Description: Vast data refers to extremely large and complex datasets that cannot be effectively managed, processed, or analyzed using conventional data processing tools. These data can come from various sources, such as social media, IoT sensors, business transactions, and online activity logs. The main characteristic of vast data is its volume, but it also includes variety and the speed at which it is generated, known as the ‘3 Vs’ of data: volume, variety, and velocity. Automation with artificial intelligence (AI) plays a crucial role in managing this data, allowing organizations to extract patterns, trends, and valuable insights that can inform strategic decisions. The ability to analyze large volumes of data in real-time has transformed various sectors, facilitating service personalization and process optimization. In summary, vast data is a fundamental resource in the digital age, and its automated analysis through AI is essential to harness its potential.
History: The concept of vast data began to gain attention in the 2000s when the exponential growth of digital information became evident. With the rise of the Internet and the digitization of processes, organizations began to accumulate large volumes of data. In 2001, the term ‘Big Data’ was popularized by researcher Doug Laney, who defined the ‘3 Vs’ that characterize this data. Since then, the evolution of technologies such as cloud storage and the development of machine learning algorithms have enabled more effective analysis of these massive datasets.
Uses: Vast data is used in a variety of applications, including predictive analytics, user experience personalization, business process optimization, and fraud detection. In various sectors, it is used to analyze large volumes of data and improve outcomes. For example, in commerce, it allows companies to better understand consumer behavior and adjust their marketing strategies accordingly.
Examples: An example of the use of vast data is the analysis of social media data to identify consumer trends and user preferences. Another case is the use of sensor data in industry to predict machinery failures and optimize maintenance. Additionally, platforms utilize vast data to recommend content to their users based on their habits and preferences.
