Here are some more examples of different sources of big data, and how you can analyse them with respect to the four Vs.
Social media
There are millions of people using Facebook and Twitter.
All the data is produced in an online fashion arriving in the form of a data stream. Users post a variety of data online on Facebook, such as text, images, videos. Similarly, Twitter has short text messages. The data is high volume and arrives with high velocity at the Facebook/Twitter servers. Users may be tagged by their location using GPS coordinates. These coordinates are usually imprecise leading to veracity of the data.
Fraud detection in banking transactions
Banking produces millions of transactions per day. These transactions have to be processed safely and reliably. Thinking about a bank’s transactions over a month results in a vast volume of data.
Fraud detection refers to finding bogus transactions that have been triggered by criminals, such as using stolen credit card details. This can be by using a stolen credit card or even only its details. You see that for fraud detection you would have to deal with large volumes of data, each transaction arriving rapidly, and a decision having to be made as soon as a transaction arrives.
There are some indicators that can be used to identify fraud, for example a credit card used at an ATM in one country when all other transaction in the previous 2 days have been in another country.
Finding frauds is hard and the information used to stop a transaction is usually not 100% reliable. You might even have observed this yourself when you tried to use your credit card in a different country and the card was rejected although you were the legitimate user of the card.
Skype
There are millions of people using skype. Skype offers various types of communication: text, voice call, video call. It’s possible to send various different types of data into the text messages (pdf files, images, videos, etc). So there is a lot of variety in their data.
At any given moment millions of users from various locations around the world can be using skype. Doing so, they produce a high volume of data that is arriving at the skype server rapidly.
Online stores
Online stores such as Amazon have millions of potential customers that buy a large variety of items from their online servers. These customers produce a very large number of transactions within a short time period.
Mining these transactions to extract useful information (for example to optimise advertising) has to deal with the large amount of users and the variety of items that they have bought. Making a recommendation to one particular user takes into account what the user has bought so far.
The knowledge gathered about a customer is incomplete and a recommendation system has to rely on the imprecise information that it can obtain from the transaction data of customers and their behaviour on the online store page.
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