Big Data refers to large complex data sets, both structured and unstructured, in which traditional processing techniques and / or algorithms cannot operate.
Its objective is to reveal hidden patterns and has led to an evolution from a scientific paradigm based on models to a scientific paradigm based on data. According to a study by Boyd & Crawford. “It is based on the interaction of:
(a) Technology: maximizing computing power and algorithmic accuracy to collect, analyze, link and compare large data sets.
(b) Analysis: taking advantage of large data sets to identify patterns for making economic, social, technical and legal claims.
(c) Mythology: the widespread belief that large data sets offer a superior form of intelligence and knowledge that can generate ideas that were previously impossible, with the aura of truth, objectivity and precision. “
The transformation of Big Data analysis when evaluating the reasons why several organizations are gravitating towards Big Data analysis, a concrete understanding of traditional analysis is necessary. Traditional analytical methods include structured data sets that are periodically consulted for specific purposes . A common data model used to manage and process commercial applications is the relational model; This Relational Database Management System (RDBMS) provides “easy-to-use query languages” and provides simplicity that other networks or hierarchical models cannot offer. Within this system there are tables, each with a unique name, where related data is stored in rows and columns.
These data flows are obtained through integrated databases and influence the intended use of these data sets. They do not provide much advantage in order to create newer products and / or services as Big Data does, which leads to the transformation of Big Data analysis. Frequent use of mobile devices, Web 2.0 and the growth of things on the Internet are among the few mentioned in the reasoning behind organizations that seek to transform analytical processes. Organizations are attracted to big data analysis, as it provides a means to obtain real-time data, suitable for improving business operations. In addition to providing parallel and distributed processing architectures in data processing, big data analysis also allows the following services: “after-sales service, search for missing persons, intelligent traffic control system, customer behavior analysis and data processing system. crisis management. “
The concept of Big Data analysis is constantly growing. Its environment demonstrates great opportunities for organizations from various sectors to compete with a competitive advantage, as shown in the examples mentioned above. The future of medical science is changing dramatically due to this concept, scientists can quickly access data on a global scale through the cloud, and these analyzes contribute to the development of predictive analytical tools (that is, they facilitate predictive results in primary stages). However, as mentioned (Section IV), there are inconsistencies and challenges within Big Data privacy: encryption algorithms sufficient to hide data or unprocessed analysis, reliability and integrity of Big Data, data storage problems and failures within of the MapReduce paradigm. This document sheds light on the conceptual ideologies about big data analysis and shows through some scenarios how it is beneficial for organizations within several sectors if the analysis is performed correctly. Other areas of research to better understand Big Data are data processing and transfer techniques.