What's Up Big Data?

Have you ever wondered how a shopping app or the website say eBay or Myntra, suggest the products you actually like? 

It really is mind-blowing! This system of recommendation is really helpful for searchers or potential customers. 

But do you know the latent the magic behind the trick of these suggestions?

Let's reveal the unknown masterpiece behind the same, it is called BIG DATA ANALYTICS. 

Does the term sounded too technical and made you perplexed? Don't worry, let this article help you solve this tricky puzzle.

WHAT IS BIG DATA?


Big data is described as a large volume of data. It is not bounded by a specific type, it can be structured or unstructured data. What is important is, how someone uses stored instructive data and statistics. It is crucial for a person to dig the best of the factual content and put the same to build a constructive conclusion. 

To put the same in easy words, “big data can be referred to a data which is- large, fast, complex, difficult or impossible to store by the use of traditional methods say record books.

The concept was popularised when an industry analyst Doug Laney articulated the now-mainstream explanation of big data via the logic of the three V’s:

Volume: 

An Organization piles up data by the use of various sources. To name a few- Business transactions, Smart (IoT) devices, industrial equipment. Storing data has always been a hectic process. There were few apps that had significantly reduced the burden of the above problem.  For example- data lakes and Hadoop 

Velocity: 

The Internet is a magical book, where you can get an answer to anything. It has thus, become a required need to manage the same. 

FACT- data streams in businesses have increased at an unprecedented speed. Which arose the need for handling the same.

In addition, RFID tags, sensors and smart meters are pushing towards the value to deal with huge torrents of data in near-real-time.

Variety: 

As discussed before, data isn't classified or narrowed down to any type or form. It exists in all variety of formats - Structured, Numeric data in traditional databases, Unstructured text documents, Emails, Stock ticker data, financial transactions


INDUSTRIES AND BIG DATA 

Medicine

This the industry relies on big data to specialize in the equipment needed to track vital signs, coordinate with procedures, and diagnose the disease/ infection. The analytics tools are a source of great assistance in improving health in many ways.

For example- public health departments critically analyze the data and analytics in order to prioritize food safety inspections which are at-risk facilities. Researchers keep on scrounging to find data that can reveal the places with the most significant disease patterns. The analysis stands as a pillar to hospital managers in reducing waiting times and works on care facilities. The patterns drawn prescribe recommendations as to progress with the procedure.

Construction

Construction firms make a plan which includes expenses on materials, the average time for completing the specific task, and much more. It can be clearly seen, how data analytics has become a vital part of the industry. Moreover, construction professionals also consider the field service metrics- Attrition, Lifetime values of clients, Rates, and revenue The result -  a clear portrayal of what metric would work and which service needs improvement. 

Various projects have the incorporation of sensors in buildings and bridges. It helps the accessories to store data and revert the same to people for analysis.


PROS

Storage: It has provided an option to store all kinds of data in a manageable and informative manner. The same covers fields such as financial trading, sports, polling, law enforcement, etc.

Fraud Detection: The analytics feature of big data which is processed through machine learning has become excellent at detecting and analyzing various patterns and anomalies. 

Example- Banks are able to keep insight into the payments or any action taken via credit cards or any other cards. The banks can detect if any fraudulent transactions are done by the stealer of the cards. Thus, it has helped in protecting the owner who possesses the ownership over the money.

Efficient and Cost SavingsTools such as Hadoop and cloud-based analytics are knowns as eco- cost savers. One can save large chunks of data and also enjoy the benefit of suggestions by tools for doing a task in an efficient manner.

Analytics as a Boon: The analytics derives- an innovative solution, Understand and target customers, Optimization of business processes, improve science and research improves healthcare and public health as a record of patients can be stored.


DISADVANTAGES

Correlation Errors: The analytics process relies heavily on linking correlation to one variable to another as to draw a pattern. It is not possible that every interconnection drawn is righteous and informative. Thus, a wrong conclusion results in a bad decision.

Need for skilled human resources: IT field is run by expert scientists who are among the most coveted and highly paid workers. There is a need for skilled workers, for the same the cost of training, hiring, and acquiring skilled staff increases.

Need for security: The analysis can violate principles of privacy, the same can be used to manipulate customer records. Thus, resulting in an increase in social stratification. The analysis is useful when data is analyzed for a longer duration to leverage its benefits. It can result in misleading information as well.


TECHNOLOGY OF BIG DATA

Operational Big Data Technologies

It takes account of data generated on a daily basis. It stores online transactions, social media, or any kind data of data that can be used from a particular firm for the purpose of analysis by data technologies based software. The data is considered as raw information and is stored by Analytical Big Data Technologies.

For example- The Operational Big Data Technologies such as executives’ of any MNC, online trading, and purchase via Flipkart, Walmart, etc.

Analytical Big Data Technologies 

It is about the advanced adaptation of Big Data Technologies which bit complex. It means that in comparison to the former type discussed above.

For example - stock marketing, weather forecasting, medical-health records, etc


EXAMPLES OF TECHNOLOGIES OF BIG DATA

R Programming

It is a programming language with an open-source project. The services of this software can be enjoyed without any cost i.e. for free. The same is mainly used for the metrics of statistical computing, unified developing environments like Eclipse and assistance to Visual Studio communication. It has been revealed by experts that, it has tremendously brought a change in the most prominent language across the world. Its beneficial services are also availed by data miners and statisticians. They use it for the implementation of designing statistical software and in data analytics.

NoSQL Database

It is ranked under the range of separate database technologies which is primarily focused on the development of the design of modern applications.  It shows a non SQL or no relational database which helps to deliver methods for accumulation and retrieval of data.  It aims at storing the unstructured data and delivering a fast pace performance. It proffers flexibility when it is about to deal with all sub-types of datatypes at a huge scale. Example- MongoDB, Redis, et cetera. The highlight is the integrity of design, ease control, uses data structures, quick computations NoSQL. For example, Twitter stores terabytes of user data every single day. 

A recent survey made a statement which said-

"Fortune 1000 companies have the potential

 to gain more than $65 million

in addition to their respective net income,

just by an increase of 10%

in their data accessibility." 

This fact is quite shocking and shows how big data can help various industries. It can lead to a chain of revolution in the way data is stored. Moreover, it can have an impact on the economy of the world.

Writer: Ishita Gupta

Editor: Sonal Kamble

 

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