Big Data and Data Management

 Companies' interest in large amounts of data has intensified in recent years. Data is being collected at ever increasing rates and, in many cases, is collected even for a specific purpose. Online stores have started to store not only information on what is purchased, but also all items viewed and all links clicked. Whether public or private, structured or unstructured, data sets are piling up everywhere.


In the past, data was collected intentionally, so that it represented some intentional measure of the real world. More recently, however, the Internet, ubiquitous electronic devices and a latent fear of losing hidden value in data have led organizations to collect as much data as possible, usually on the premise that this data will be useful later. For the first time in history, various sectors are first collecting data and then wondering what can be done with that data.


Social networks usually store everything that their users do on them. The purpose of this collection is not always to sell the data although this also occurs. Much of the websites and applications use their data to improve the user experience. As a result, the owners of these sites and applications are divided between the value of the data as something that can be sold and the value of the data when maintained and used internally.


Many publishers are afraid to sell their data because it opens up the possibility that someone else will find something profitable to do with them. Many of them keep their data to themselves, saving it for the future, when they are supposed to have enough time to extract all its value.

The Information Security Engineer is responsible for the security of an organization’s computer systems and networks. Also known as an Information Security Analyst, the engineer implements security measures that effectively safeguard sensitive data in the event of a cyber-attack. 

This article has the general objective of presenting the aspect of data management in data science. Delimiting itself to analyze the relevant aspects related to obtaining data observed in data science to obtain useful information, as well as the care that must be taken when managing this data.


This work is justified by the importance of knowing the methods used by data science to obtain value from the data.

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