Description: An analytical model is a mathematical representation of a system or process used to analyze data. These models are fundamental in the field of business intelligence, allowing organizations to make informed decisions based on data. Through various statistical techniques and machine learning algorithms, analytical models can identify patterns, trends, and relationships within large volumes of data. Their design can range from simple models, such as linear regressions, to complex approaches like neural networks that simulate the functioning of the human brain. The ability of these models to adapt and learn from new data makes them essential in a constantly changing business environment. Additionally, their implementation in data integration and analysis platforms facilitates real-time data processing, improving operational efficiency. In the context of federated learning, analytical models allow the training of algorithms without the need to centralize data, respecting privacy and information security. In summary, analytical models are powerful tools that transform data into knowledge, driving innovation and competitiveness in the market.
History: The concept of analytical models has evolved since the development of statistics in the 18th century, with figures like Pierre-Simon Laplace and Carl Friedrich Gauss laying the groundwork for data analysis. With the advent of computing in the 20th century, the ability to process large volumes of data enabled the creation of more complex models. The popularization of machine learning in the 2010s marked a milestone in the evolution of analytical models, allowing organizations to leverage advanced algorithms for predictive analysis and decision-making.
Uses: Analytical models are used in various areas, including sales forecasting, customer segmentation, risk analysis, and process optimization. In healthcare, they are applied to predict disease outbreaks and improve treatments. In finance, they help identify fraud and assess credit risks. Additionally, in marketing, they enable campaign personalization and enhance customer experience.
Examples: An example of an analytical model is the use of linear regressions to predict housing prices based on features such as location and size. Another case is the use of neural networks in image recognition, where models are trained to identify objects in photographs. In finance, credit scoring models are used to analyze applicants’ payment histories to determine their eligibility for loans.
