The term Big Data can be defined in different ways. However, there is a commonly accepted (and pioneering) one given by Gartner which can be translated into: "...high volume, high velocity and/or high variety information assets that require innovative and cost-effective ways of processing information that allow a enhanced vision, decision making and process automation.” Starting from this definition, we are able to identify three Vs: Volume, Velocity and Variety.
Volume is perhaps the most remembered "V" and its presence here is easily justified: the amount of data being produced and its sizes has been growing significantly. Big data has the role of enabling us to use this gigantic mass.
It is not just about the speed with which this data is being generated, such as messages on social networks and credit card transactions. It also takes into account their rhythm, not necessarily constant, but the need to receive and manipulate them at peak times.
With the infinity of devices capable of producing and collecting information, we also consider the different forms of human communication (text, audio and image). Files considered relevant to an organization are increasingly found in different formats and extensions, structured or not.
Note that the definition presented is also composed of terms such as "innovative and cost-effective ways of processing", "improved vision and decision making", and "process automation". Therefore, the search for solutions that make it possible and viable ($) is intrinsic to this universe. In addition, there are studies that identify opportunities and make use of big data for each organizational scenario.
Organizations of any size, also considering the seed planted by Business Intelligence (BI), are becoming aware that greater efficiency and quality in the storage, treatment and analysis of their data is no longer a competitive advantage. It is necessary to have a vital structure and good positioning in their respective markets. Technology is increasingly present. Consequently, big data is already part or will start to do so in the very near future of the reality of companies in the market. In this way, the demand for qualified professionals is also growing. If you are an extremely numerical person, who identifies with dense data analysis, it will definitely make a lot of sense for you!
A good data scientist has a lot of technical knowledge, a skill that today is extremely scarce among professionals in the field. For this, I recommend that you check out the Data Science Degree . In it, you will have the opportunity to study a module dedicated to Big Data in depth, in addition to becoming a data scientist in just 14 months. If you would like to read other complementary articles to this one, check out the blogs Decoding Cloud Computing and What's the Difference Between Analyst, Scientist, and Data Engineer.
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