What is Data?

The quantities, characters, or symbols on which operations are performed by a computer, which may be stored and transmitted in the form of electrical signals and recorded on magnetic, optical, or mechanical recording media.

What is Big Data?

Big Data is also data but in this case, the data is  huge in size. 

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.


The importance of big data doesn’t revolve around how much data you have, but what you do with it. You can take data from any source and analyze it to find answers that enable you to do the following:

  1. cost reductions
  2.  time reductions
  3. new product development and optimized offerings
  4. smart decision making. When you combine big data with high-powered analytics, you can accomplish business-related tasks such as:
  1. Determining root causes of failures, issues and defects in near-real time.
  2. Generating coupons at the point of sale based on the customer’s buying habits.
  3. Recalculating entire risk portfolios in minutes.
  4. Detecting fraudulent behavior before it affects your organization.

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Examples Of Big Data

An example of big data are (1,024 terabytes) or exabytes (1,024 petabytes) of data consisting of billions to trillions of records of millions of people—all from different sources.

  1. Stock exchange: The New York Stock Exchange generates about one terabyte of new trade data per day.
  1. Social Media: The statistic shows that 500+terabytes of new data get ingested into the databases of social media site Facebook, every day. This data is mainly generated in terms of photo and video uploads, message exchanges,comments.
  1. Jet: A single Jet engine can generate 10+terabytes of data in 30 minutes of flight time. With many thousand flights per day, generation of data reaches up to many Petabytes.

This datas is typically loosely structured data that is often incomplete and inaccessible.


  1. Volume – The name Big Data itself is related to a size which is enormous. Size of data plays a very crucial role in determining value out of data. Also, whether a particular data can actually be considered as a Big Data or not, is dependent upon the volume of data. Hence, ‘Volume’ is one characteristic which needs to be considered while dealing with Big Data.
  1. Variety –The type and nature of the data. This helps people who analyze it to effectively use the resulting insight. Big data draws from text, images, audio, video; plus it completes missing pieces through data fusion.

The next aspect of Big Data is that Nowadays, data in the form of emails, photos, videos, monitoring devices, PDFs, audio, are also being considered in the analysis applications. This variety of unstructured data poses certain issues for storage, mining and analyzing data.

  1. Velocity – The term ‘velocity’ refers to the speed of generation of data. How fast the data is generated and processed to meet the demands, determines real potential in the data.Big Data Velocity deals with the speed at which data flows in from sources like business processes, application logs, networks, and social media sites, sensors, Mobile devices, etc. The flow of data is massive and continuous.
  1. Variability – This refers to the inconsistency which can be shown by the data at times, thus hampering the process of being able to handle and manage the data effectively.

Big Data Applications:

The fields where big data is being used are as follows.

  1. Education: Education industry is flooded with huge amounts of data related to students, faculty, courses and results. The proper study and analysis of this data can provide insights which can be used to improve the operational, effectiveness and working of educational institutes.

Here are some of the fields in the education industry that have been transformed by big data;

  1. Customized Learning: ProgramsCustomized programs and schemes is used to benefit individual.students schemes can be created using the data collected on the bases of each student’s learning history. This improves the overall student results.
  2. Grading Systems:New advancements in grading systems have been introduced as a result of a proper analysis of student data.
  3. Career Prediction:Appropriate analysis and study of every student’s records will help understand each student’s progress, strengths, weaknesses, interests, and more. It would also help in determining which career would be the most suitable for the student in future.
  4. The applications of big data have provided a solution to one of the biggest pitfalls in the education system, that is, in e-learning solutions.ExampleThe University of Alabama has more than 38,000 students and an ocean of data. In the past when there were no real solutions to analyze that much of data, some of them seemed useless. Now, administrators are able to use analytics and data visualizations for this data to draw out patterns of students revolutionizing the university’s operations, recruitment, and retention efforts.
  5. Social Media and entertainment industry:With people having access to various digital gadgets, generation of large amount of data is inevitable and this is the main cause of the rise in big data in media and entertainment industry.Other than this, social media platforms are another way in which huge amount of data is being generated. Although, businesses in the media and entertainment industry have realized the importance of this data, and they have been able to benefit from it for their growth. 

Benefits extracted from big data in the media and entertainment industry are given below:

  • Predicting the interests of audiences
  • Getting insights from customer reviews
  • Effective targeting of the advertisements Example:Amazon Prime that offers, videos, music, and Kindle books in a one-stop shop is also big on using big data.
  1. Technology:The technological applications of big data comprise of the following companies which deal with huge amounts of data every day and put them to use for business decisions as well. For example, eBay.com uses two data warehouses at 7.5 petabytes and 40PB as well as a 40PB Hadoop cluster for search, consumer recommendations, and merchandising. Inside eBay‟s 90PB data warehouse. Amazon.com handles millions of back-end operations every day, as well as queries from more than half a million third-party sellers. The core technology that keeps Amazon running is Linux-based and as of 2005, they had the world’s three largest Linux databases, with capacities of 7.8 TB, 18.5 TB, and 24.7 TB. Facebook handles 50 billion photos from its user base. Windermere Real Estate uses anonymous GPS signals from nearly 100 million drivers to help new home buyers determine their typical drive times to and from work throughout various times of the day.
  2. Big Data in Weather Patterns:There are weather sensors and satellites deployed all around the globe. A huge amount of data is collected from them, and then this data is used to monitor the weather and environmental conditions.

All of this data collected from these sensors and satellites contribute to big data and can be used in different ways such as:

  1. In weather forecasting
  2. To study global warming
  3. In understanding the patterns of natural disasters
  4. To make necessary preparations in the case of crises.

Benefits of Big Data Processing

Ability to process Big Data brings in multiple benefits, such as-

  1. Businesses can utilize outside intelligence while taking decisions. Access to social data from search engines and sites like facebook, twitter are enabling organizations to fine tune their business strategies.
  2. Improved customer service- Traditional customer feedback systems are getting replaced by new systems designed with Big Data technologies. In these new systems, Big Data and natural language processing technologies are being used to read and evaluate consumer responses.
  3. Big Data technologies can be used for creating a staging area or landing zone for new data before identifying what data should be moved to the data warehouse. In addition, such integration of Big Data technologies and data warehouses helps an organization to offload infrequently accessed data.