Big data means lots of data and gets generated through many sources, a few of them are described below:
One of the sources is the social media where one can post comments, opinions, and experiences or just about anything that a person is feeling or would like to share. This is available to closed group of friends or to everyone all across the world based as desired by the person sharing. Even language is no longer a barrier and translators exist which transliterate or translate the content and one can read and communicate globally without any sort of barrier. The social media explosion like blogs, face book, you tube, twitter, linked-in etc have caused constant generation of data in large volumes and contributes to creation of big data.
Another source of generating big data is when we start collecting granular information in our enterprise transactions and correlate enterprise data with each other and interpret them as events. For example in an electric metering system we may be taking sample readings every hour or 10 minutes instead of bi monthly reading, we also collect additional information on whether that unit of consumption was for a wash load, a microwave oven or for lighting, heating or cooling. In a retail ecommerce site we may collect every item purchased in a transaction in all details as well as do location correlation with demographics of the city or town as well as weather to see if certain demographic patterns or weather contribute to the purchase. We may also do basket analysis to see which set of products are purchased by typical customer together in a single shopping trip. We may also collect globally all data in real time in a central location or we may offer a special product or a package to every individual in a customized way instead of having a set of products distributed by demography and category of customers or prospects. Correlating data with other data and tracking with time as events are another source of big data generation.
In addition to above there is additional information available about the customer location as mobile devices become more prevalent and if we track the location of each customer, that I self could generate a lot of information.
To be able to leverage BIG Data as generated by means above, one needs to have very fast number crunching engines or computers as well as very fast storage and retrieval media. This is facilitated these days through very large static memory computers as well as cluster and cloud computing with huge amount of processing power which allows large volumes of data to be crunched in real time with algorithmic routines. Also large bandwidth network infrastructures allow data to be carried from distant place to central location and back in real time.
One could leverage the big data and huge volume crunching capabilities in real-time to enhance business value, increase customer satisfaction and grow revenues. Many businesses are leveraging these new technology tools in innovative ways. Some of the examples are as below:
1. Imagine one driving on a highway and using a navigator like Navi or Tom-Tom device and you search for a nearest fine dine restaurant. If the restaurant could detect your search and location, it could send a special invite to you and family thereby getting your additional revenue. It is important to find this opportunity in real time, otherwise if you could only get this an hour later you could be 60 miles away from the location and information will not be useful.
2. Imagine after a flight you being told that unfortunately that your bag did not make it and airline apologizes for the same and tells you that you will have your bags the next day at the location of your choice. You don’t have to wait at the baggage belt, wait for all the bags to arrive and then find that your bag did not make it and file a baggage lost report. Why should you have to go through the ordeal if the baggage is also tagged and passenger is also tagged and both are checked for boarding? It is just that the data of baggage is not event co-related with the data about passenger while information of both exist in the airlines systems. Better still would be to detect proactively and ensure very high percentage of baggage boarding the same flight as the passenger so the incidents of misplaced bags are reduced or eliminated.
3. Imagine in a fashion retail / or household electronics/ video games store during holiday sales period, one knows of many instances where people keep visiting every day to see how much the price has come down and whether it is sufficiently less to buy at the stage. Leaving the goods in store has a risk of it getting sold to someone else before you decided to buy the same. If there was a provision to offer your price at which you are ready to buy as an individual offer, you may not visit the store every day to check, also the store could see all the offers and decide to procure more quantities of maximize its revenue from all available offers.
There are countless such examples where big data with real-time analytics can be used in business to enhance customer experience and maximize business revenue.