What Is Big Data and How to Bring It to Your Day-to-day Business?

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delwar80
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What Is Big Data and How to Bring It to Your Day-to-day Business?

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In a world increasingly populated by data , those organizations that know how to take advantage of its potential by analyzing it in a productive and useful way take the lead. To refer to this immense flow of information, you have probably already heard of the term Big Data. Whether in your work environment or even in the news, this topic has never been as high as it has been in recent years.

Today, knowing how to analyze the vast amount of, employee contact list information available in the market gives the company the opportunity to fully understand its audience and to personalize its products and services more and more. Do you want to know what Big Data analysis is and how it works? Join us in this article. What is the big data?

The term Big Data was born in the early 1990s at NASA with the aim of describing large agglomerates of complex data that challenge the ability of traditional computers to capture, process, analyze and store them in an organized manner . In the current context of Information Technology, the term Big Data refers to all data (structured or not) that is generated every second.

Its analysis constitutes a powerful tool that allows improving decision making. Information on traffic in real time, data crossing to detect local outbreaks of diseases, analysis of fuel consumption in large airports... The applications of Big Data are extremely varied. According to a BSA study | The Software Alliance, in 2015, 2.5 quintillion bytes of information were generated daily. On the other hand, the giant Seagate expects that the volume of information available will exceed 175 zettabytes in 2025, quintupling the amount of data existing in 2018 (33 zettabytes).

According to other studies, it is estimated that until 2024 the servers will process a volume of data equivalent to the content of a stack of books that would reach a hypothetical distance of 4.5 light years, a distance that would go beyond the Milky Way itself. Big Data analysis With the comparisons and predictions mentioned here, it goes without saying that the context of Big Data is impossible to be understood and interpreted by any human being, right?

Well, this is where Big Data analysis comes into play. Just a blog entry, a call center call , a streaming service , a text posted on social media, or any other data source is all it takes for a business to start extracting useful insights from this universe. In addition, we can say that the term Big Data is actually a fairly relative concept, since the perception of the size of a mass of data depends on who is observing it.

For example, the information base that the British statistician John Graunt analyzed in 1663 to study the Black Death epidemic in Europe could well be considered a good antecedent of Big Data due to its considerable size. Another worthy precedent could be the demographic censuses of the 19th century in the United States, which also began to be processed mechanically by tabulation machines. Although currently, in our scenario of constant digital transformation , the amount of data that can be subjected to analysis is much, much greater, which will also require a series of tools to match. What defines Big Data?

The simple fact that a database contains a large amount of information does not allow us to fit it into the Big Data category. For this, it needs to have a series of characteristics that define it as such. Thus, to consider data significant enough to be called Big Data, we need to analyze 5 fundamental characteristics known as the 5 Vs. Volume When we talk about Big Data, we are invariably talking about large volumes of information .

It is, therefore, a large flow of data generated every second. More than terabytes, Big Data works on the scale of zettabytes and brontobytes. For example, Facebook has a flow of 10 billion messages, 4.5 billion likes and 350 million photo shares per day. Therefore, Big Data analysis focuses on treating this large volume of information, categorizing it and storing it through specific software. Speed A dataset can only be considered Big Data if the data is created at a very high frequency and speed.

Therefore, it is not enough that the amount of information is huge, but also that it increases in a continuous and very fast flow . Imagine what happens in a process of viralization of messages on social networks, during the verification of transactions of credit card companies or when calculating the trading values ​​of shares on the stock market, which vary every second. In this sense, Big Data refers to a large flow of data created almost instantly, which also alters the way it should be analyzed. Variety In the past, much of the data generated by organizations was considered structured.

These data could be easily tabulated and related. Today, more than 80% of the data generated is considered unstructured, being made up of photos, videos, audios and messages. Such variety in the composition of information makes Big Data analysis necessary to manage this heterogeneous universe of data, which will allow it to be placed side by side with more traditionally generated data.

Veracity In any type of data analysis it is essential that the information managed is true. In times of fake news like ours, the processing of false data can lead to erroneous results and truly disastrous decision-making. In Big Data it is impossible to control every piece of false information available on the network, but we can use statistical analysis of large volumes of information to compensate for incorrect data. In other words, from comparisons it is possible to reach satisfactory levels of accuracy, thus acquiring an information base closer to reality that will help us in our future planning. Value Finally, the last V considers the relevant data.

It is useless to have a large amount of information generated every second if it is not possible to make it have any value. Giving value and meaning to large volumes of data is what makes Big Data an important tool for your business. Therefore, always keep in mind that there is not only a cost related to the capture and analysis of information, but also a necessary cost to make it valuable. What are the types of Big Data? When we talk about Big Data, we can basically classify data into two types, depending on its form: structured and unstructured.

The data that is considered structured is that which has a defined structure , so it may include, to name a few examples, information on categories, definitions, location, sales, customer profile information, etc. Very frequent in traditional data banks, they are based on their need to store information in an organized manner, thus allowing the easy location of each type of information and the identification of patterns in the distribution of said data. Virtually every company uses some type of structured data storage software, such as ERP or CRM, which manage data on finances, Human Resources, sales, etc.
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