This article is about Data Science
Big Data – Definition, Features, Advantages & Applications
By NIIT Editorial
Published on 21/08/2020
It’s easier to understand the role data plays in running the world when you see basic smartphone apps requesting permission to collect and use personal data. But that wasn’t always the case. The data revolution you see around you, with phrases being thrown around such as data is the new oil, birthed out of the digital revolution of industries at once. The commercialization and user reach of computers and smart devices made archiving information on paper, illogical.
As a result, rose from the scientific horizon data centers that offered to collect the humungous volume of data being generated everywhere. The sheer size of the phenomenon helped coin the term, Big Data and this beginner’s guide will touch upon what Big Data exactly is, its applications and benefits.
What is Big Data?
Big Data refers to the data that is everywhere. In every action you do online, from making a google search to surfing through eCommerce sites, you leave a trail of data behind that businesses pick up to understand user behaviour. This could include information such as your IP address, email, etc., that helps businesses identify you online and track your behaviour.
Characteristics of Big Data
Yet, not all data is Big Data. There are special characteristics that are associated especially to Big Data, suggested first by Gartner. They are referred to as the 3 V’s of Big Data.
Let us take the above example of you browsing through an eCommerce website. If you are a frequent shopper, the business can predict your future shopping choices based on your purchase history. Now multiple that by a factor of thousands, denoting other customers like yourself. To give you an idea of the volume, data analysts need this or even bigger volumes of data to make sense of. That’s how big Big Data is. Operating on terabytes (1000 GB) or even petabytes (1000000 GB) of data is an everyday thing for data scientists.
It refers to the speed with which data is produced and processed. The faster you can collect, organize, and process data to draw value from the information, the higher the quality of the Big Data. The velocity factor of Big Data also considers the multiple sources from which the data flows into the system. In the above example, users could be pouring in from multiple platforms and referral links onto the eCommerce website. Add to that the time at which they finally placed the order, what is the dispatch location, how much price for, etc. etc. The more real-time your insights from such data, the faster you would be able to monetize the data.
The database system must be capable of handling multiple types of data and not just the structured and unstructured data. Add to the list audio, video formats, textual data, numeric data, and transactional data in the name of options. Each type of data needs a different treatment be it storing or processing. Traditional databases simply cannot handle the diversity of such data formats, making Big Data unique in this department.
Types of Big Data
It can be classified into 3 categories:
This type of data is stored in the database in an ordered manner. The database is stylized to store such data in a pre-defined format. In simple words, structured data is the kind that can be stored, understood, and computed in a fixed format. Its characteristics stay the same and only the values of the field change in the database tables. For instance, common identifiers for our average e-commerce website user would be a name, email, phone number, with the format for such fields being text, alphanumerical, and numerical respectively staying the same.
Data whose format can’t be known in advance is referred to as unstructured data. Imagine huge volumes of intermixed formats of data that you have to derive insights out of. Sounds tough because it is. A simplistic example would be the results displayed by search engines for a user query. It includes a mixture of images, audio, and video files.
When you mix both the structured and unstructured data, you get the semi-structured version of it. This type includes data that although may have a structure but does not necessarily adhere to a data model.
Advantages of Big Data
- Businesses are increasingly becoming consumer-facing due which the importance of real-time fact-checking and dispute resolution becomes massive. Social media is a ventilator for customers to spell out their differences with brands. Big Data can aid in reputation management. Imagine an irate customer who is going haywire on Facebook for sub-par customer service. With Big Data processing systems, you can use their name to identify the purchased item and the incident related to it, making you appease the person.
- Revenue generation can be optimized with Big Data analytics. The software allows project stakeholders to see for themselves the impact of their strategies on sales. If things don’t work out the way they would’ve liked to, then experimental numbers can be first processed by Big Data analytics systems to forecast in advance the potential revenue that could be generated.
- If customers are unique so should be their experiences on your website. Though there are many components of the website that have to be kept static, yet the information displayed to the consumer can be unique and help you better your impression in their mind. Remember the last time you logged into your e-commerce account? You are recommended items based on your purchase history. That’s Big Data at work.
- Manufacturing enterprises can make use of Big Data by running an analysis on the shelf life of its machinery. Doing so would help ground managers be decisive about substituting older hardware with new ones precisely as foretold by Big Data.
- Health Technology is making strides in cutting down on experimental treatment. The massive stockpile of patient records, categorized as Big Data sets and then analysed, can help doctors prescribe the most relevant course of treatment, leading to higher recovery rates.
- Most importantly, Big Data can be used to secure critical information that is counted as the intellectual property of the business. You can feed the program key pointers to look for, simulating which, it can point to what proportion of the company data is truly secure.
Applications of Big Data
The following are live examples of how industries are making use of this technology.
Banking, Financial Services, and Insurance industry (BFSI) face everyday challenges like identity theft, securities fraud, and card fraud among others. Big Data is being implemented for real-time fraud detection. Financial firms are using it to forecast price spikes/falls and make a handsome profit in trading stocks.
The Media and Entertainment Industry has desperately sought real-time solutions for understanding what ticks user interest in particular artists. This holds all the more true in the digital age where streaming direct to consumer (D2C) is the biggest trend. Streaming businesses such as Spotify and Amazon Prime use Big Data for sentiment analysis and recommending material akin to the liking of users.
Education is one business that will always stay in fashion. With thousands of students renewing institutional databases annually, how can we leverage Big Data? One of the many solutions possible could be to use it and understanding student enrolment patterns. Knowing exactly the kind of student-profile the school/college attracts, marketing strategies could target the user base effectively and increase ROI.
The Government of all has the best-case scenario for using Big Data having an unmatched repository of citizen information. From paper-work to digital enrolments in government-sponsored programs, Big Data can churn those most likely to lie on their applications. It can extrapolate those most likely to lie about their tax records in process defrauding the system. From Income Tax to Human Resource departments Big Data applications are limited by but imagination.
The Retail Industry applies Big Data to know customer preferences for on the shelf products as a case. Having proven information on customer favourites helps retail managers optimize limited space and showcase brands with the best return on investment.
Big Data has proven a boon for every industry that has adopted it. To build a foundational setting for becoming a Big Data Engineer, you must have strong knowledge of programming languages like Python, R, and Java. Take your first step towards learning Big Data implementation with the following programming courses:
Python Programming and Data Exploration in Python
Get Ready for New age job roles by learning the programming language that is most popular for Data Analytics – Python.
Most popular programming language
Cutting Edge Curriculum