These are way efficient and need to be considered in every scenario, whether it is the matter of choosing the tools or getting a solution that can help you to grab all the analysis and insights about the desired big data. Keep these in mind and effectively use the Big Data.