Big data is a term that describes the large volume of data – both structured and unstructured that inundates a business on a day-to-day basis. But it is not the amount of data that is important. It is what organizations do with the data that matters.

The term “big data” was coined to describe datasets whose size is beyond the ability of traditional relational databases to capture, store, manage, and analyze. Size, on the other hand, is highly subjective. What one company considers big data may be considered medium or small data by another. The key issue is how prepared a company is to deal with the volume of data it has, regardless of its absolute size.

Big data is one of the most important topics in today’s business landscape. This is because big data has the potential to help businesses become more efficient and effective in their operations.

There are several different definitions of big data out there, but one of the most popular is that big data refers to high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization.

Types of Big Data

Structured Data
Unstructured Data
Semi-structured Data
Multi-structured data

Structured Data
Structured data is stored straightforwardly; it is organized in columns and rows. This type of data comes from accounting systems, SQL databases, spreadsheets, and other highly organized sources.

Unstructured Data

Unstructured data is data that is not structured in any way. Examples of unstructured data include email messages, research papers, social media posts, videos, audio files, and photos.

Semi-structured Data

Semi-structured data is a mix of structured and unstructured data; it can be parsed into standardized portions and has some structure to it but is not completely organized.

It is represented as key-value pairs or labelled fields that describe the information. Semi-structured datasets can include XML documents or JSON files.

Multi-Structured Data

Multi-structured data is very similar to semi-structured data; the key difference is that multi-structure may include multiple structures (such as an XML document combined with images).

What are the 5Vs of big data:

Volume

Variety

Velocity

Veracity

Value

Volume

Volume refers to the massive amount of data that Big Data can store, analyze, and report on. Because Big Data can draw from so many sources, it has an almost unlimited ability to gather information about customers and their habits. It can also analyze multiple different data sources simultaneously.

This makes it possible for businesses to see trends and patterns across a wide range of customer data.

Variety

When we talk about variety in the context of Big Data, we mean that Big Data can easily process both structured (i.e., organized) and unstructured (i.e., not organized) data sources.

Structured data would include things like customer names or addresses stored in a database; unstructured data would include things like Tweets or Facebook posts. With Big Data, you do not have to choose between

Velocity

Velocity refers to the speed at which this data becomes available.

According to jigsaw, Big data systems are designed to handle a massive and continuous flow of data methods like sampling to help in dealing with velocity issues in a big data system.

As an example of the velocity that a big data system has to endure, more than 3.5 billion searches per day are made through the Google search engine. With an ever-increasing number of active accounts on Facebook, the number of likes, updates, shares, and comments coming into Facebook increases by 22% every year.

Veracity

This is the trustworthiness of the source’s data, as well as the quality of the data derived after processing, which is referred to as veracity.

Veracity in big data is commonly known as the assurance of quality or credibility of the collected data.

Value

Value refers to how much an organization can benefit from analyzing its raw data and transforming it into meaningful insights that lead to actionable decisions.

Value is the major issue that we need to concentrate on.
It is not just the amount of data that we store or process. It is the amount of valuable, reliable and trustworthy data that needs to be stored, processed, analyzed to find insights.

To conclude this article, I will say that data is essential to the development of an organization. According to Peter Sondergaard “Information is the oil of the 21st century, and analytics is the combustion engine.”