Big Data, what is all the fuss about?

Recently, you may have heard about Big Data in a thousand and one places. Perhaps you wanted to get informed, and what you read left you with the same doubts. Or maybe even more, due to the excessively technical language of some articles or reports.

At this point, it is clear that it is something more than a passing trend and that every company or organization of a certain size can benefit from its advantages.

It’s time for you to know and, above all, understand well what Big Data consists of and its pros and cons. Are you ready?

What Big Data Consists Of

We will later go into somewhat more "scientific" descriptions, but surely with this cinematic metaphor, you will grasp it better. Do you remember when the members of the resistance in the Matrix saga saw numbers cascading on a screen and could "see" things in them?

Well, something like that is what Big Data is about: interpreting a tremendous amount of digital data to understand things or anticipate trends and behaviors based on certain patterns, and thus make more precise and accurate decisions. In the case of companies, focused on offering better services or new products that customers like.

Without having reached the apocalyptic extremes of Matrix, the truth is that we live in a society where every passing day, information continues to grow uncontrollably, in all areas and at all levels.

It is as famous as it is true that an iPhone has greater computing power than NASA when man landed on the Moon, so each person generates a huge amount of data daily.

Without mechanisms to collect, organize, classify, analyze, and interpret all that information, translating it afterward for the average person, having it would be of little use.

Traditional software has already fallen short for such a task in the face of the immense volume, velocity, and variety of data. And that’s where the famous Big Data has entered the scene in recent years.

What Big Data Is Used For

There are fewer and fewer unexplored territories for Big Data. Its application in the business sector, for example, is on the rise, and medium to large firms are investing in this type of data processing as they discover its great advantages.

Almost without realizing it, we are generating information, but not just us, people: machines also "communicate" with each other and exchange data.

Did you know that when you enter a large store with WiFi, even without connecting to the signal, you are leaving a "trail" with your smartphone? The company can thus know what your route was inside the establishment, how much time you spent there, even in which area more and in which less, if you had to wait a long time at the checkout, how many times you go per month…

All that information of yours, along with that of thousands of other customers, helps the shopping center to trace behavior patterns and shopping habits. And with that, everything can be optimized to provide better service and, therefore, boost sales.

Not to mention social networks, where without the slightest embarrassment we provide countless data about ourselves. Have you stopped to think about how Facebook, for example, decides on friend suggestions? Well, it does so with an algorithm that takes into account a multitude of variables based on the data you provide and it collects.

It is not surprising, in this sense, that there are already companies that take your social media profile more into account than your resume when assessing your suitability for a job position.

We Are a Constant Flow of Information

Every time we change the channel on TV, withdraw money from the ATM, or pay with a card at the supermarket, when we travel by plane, use GPS, send an email, call our phone operator or car insurance, visit a website, or shop online, we are generating data that is processed and analyzed.

The future, in many cases already present, involves conducting these studies in real time, which allows, for example, to make personalized offers to each customer based on their tastes and interests. What do you think the Google app on your iPhone is, if not that?

The preparation of matches and athlete statistics, climate change, crops, personnel selection processes at work, university study plans, disease research… Big Data, erected as a sort of crystal ball with reliable predictions, can be applied today to almost any field.

As an example, take the curious case of the American retail chain Walmart, which discovered thanks to massive data that a certain brand of cookies significantly increased its sales every time the weather forecast indicated the arrival of hurricanes.

The reason was none other than the consumers' desire to stock up on supplies, just in case. Once this correlation was discovered, the mentioned store would stock up well and place cookie batches in prominent positions whenever the weatherman predicted such adverse phenomena.

Who Conducts Big Data Analysis

Data is the manna. And there are it in unbelievable quantities. The so-called data mining has opened the door to a new profession, that of the data scientist, with immense demand and relatively few people prepared.

Knowing how to handle all that flow of information, detect patterns, establish correlations, and develop tools that facilitate such work, preferably with the greatest immediacy possible, is the task of those dedicated to Big Data, the area where it is most difficult to find competent professionals.

The "internet of things", with advancements in home automation, will multiply the already gigantic daily amount of data that each of us generates. Our "avatar" is becoming increasingly complete and revealing of who we are and what we like.

The infographics or tag clouds are a visual way to show the conclusions of Big Data reports and make them digestible for those who are not very versed in the subject.

Pros and Cons of Big Data

The positive part is that Big Data can be used in very useful and beneficial studies, such as predicting natural disasters or terrorist acts, containing epidemics or addressing health issues, and even reducing crime and unemployment rates.

Its main drawback, and a major argument of its detractors, is the question of where the privacy of individuals stands. The handling of sensitive data has brought to the forefront the debate on how far the protection of privacy or the use of that information should go.

In this kind of "Big Brother" that sees everything, we do not fully realize the use and purpose of our data, which can even influence our mood, as revealed some time ago by a controversial Facebook experiment.

If you haven't seen it, we recommend the movie "Moneyball", which will illustrate many of the things we've told you. The series "Mr. Robot" will also make you reflect on the use of data and privacy.

Now that you know a little better about Big Data, what do you think about it? Do you think it could be useful for your company? Would you be willing to invest in big data analysis?