Description: Analytical processing refers to the set of techniques and tools used to analyze large volumes of data in order to extract valuable information that supports decision-making in organizations. This process involves the collection, transformation, and storage of data in a data warehousing environment, where it is organized in a way that is easily accessible and analyzable. Through ETL (Extract, Transform, Load) processes, data is integrated from various sources, cleaned, and structured to facilitate analysis. The goal of analytical processing is to provide decision-makers with accurate and timely information, allowing them to identify trends, patterns, and correlations that can influence business strategy. This approach is fundamental in the era of Big Data, where organizations seek to leverage available information to enhance their competitiveness and operational efficiency.
History: The concept of analytical processing has evolved since the 1980s when companies began adopting data warehousing systems to store large volumes of data. In 1996, Ralph Kimball popularized the ‘dimensional data warehousing’ approach, which facilitated data analysis by creating more intuitive structures. As technology advanced, analytical processing benefited from improvements in storage capacity and analysis tools, such as OLAP (Online Analytical Processing), which allowed for more efficient complex queries.
Uses: Analytical processing is used across various industries for strategic decision-making. For example, in the financial sector, it is employed for risk analysis and fraud detection. In retail, it helps understand consumer behavior and optimize inventory. Additionally, in healthcare, it is used to analyze patient data and improve medical care.
Examples: A practical example of analytical processing is the use of tools like Tableau or Power BI, which allow organizations to visualize and analyze data to identify trends and patterns. Another case is the use of predictive analytics systems in sectors such as insurance, where historical data is analyzed to predict future outcomes and behaviors.
