Introduction

Your messenger chats, social network postings, likes, purchasing history on the e-commerce websites you visit regulary, Netflix movie recommendations, browser history, and so on are all data. Big Data is one of the fastest-growing industries on the planet. It refers to the process of gathering and analysing vast amounts of data to provide actionable insights that a company can use to improve its many features. Customer data, sales figures, financial data, application traffic, and other forms of big data are used by major enterprises. You can probably guess how much data is generated per second. There is no limit in producing data as it’s the key part of every sector in the world. So, it cannot be ignored. 

What is Big Data?

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. It is made up of a lot of data that isn't processed by typical data storage or processing units. It is used by several international corporations to process data and conduct business. Government agencies, businesses, healthcare providers, and a slew of other organisations are focusing on Big Data to improve their operations and propel their growth.

Because Big Data helps firms to detect trends, patterns, and relationships that would be difficult or impossible to find with traditional data-processing methods, there is a high need for Big Data professionals across a variety of industries. Companies hope to obtain insights by appropriately analysing these massive data sets. The analysis aids them in recognising trends in the data, which leads to more informed business decisions. All of this helps to save time, effort, and money. There are a plethora of Big Data instances. Organizations in numerous industries create and use data to improve their processes, from social media platforms to e-commerce businesses. The following are some of the scenarios in which Big Data is generated:

• Netflix and Amazon Prime are two examples of online media streaming services.

• Consumer shopping patterns on e-commerce platforms are being studied.

• Using web apps to generate new consumer leads

• Wearable devices, such as smartwatches, can be used to monitor health issues.

• Transportation industry fuel optimization tools

• Autonomous vehicles will benefit from real-time road mapping.

• Cybersecurity protocols and real-time data monitoring


Now, let's look into the types of Big Data:
Structured data adheres to a specific data model and has a well-defined structure that follows a set of rules. It is created in such a way that a person or a machine can readily access and use it. Structured data is typically saved in databases with well-defined columns. Tables in a database management system are the greatest illustration of structured data. It’s all your quantitative data: age, billing, contact, address, expenses, and debit/credit card numbers. Because structured data is already tangible numbers, it’s much easier for a program to sort through and collect data.

Semi-Structured Data is similar to Structured Data. It shares a few qualities with Structured Data, however, the vast majority of this type of data lacks a clear structure. It does not follow the formal structure of data models such as a relational database management system (RDBMS). CSV files are the best example of semi-structured data. Most of the time, this translates to unstructured data with metadata attached to it. This can be inherent data collected, such as time, location, device ID stamp or email address, or it can be a semantic tag attached to the data later.

Unstructured data is a distinct sort of data that lacks a structure and does not adhere to the formal structural requirements of data models. It doesn't even follow a standard format. It changes regularly. Video and audio files, for example.

Characteristics of Big Data

Following are the five essential V’s of Big Data characteristics:

1. Velocity: The speed at which companies receive, store and manage data is referred to as velocity. It also has to do with how quickly the data will be processed. To deliver the most up-to-date insights, big data is received, analysed, and interpreted in rapid succession. Sensors, social media sites, and application logs generate massive volumes of data, which is constantly updated. Many big data platforms can even capture and analyse data in real-time. There's no use in devoting time or effort if the data flow isn't continuous. Finally, following analysis, the data will be able to suit the needs of the clients/users.


2. Veracity: The term "veracity" refers to how trustworthy the data is. It establishes the data's level of credibility. It's tough to cleanse, transform, match, and link data across systems because it comes from various sources. Because the data you get from these sources is unstructured, it's critical to filter out the irrelevant data and use the rest for processing. To maintain robust data quality, it is necessary to connect and correlate links, hierarchies, and information linkages. It determines executive-level confidence.


3. Volume: The tremendous amount of data generated every second by social media, cell phones, sensors, transactions, movies, logs, and other sources is referred to as volume. Previously, storing and analyzing this massive volume of data was a challenge. But now, the IT industry is moving toward the use of distributed systems to store data in multiple locations. A software system known as Hadoop collects all of the data. When determining the value of data, the volume of data is critical. It is measured in Gigabytes (GB), Zettabytes (ZB), and Yottabytes (YB). The volume of data will increase significantly in the future years, according to industry trends. In addition, the volume is useful in determining whether a collection of data is Big Data or not.


4. Value: Value is a crucial property of big data, and it is the most important problem to focus on. It's not just about the data you process and store; it's about the value of the data you store and analyse to generate relevant business insights. It comes from insight discovery and pattern recognition that lead to more effective operations, stronger customer relationships and other clear and quantifiable business benefits. It doesn't matter how quickly or how much data is generated; it has to be dependable and valuable. Otherwise, the information is insufficient for processing or analysis. Poor data can reduce an organization's income by a significant percentage.


5. Variety: One of the most essential properties of Big Data is its variety. It refers to the different forms of data and their characteristics. Structured, semi-structured, and unstructured data are the three types of data. Over time, the data sources have shifted. Previously, it could only be found in spreadsheets and databases. But now it is found in the form of photographs, audio files, videos, text files, and PDFs. Furthermore, the variety of data is one of the most pressing concerns confronting the big data sector, as it can occasionally degrade the efficiency of your application. As a result, it's critical to appropriately handle the variety of your data by arranging it.


Read more about the impact of these

Advantages

1. Analyses of data can assist analysts in learning more about client behaviour. Analysts acquire information on clients from a variety of social media channels to better serve them by understanding their behaviour.


2. Big data analytics is assisting businesses in cutting costs. Furthermore, big data tools aid in improving operational efficiency and, as a result, lowering costs.


3. There are various tools for increasing corporate productivity, including Hadoop and Spark. These tools are used by analysts to analyse large amounts of data. Furthermore, these technologies assist analysts in quickly analysing data.


4. Many large corporations are utilising big data to better align their business activities. It helps in understanding and targeting customers. They employ their analytics to enable frequent modifications to improve their business strategy and methods.


5. Big data assists organisations in making informed decisions. These choices result in improved customer service. When clients are satisfied, revenue rises.


6. It improves healthcare and public health with availability of record of patients.


7. It helps in financial tradings, sports, polling, security/law enforcement etc


Disadvantages

1. Every month, technology advances and improves upon prior iterations. Many large corporations are unable to meet the requirements for deploying these solutions. This quick change can sometimes result in a mess in the workplace.


2. Big data security is not effectively maintained. Big data necessitates a lot of storage, and data that isn't properly kept can be hacked. Every day, cybercrime occurs. Data security is jeopardised due to a lack of facilities.


3. High storage space for data, networking bandwidth for transporting data to and from analytics systems, and deploying resources to execute those analyses are all quite expensive to purchase. Furthermore, the expense of upkeep is extremely expensive.


4. The quality isn't always consistent due to its structure. For this reason, data scientists and analysts must ensure that the information gathered is precise and correct. If these kinds of problems continue to exist, a problem will arise. In this instance, the insights are useless.


5. Big data analysis violates principles of privacy.


6. It can be used for manipulation of customer records.


7. It may increase social stratification.


8. Big data analysis is not useful in short run. It needs to be analyzed for longer duration to leverage its benefits.


Real-World Examples

1. Banking Sector: Big Data is being used by the Securities and Exchange Commission (SEC) to monitor financial market activities. They're currently catching unlawful trading activities in the financial markets via network analytics and natural language processors.


2. Education Sector: The University of Alabama has over 38,000 students and a massive amount of information. Administrators may employ analytics and data visualisations to uncover trends in this data, which will transform the university's operations, recruitment, and retention efforts.


3. Healthcare Sector: The University of Florida used free public health data with Google Maps to develop visual data that enables faster identification and analysis of healthcare information, which is used to track the spread of chronic disease.


4. Government Sector: The Food and Drug Administration (FDA), which is part of the US Federal Government, uses big data analysis to find patterns and relationships in order to identify and investigate food-borne diseases that are predicted or unexpected.


5. Transportation: Governments use Big Data to predict traffic conditions for traffic control, route planning, intelligent transportation systems, and congestion management.


6. Entertainment and Media: Spotify, an on-demand music platform, uses Big Data Analytics to collect data from all of its users across the world, analyse the data, and then provide intelligent music choices and suggestions to each user. Additionally, Netflix, which provides movies and television shows in one convenient location, is also a huge user of big data.


Conclusion

Big data is the driving force behind important sectors such as business, media, marketing, sales, healthcare, analytics, research, and so on, as you discovered in this article. It has influenced the business strategies of both customer-based and product-based businesses all over the world. You also encountered the crucial V's that describe big data. The ability to manage large amounts of data has a variety of benefits and drawbacks. As a result, it's critical to handle and apply Big Data Analytics wisely in order to obtain useful income outcomes.