BIG DATA- WHAT IS SO BIG ABOUT IT?

• BIG DATA

Big Data basically refers to, huge volume of data that cannot be, stored and processed using the traditional approach within the given time frame.The next big question that comes to our mind is?

How huge this data needs to be? In order to be classified as Big Data?

There is a lot of misconception, while referring the term Big Data. We usually use the term big data, to refer to the data, that is, either in gigabytes or terabytes or petabytes or exabytes or anything that is larger than this in size. This does not defines the term Big Data completely. Even a small amount of data can be referred to as Big Data depending on the context it is used.

•    TYPES OF BIG DATA

Big Data could be found in three forms:

1. Structured
2. Unstructured
3. Semi-structured

Structured data:-Any data that can be stored, accessed and processed in the form of fixed format is termed as a structured data.

Unstructured data:- Any data with unknown form or the structure is classified as unstructured data. In addition to the size being huge, un-structured data poses multiple challenges in terms of its processing for deriving value out of it.

Semi-structured data:- Semi-structured data can contain both the forms of data.

•     USES OF BIG DATA

Location tracking :-  

 Logistic companies have been using location analytics to track and report orders for quite some time. With Big Data in the picture, it is now possible to track the condition of the good in transit and estimate the losses. It is now possible to gather real-time data about traffic and weather conditions and define routes for transportation. This will help logistic companies to mitigate risks in transport, improve speed and reliability in delivery.

Advertising :-

Advertisers are one of the biggest players in Big Data. Be it Facebook, Google, Twitter or any other online giant all keep a track of the user behaviour and transactions. These internet giants provide a great deal of data about people to the advertisers so that they can run targeted campaigns. Take Facebook, for example, here you can target people based on buying intent, website visits, interests, job role, demographics and what not. All this data is collected by Facebook algorithms using big data analysis techniques. The same goes for Google, when you target people based on clicks you will get different results and when you create a campaign for leads that you will get different results. All this is made possible using big data.

Entertainment and media:- 

In the field of entertainment and media, big data focuses on targeting people with the right content at the right time. Based on your past views and your behaviour online you will be shown different recommendations. This technique is popularly used by Netflix and YouTube to increase engagement and drive more revenues.

Pros and cons of big data

PROS:

• Fraud detection: Another common use for big data analytics — particularly in the financial services industry — is fraud detection. One of the big advantages of big data analytics systems that rely on machine learning is that they are excellent at detecting patterns and anomalies. These abilities can give banks and credit card companies the ability to spot stolen credit cards or fraudulent purchases, often before the cardholder even knows that something is wrong.

• Savings – Even though implementation of real-time big data analytics can be expensive, the high value of immediate data analysis can make up for this expenditure.

• Strategies toward competitors – Competition scares many people in the market today, and big data analytics assists in providing a detailed picture of competitors, such as launching a new product, lowering/increasing prices for a particular duration or focusing on users from a specific location.

CONS:

·       Privacy:- De-identification or the process of anonymizing data by removing personally identifiable information as a way of justifying mass collection and use of personal data. Big Data can be compliant with the existing regulatory and legal frameworks on data protection.

·       Security:-The larger the quantities of confidential information stored by a certain company in their databases would specifically attract potential hackers. Also, it is a duplication of data to many locations to optimize data queries processing that may result in difficulty in locating and securing all items and personal information.

·       Distraction:At times, the Data Visualization apps create reports and charts laden with highly complicated and fancy graphics, which may be tempting for the users to focus more on form than on function.

 The Future of Big Data:

 

·        Privacy Will Be the Biggest Challenge.

·        Data Scientists Will Be In High Demand.

·        Businesses Will Buy Algorithms, Instead of Software.

·        More Developers Will Join the Big Data Revolution.

·        Big Data Will Help You Break Productivity Records.

Thank You for Visiting Techtical .That was all about Big data. Let us know in the comments section about your thoughts on Big Data. Stay tuned for more interesting blogs on popular topics.