Análisis sobre el proceso de transformación de los datos en inteligencia de negocios: disponibilidad, unificación, automatización y su integración con la inteligencia artificial, en el departamento data, analítica e inteligencia artificial

Abstract

Today, data has become a strategic asset for organizations, as it enables them to measure, control, and optimize processes. For this reason, the centralization and cleansing of information are essential to support planning and decision-making. In this sense, it is imperative that the data available within a company be clear, structured, timely, and — above all — reliable. Likewise, the democratization of data, underpinned by adequate centralization, fosters the existence of single, certified sources of information, allowing users to work from a "single version of the truth" and to have a clearer strategic vision at the moment of making decisions. With the implementation of artificial intelligence, data takes on even greater relevance, as it constitutes its primary input. To the extent that information is reliable and properly structured, the quality of responses and process automation will improve accordingly, enabling artificial intelligence to contribute meaningfully to the optimization of manual and repetitive tasks. For this reason, it is essential that companies implement data centralization and democratization strategies in order to achieve more agile and efficient information flows. Building on the above, opportunities for improvement can be identified through data centralization, data democratization, and process automation. Within the organization — where multiple business units and varying Business Intelligence practices coexist — it becomes indispensable to understand the perceptions of both internal and external clients regarding the information they consume: its quality, timeliness, consistency, and ease of access. These inputs make it possible to guide a comprehensive process of data centralization, governance, and exposure, with the aim of consolidating a reliable, standardized source for the entire company. In this way, the foundations are laid for scaling advanced analytics and enabling artificial intelligence use cases with higher-quality data — an aspect of particular relevance in a context where AI adoption still presents maturity gaps in Colombia.