We hear more and more often talk about Big Data and how they are changing the approach of companies of different sectors.
The amount of data generated today is abnormal: phones, credit card used for purchases, television, the necessary storage for computer applications, smart urban infrastructure, sensors on buildings, public and private transport and so on.
The data is generated with such a growing flow that all the information accumulated over the last two years has surpassed the order of the Zettabytes (1021 bytes), marking a record for human civilization.
However, the huge amount of information is not the real revolution about Big Data, but the ability to use all this information to process, analyze and find objective evidence on different themes.
The true meaning of Big Data
The Big Data revolution and, in general, the term Big Data refers precisely to what can be done with all this amount of information, that refers to the algorithms able to treat so many variables in a short time and with few computational resources.
Let’s see a comparison: until recently, to analyze a mountain of data that we would now define Small or Medium Data would a scientist have needed a long time and would have used mainframe computers of over 2 million dollars. Today, with a simple algorithm, the same information can be processed within a few hours, perhaps using a simple laptop to access the analysis platform.
And the beauty is that Big Data is not just about the IT industry. In fact, if Information Technology represents for Big Data the great starter from which to start with the necessary tools such as cloud computing, search algorithms and so on, on the other hand Big Data are necessary and useful in most business markets: from automobile, to medicine, from commerce to astronomy, from biology to pharmaceutical chemistry, from finance to gaming. No industry where there is marketing and data to analyze can be unscathed by the Big Data revolution.
Examples of Big Data in everyday life
This revolution touches the lives of every single person without anyone noticing.
Here are some examples of what Big Data is capable of.
In marketing, the use of Big Data is familiar in the construction of so-called recommendation methods, such as those used by Netflix and Amazon to make purchase proposals based on a client’s interests compared to those of millions of others. All data coming from a user’s browsing, from his previous purchases, from products evaluated or researched, allow the trade giants (electronic or not) to suggest the most suitable products for the client’s purposes, those that tickle his curiosity and push him to buy by momentary necessity, permanent or by simple impulse. The Big Data algorithms can predict if a female shopper is pregnant, tracing her research on the Web and the objects previously acquired, such as lotions and so on. Once the particular status has been identified, the same user is offered special offers and coupons on products related.
In the public sphere, there are many other types of Big Data applications:
- the dispatching of police forces where and how much crimes are more likely to occur;
- the study of associations between air quality and health;
- genomic analysis to improve the drought resistance of rice crops;
- the creation of models to analyze data coming from living beings in the biological sciences;
and so much more.
A Big Data problem, sharing information
Although everything seems so simple, the evolution of Big Data is not so close to humanity. The preeminent obstacle to overcome is the distrust of companies, research centers and some scientists to share data on which Big Data could work.
Thus, the field of medicine represents the one where perhaps there is the greatest waste of data and the worst consequences: despite the means provided by Big Data, millions of people continue to die every day, partly because the data are not shared.
Once the mistrust and ignorance in this sense are overcome, Big Data can best support the collection, classification, analysis and synthesis of data in a given sector, offering valuable information that goes beyond simple raw data.