What is Big Data?

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Big Data is a process of analyzing and interpreting a large volume of remotely stored data.

Everything that is available online, in a non-confidential way, regardless of the amount of information, is within the reach of Big Data and can be grouped according to interest.

And that includes not just public databases like YouTube is for videos, or Wikipedia, which functions as the internet’s largest encyclopedia.

Big Data can integrate any data collected about a subject or a company, such as purchase and sale records and even non-digital interaction channels ( telemarketing and call center).

Where there is a record made, technology catches up.

Only really inaccessible information is left out, such as your financial transactions and private information of some organizations, for example.

What wanders through the web can be accessed, collected and grouped.

The most incredible thing is that this is carried out at great speed, using specific Information Technology (IT) tools.

And if we stop to think about it, it must be that way, given the gigantic amount of information generated each day by different devices.

With Big Data, therefore, it is possible to interpret and analyze this data for various uses.

Among them, defining a company’s marketing strategies, reducing costs, increasing productivity and giving a smarter direction to the business itself.

Recently, managers have used the “philosophy” of Big Data a lot as a strategic support tool .

What happens is that they have come to understand its importance to gain insights into market trends and consumer behavior, in addition to improving the work process itself.

Indicatives are able to help in making more assertive decisions and, above all, more advanced than the competition.

It goes without saying how critical this is to ensuring the success of any business.

Therefore, all this information, available online and offline , is capable of helping the company to grow.

But that’s still not all there is to know about the importance of Big Data.

The history of Big Data

Although its use has become more frequent in recent times, the term Big Data was born in the 1990s .

And look where: at NASA (National Aeronautics and Space Administration), the American space agency.

At the time, DB was used to describe complex data sets that challenged the traditional computational limits of capturing, processing, analyzing and storing information.

In 2001, then Vice President and Research Director of Enterprise Analytics Strategies, Doug Laney, articulated the definition of Big Data in three V’s :

  • Volume
  • Variety
  • Speed.

But 12 years later, Express Scripts head of data, Inderpal Bhandar, argued that there were three additional V’s

  • Value
  • Volatility
  • veracity.

As we will see in the next topic, the history of Big Data would still reserve the proposition of a seventh V :

  • visualization.

The model was then complete.

And because of the efficiency , organizations started to realize the power of using Big Data.

According to this report (in English), published by the Forbes Magazine website in 2015, about 90% of medium to large companies already invested in BD.

What is Big Data for?

The amount of data generated worldwide is absurd, and the speed of this process is increasing exponentially.

From 2021 to 2024, more information is expected to be created than in the last 30 years combined, according to an IDC survey.

Just to give you an idea, by 2020, about 40 trillion gigabytes were generated, which gives an average of 2.2 million terabytes per day.

This ocean of content hides information that can be valuable if correctly collected, processed and analyzed.

This is precisely where Big Data and its technologies come in, to work with a large volume of data quickly, at an affordable cost and effectively.

Thanks to these solutions, it is possible to make decisions and develop more informed and assertive insights .

How does Big Data work?

To better understand how Big Data works, it becomes easier to divide this processing into steps .

So, let’s go to them:

Data collect

Also called data acquisition or recording, it is the phase of gathering all that great volume and diversity of information.

While it is being collected, it is necessary that this information has already passed through some type of filtering or formatting , eliminating errors and incomplete data.

This type of care is essential so that there is no damage in the following steps, as can happen in the analysis process, if there are corrupted data.

data integration

After that first moment, it’s time to integrate this data.

As they are of different fonts, formats and characteristics, they must receive specific treatments.

It is here, therefore, that validation, acceptance, security criteria and data categories must be defined, according to their sources.

Data analysis and modeling

This is one of the most important phases in Big Data, as it is where data begins to gain value and become information.

For this, it is necessary to have trained professionals and the support of artificial intelligence and machine learning technologies, which will make this work more agile and assertive.

In addition, it is also important to research new types of data visualization so that valuable discoveries are made, which favor a better interpretation of the information.

Data interpretation

The data interpretation phase is the last and also the one that makes all the investment in big data worthwhile.

After all, this is where you will be able to extract insights that will guarantee your business competitive advantages and offer a great customer experience.

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