Description: Augmented analytics refers to the use of enabling technologies such as machine learning and natural language processing to enhance data analytics. This methodology allows organizations to extract more valuable insights and process large volumes of data more efficiently. By automating analytical tasks, augmented analytics facilitates the identification of patterns and trends that might go unnoticed in traditional analysis. Furthermore, by integrating artificial intelligence capabilities, it enhances data-driven decision-making, enabling users, even those without deep technical knowledge, to interact with data intuitively. Augmented analytics is applied in various areas, from optimizing industrial processes in the context of Industry 4.0 to improving customer experience in diverse sectors. Its ability to operate in Data Lakes and Edge AI environments makes it an essential tool for companies looking to remain competitive in an increasingly data-driven world.
History: Augmented analytics began to take shape in the mid-2010s when companies started integrating artificial intelligence technologies into their data analysis processes. With the rise of big data and the need to process large volumes of information, tools emerged that combined machine learning with traditional analytics. In 2017, Gartner coined the term ‘augmented analytics’ to describe this emerging trend, highlighting its potential to transform how organizations analyze and utilize data.
Uses: Augmented analytics is used in various applications, such as predicting market trends, optimizing supply chains, personalizing customer experiences, and detecting fraud. In the realm of DataOps, it enables smoother integration of data and analytics, enhancing collaboration among teams. In Industry 4.0, it is applied to optimize manufacturing processes and predictive maintenance, while in Edge AI environments, it facilitates real-time data analysis on local devices.
Examples: An example of augmented analytics is the use of platforms like Tableau or Power BI, which incorporate artificial intelligence capabilities to help users discover insights in their data without requiring advanced analytical knowledge. Another case is the use of machine learning algorithms in the financial sector to detect suspicious behavior patterns and prevent fraud in real-time.
