An Introduction to The 5 V’s Of Big Data Analytics
The term big data has been in the hype for quite a while now, but how many of you actually know its meaning and basics? Worry not; in between the ocean of knowledgeable resources on big data analytics, your boat of curiosity and understanding has stopped at the right port.
Before diving into the 5 characteristics popularly known as V’s of big data, let us start understanding things with the basics.
What Is Big Data?
Big data refers to enormous datasets of unstructured, semi-structured, and structured information from multiple sources. This data is generally never-ending, and most of it is practically of minimum to no use. However, big data analytics consulting services help you store, analyze, and process the data into actionable and valuable information.
Big data generally found in machine learning projects, predictive modeling, and various complex analytics applications is mined to gather valuable insights that can benefit organizations to enhance customer service, better operations, and develop custom and targeted marketing campaigns.
5 V’s Of Big Data Analytics
Big data analytics began with 3 V’s, but slowly, the number of V’s increased to 8, 10 and will keep increasing. However, let us proceed with the discussion of the 5 most important ones.
Volume
Let us start with the primary characteristic. Big data was developed to describe massive information; thus, this trait distinguishes whether a dataset can be considered big data. Volume represents the data’s quantity and size. However, the big data definition can take a turn based on the market’s computing capability at any given time.
Velocity
Several sources, including networks, mobile phones, servers, and social media, send in data, and velocity captures the rapid movement and generation of the data. Additionally, it plays a crucial role in determining how rapidly the raw big data converts into something valuable that an organization can benefit from and act before its competitor.
Variety
Big data is a compilation of information from multiple sources with varying relevance or value. Thus, variety represents the diversity of big data along with its sources. The entire data from internal and external sources is categorized into three types. They are.
● Structured data: Organized or structured data refers to information of a specific format and length.
● Semi-structured data: The information received here accompanies additional information, such as metadata, even when it is not in line with the formal data structures.
● Unstructured data: It includes information that doesn’t perfectly fit in the conventional structure of rows and columns in the databases.
Veracity
There are numerous sources of data, and it accompanies the chances of information having errors, uncertainties, gaps, inconsistencies, and redundancies. Thus, veracity as a trait represents the quality and accuracy of the big data to ensure that the analysts do not come across thousands or millions of data having accuracy concerns.
Value
One of the primary reasons for mining big data is to extract information that can benefit the organization. Therefore, the value here represents the worth big data can add to an organization’s growth by offering insights and information that can help improve its overall functioning.
Wrapping Up
Big data analytics is essential for businesses and organizations to identify opportunities that can lead to better operations, smart strategies, extremely satisfied customers, and higher profits. It improves the decision-making of the organization and can be a crucial weapon to maintain the industrial standing against competitors.
So, it is best to get in touch with big data consulting services in today’s world and leverage the power of technology.
