Big Data and Data Analysis

Big Data has become a central term in the era of digital information, marking a significant evolution in the way we collect, store, process, visualize and manage enormous amounts of data. It refers to data sets so large and complex that traditional data processing methods are insufficient to handle them. Its evolution has been parallel to technological progress, marking an era where the ability to process large volumes of information is crucial for development and innovation.

Brief history

Figure 1 shows some milestones in the evolution of Big Data. The first steps towards Big Data are taken with the development of databases and data warehouses. Storage and processing capacity was limited, but it laid the groundwork for future advances.

The introduction of relational database management systems (1980) provided more efficient ways to store and retrieve large sets of data.
Between 1990 and 2000, with the arrival of the Internet, social networks, IoT and IIoT devices and the rise of electronic commerce, there was an explosion in the amount of data available, as well as in the sources that provide such data. Specific algorithms were developed to process and analyze large volumes of data, not only local, but hosted on the Internet to improve the user experience.
In the middle of 2000, the era of Big Data began. Data is the protagonists and how it is used to enhance the capacities of organizations will be the trend. Not only with regard to the technology used to store and process, but also because of the value of information as a strategic asset, it makes the difference between the products and services offered by organizations.

Components of Big Data

A Big Data architecture must consider the integration of new technologies and tools, which operate on data:
Types of data. They refer to the different categories of information that can be processed and analyzed. These are essential for understanding how data can be stored, managed and used.
Distributed processing. The development of new hardware and software platforms that allow distributed processing on networks for large data sets using server clusters (groups of computers that are managed together and participate in workload management), makes it possible to solve scalability and storage challenges.
Computing, storage and processing in the cloud. The cloud (networks of server clusters that are accessed via the Internet as a single unit) provides storage and computing resources on demand that allow organizations and individuals to store and process large amounts of data without the need for expensive infrastructure.
Analysis processes. They refer both to the approach and to the way in which data will be examined and exploited: Data Analysis (exploration and study of data sets to extract useful information and knowledge) and Data Mining (methods for identifying hidden patterns and relationships within data using statistical techniques and machine learning algorithms).
Each of these components has its own specific techniques and tools that are adapted to different operational or business needs and objectives.

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Risks

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When accessing large amounts of information in a set that includes application, storage, database and network technologies, the following must necessarily be taken into account:
Data privacy and security. As data collection increases, so do concerns about data privacy and security, as well as about the Big Data infrastructure itself.
Ethics and Governance. The responsible use of Big Data is a topic of ongoing debate, with calls for better governance (policies, processes and tools for managing and controlling the use of data in the organization) and ethical regulations, ensuring that the use of data is legal, transparent and responsible, with respect to decision-making based on data, as well as the need to consider the social and cultural implications of its use.

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Big Data in Mexico

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In Mexico, there is no formal date on which Big Data was adopted. It has gained popularity over the past ten years, as it offers significant opportunities in several sectors, both in initiatives aimed at the operation, management and optimization of industrial processes, and in the improvement of strategic, tactical and operational business processes, enabled by decisions based on data. Several projects under development in the country demonstrate the potential of these technologies.

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Conclusions

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Big Data has undergone a significant evolution in the way in which large amounts of data are collected, analyzed and used, marking an era where the ability to process information is crucial for development and innovation. As a set of hardware and software components that allow data storage and analysis, factors such as privacy, security, ethics and data governance must be considered.
In Mexico, Big Data has gained popularity over the past ten years and offers significant opportunities in several sectors. However, it will depend on the business sector and on the training of cadres of specialists in sector data, whether these initiatives empower organizations by applying them not only as technology, for the optimization of industrial processes, but also in making decisions based on data for the improvement of the products, services, operations, maintenance and strategies offered.

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About Apollocom

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At Apollocom, we develop these capabilities in the disciplines of Telemetry, Telecommunications and Control and Automation, not only in the Oil and Gas sectors, but in new ones such as Aeronautics and Railway Transportation, among others, to bring your organization to the technological forefront, integrating Connected Technology with Intelligence®. Contact us.

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