Home Big Data What is Big Data why is it so popular and buzzwords?

What is Big Data why is it so popular and buzzwords?

by arsathnatheem
BigData - DataScieneceForu.Com


The term “big data” refers to the large volume of structured and unstructured data that inundates a business every day, Data volume isn’t the most important factor, Information is only as good as the use organizations make of it, data collected by organizations typically consists of structured data, semi-structured data, and unstructured data, Data that can be mined for information and used for machine learning projects, predictive modeling, and other advanced analytics

With big data, it is possible to gain insights that help make better decisions and move the business forward.

History of Big Data

The idea of big data originally emerged in the 1960s and 1970s when data centers and relational databases were just emerging as the world of data was just getting started.

By 2005, it was becoming increasingly clear how much information was being generated by Facebook, YouTube, and other online services. This was also the year that Hadoop (an open-source software platform for storing and analyzing large data sets) was developed. It was also during this time that NoSQL became popular.

As big data has exploded, open-source frameworks including Hadoop (and more recent Spark) have been paramount to producing data that is easier to work with and more cost-effective to store. There has been an exponential growth in big data since then. Despite all the data that humans are generating, computing companies are also generating a huge amount of data.

The Internet of Things (IoT) makes it possible for objects and devices to be connected to the internet, allowing for a better understanding of customer usage patterns. As a result of machine learning, even more data has been created.

Big Data: Evolution

The first data collection methods can be traced to ancient civilizations that used stick tallies to track food, but the history of big data actually begins much later. An overview of some of the important moments leading up to the present day is provided below.


  • It was the 1880 census, which was one of the first to experience data overload. It takes ten years to process census data before the Hollerith Tabulating Machine reduces that to under a year.


  • Pfleumer paves the way for digital data storage in the next century by developing magnetic data storage on tape.


  • A foundation is laid for today’s information infrastructure with Shannon’s Information Theory.


  • Edgar F. Mathematics professor Codd introduces a “relational database” that demonstrates how to extract information from vast databases without being aware of their structure. A specialization or extensive knowledge of computers used to be required for this.


  • The use of Material Requirements Planning (MRP) systems for commercial purposes is developing to help organize and schedule information and catalyze business operations.


  • Tim Berners-Lee was the creator of the World Wide Web.


  • In his paper, Doug Laney outlines three elements of big data that comprise the “3 Vs of Data”. It was also during that year that the term “software-as-a-service” was first mentioned.  


  • A framework for large dataset storage was created called Hadoop, which is open-source software.


  • An article by Wired is the first to speak of “big data.” It’s a topic introduced to the masses as the “data deluge makes science obsolete.”


  • Developing breakthroughs in Commerce, Science, and Society with Big Data Computing is the latest paper published by computer science researchers, The paper describes how big data is fundamentally changing how business is conducted.


  • According to Eric Schmidt, Google CEO, people create more information every two days than people did from the dawn of civilization to 2003.


  • As companies migrate to the cloud, they are using Enterprise Resource Planning Systems (ERP).
  • An estimated 3.7 billion connected things are in use today, transmitting huge amounts of information daily. This is an example of the Internet of Things (IoT) becoming widely used.


  • President Obama formally announces the “Federal Big Data Research and Strategic Development Plan,” aimed at driving the research and development of societal and economic applications that directly benefit big data.


  • An IBM study reveals that 2.5 quintillion bytes of data are created each day and that 90% of the world’s data was generated within the last two years. 


  • A CAGR of 10% is forecast for the big data market in 2020, which will surpass $76 billion. Analysts expect big data and business analytics to surpass %210 billion in two years. The big data sector is expected to generate $123.2 billion in revenue by 2025. A number of industries will also accelerate their use of the technology.

Growth of Unstructured Big Data 2030 Prediction

It has come a long way, but big data still has a ways to go before it proves useful. Big data has been expanded even further thanks to cloud computing. As a result, developers have access to truly elastic scalability, where they can create ad-hoc clusters to test subsets of data on-demand. As well as graph databases, which quickly and comprehensively generate analytics based on huge amounts of data, they are becoming increasingly important.

What are 3Vs in Big Data?

7Vs of Big data

As a result, big data management systems, tools, and analysis support have become common components of data management architectures in organizations, There are Multiple Vs Described in Big Data, Here we explained Three Essential Vs are often associated with big data: veracity, value, and velocity.

  1. Managing large volumes of data across many environments;
  2. Big data systems store a wide variety of data types; and
  3. In many cases, data is generated at a high velocity, acquired and processed.


Applications Of Big Data

Following are some examples of industries where the big data revolution has already begun:

A wider range of applications

  1. The advertising and marketing industry
  2. Entrepreneurship
  3. Online shopping and retail
  4. Sports 
  5. The education system
  6. The internet of things
The Financial sector

Among other uses of big data and predictive analytics in finance and insurance are fraud detection, risk assessment, credit scoring, brokerage services, and blockchain technology.

Big data is also being used by financial institutions to enhance cyber security and make financial decisions that are more personalized for their customers.

The healthcare industry

Big data solutions are being adopted by hospitals, researchers, and pharmaceutical companies to increase the efficiency and effectiveness of healthcare.

Thanks to the wealth of patient and population data available in healthcare, patients are receiving better treatment, performing better research on diseases such as cancer and Alzheimer’s, developing new drugs, and gaining valuable insights into trends within population health.

The media & entertainment industry

Streaming services like Netflix, Hulu, and other services that guarantee recommendations have implemented big data. 

To provide tailored experiences, media companies analyze the habits of readers, viewers, and listeners. In addition to graphics, titles, and colors, Netflix uses information about customer preferences to determine what to display.

Agricultural Sector

In the farming industry, automation and big data are enhancing the process of engineering seeds and predicting yields with impressive accuracy.

Researchers and scientists use big data to tackle hunger and malnutrition in many countries thanks to the explosion of data over the past two decades. There is some progress being made in the fight to end world hunger as global groups such as the Global Open Data for Agriculture & Nutrition (GODAN) promote open and unrestricted access to agricultural and nutrition data around the world.

Leave a Comment