Big Data Analytics: The Future of Information

Big Data Analytics

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In the contemporary era, many social apps are being developed, resulting in a massive increase in data every day. As we talk about social media platforms, millions of users connect on a daily basis, and information is shared while users use a social media platform or every other website, so the question arises as to how this massive amount of data is handled and by what medium or tools the data is shared. This is where Big Data Analytics enters the picture.

Let us learn what big data is and how it is categorised in this blog. 


Let’s start with a definition of the term “data.” 

Data can be described as all facts and figures that can be stored in a digital format. Data includes all of the text, numbers, photographs, audios, and videos saved on our digital devices.

Big Data is a term that refers to a vast amount of organised and unstructured data that is difficult to process using conventional database and software techniques. 

In most business cases, the amount of data is either too large, travels too quickly, or exceeds current processing power.

Big Data has the ability to help businesses develop their processes and make more informed decisions faster. 

Emails, mobile devices, apps, databases, servers, and other sources are all used to collect information. 

When this data is collected, stored, and analysed, it may help a business gain valuable insight into how to increase sales, gain or maintain customers, and enhance operations.

Types of Big Data

There are three types of big data classifications:

  1. Structured Data
  2. Unstructured Data
  3. Semi-structured Data

These three terms are crucial in big data, even though they are theoretically relevant at all levels of analytics. 

When dealing with large amounts of data, it’s much more important to understand where the raw data comes from and how it needs to be processed before being analysed. 

Since there is so much of it, data extraction must be effective in order for the project to be worthwhile.

The structure of the data is crucial in determining not just how to deal with it but also what insights it can yield. 

Before it can be analysed, all data must go through an extract, transform, and load (ETL) procedure. 

Data is collected, formatted so that an application can read it, and then stored for later use. Each data structure requires a different ETL method.

Structured Data

Structured data is most often classified as quantitative data, and it’s the sort of data with which most of us are familiar. Consider data in relational databases and spreadsheets that fits neatly inside fixed fields and columns.

Names, dates, addresses, credit card numbers, stock details, geolocation, and other structured data are examples.

Machine language can easily understand structured data because it is well-organized. Many working with relational databases will use a relational database management system (RDBMS) to easily input, scan, and manipulate structured data. This is the most appealing characteristic of structured data.

Unstructured Data

Unstructured data is information that lacks a predefined data model or isn’t organised in a predetermined way. 

It is usually text-heavy, but it may also include data including dates, numbers, and statistics. 

As opposed to data stored in formal databases, this results in errors and ambiguities that make it difficult to understand using conventional programmes.

Audio, video files and No-SQL databases are all examples of unstructured data.

In recent years, the ability to store and process unstructured data has significantly improved, with many new technologies and software that can store specialised forms that enter the market.

Since a large portion of information in organisations is unstructured, the ability to analyse unstructured data is particularly important in the context of Big Data. 

Consider images, videos, or PDF documents. One of the key drivers of Big Data’s rapid growth is the ability to derive value from unstructured information.

Semi-Structured Data

Aside from structured and unstructured data, there is a third type that is a hybrid of the two. 

Semi-structured data has certain distinguishing or consistent characteristics but does not adhere to the rigid structure that is expected for a relational database. 

As a result, certain organisational properties such as semantic tags or metadata have been added to make it easier to organise, but the data remains fluid.

A good example is email messages. The content is unstructured but contains structured data such as the sender’s and recipient’s names and email addresses, as well as the time the message was sent. 

A digital photograph is another example. The picture is unstructured, but it will be date and time stamped, geo branded, and have a system ID if it was taken on a smartphone, for example. 

After being saved, the picture could be labelled with tags that provide structure, such as “forest” or “city.”

Since it includes certain classifying features, a lot of what people would normally classify as unstructured data is actually semi-structured.

Importance of Analyzing Big Data

The importance of big data can be seen from two broad and distinct categories.

Impact on the Market

Companies use the big data that has accumulated in their databases to optimise operations, provide improved customer support, develop tailored marketing strategies based on individual customer needs, and, eventually, increase profits. 

Businesses that use big data have a potential competitive advantage over those that don’t because they can make better and more educated business decisions if the information is used properly.

Big data, for example, can provide businesses with useful customer information that can be used to improve marketing strategies and tactics in order to boost customer loyalty and conversion rates.

Its use allows businesses to become more customer-centric. 

Consumer expectations can be assessed using historical and real-time data, allowing companies to update and develop their marketing strategies and become more attentive to customer wants and needs.

Influence on various Industries

Health researchers and physicians use big data to classify disease risk factors and diagnose diseases and disorders in specific patients. 

Furthermore, data derived from electronic health records (EHRs), social media, the internet, and other outlets provides healthcare facilities and government agencies with up-to-date information on infectious disease risks and outbreaks.

Big data assists oil and gas producers in identifying possible drilling sites and monitoring pipeline activity, and utilities use it to track electricity grids. 

Systems that utilize big data are used by financial services companies for risk management and real-time business data analysis. 

Manufacturers and logistics firms use big data to track supply chains and improve distribution routes. 

Emergency intervention, crime reduction, and smart city plans are some of the other government uses.


Because of the importance of big data analytics, there is a lot of competition and a lot of demand for big data experts. 

Data science and analytics is a rapidly developing field with a lot of promise. Big data analytics has a significant role to play in a variety of fields and industries. 

As a result, it is important for a practitioner to remain knowledgeable about these techniques. 

At the same time, businesses will benefit greatly from the proper use of these analytics tools.

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