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Business Intelligence na tomada de decisão sobre a oferta formativa no ensino superior
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O ensino superior em Portugal enfrenta o desafio de alinhar a sua oferta formativa com as necessidades reais do mercado de trabalho, num contexto marcado pela transformação digital e pela crescente valorização das competências analíticas. Neste cenário, as tecnologias de Business Intelligence (BI) surgem como ferramentas estratégicas para apoiar a tomada de decisão, permitindo às Instituições de Ensino Superior (IES) analisar dados, identificar tendências e otimizar as suas estratégias de ensino.
A presente dissertação tem como objetivo principal compreender como o Business Intelligence pode contribuir para a definição de estratégias mais eficazes no ensino superior, de forma a reforçar a sua adaptabilidade face às dinâmicas do mercado laboral. Para tal, o estudo combina uma análise bibliométrica da literatura científica internacional com um estudo empírico, baseado em dados da Direção-Geral de Estatísticas da Educação e Ciência (DGEEC), explorados através da metodologia CRISP-DM (Cross Industry Standard Process for Data Mining) e da ferramenta Power BI.
Os resultados obtidos demonstram que a utilização do BI possibilita uma visão integrada e dinâmica sobre o perfil dos diplomados, áreas de formação e evolução da oferta formativa, de forma a facilitar a identificação de padrões e previsões relevantes. Conclui-se que a adoção de soluções de BI no ensino superior potencia uma gestão baseada em dados, promove a eficiência institucional e contribui para a harmonia entre a formação académica e as exigências do mercado de trabalho, constituindo-se como um fator diferenciador para a competitividade e sustentabilidade das IES.
Higher Education in Portugal faces the challenge of aligning its educational offer with the actual demands of the labour market, in a context shaped by digital transformation and the increasing importance of analytical skills. In this scenario, Business Intelligence (BI) technologies emerge as strategic tools to support decision-making, enabling Higher Education Institutions (HEIs) to analyse data, identify trends, and optimise their academic strategies. This dissertation aims to understand how Business Intelligence contributes to strategic decision-making in Higher Education, enhancing institutional adaptability to the dynamics of the labour market. To achieve this, the research combines a bibliometric analysis of international scientific literature with an empirical study based on data from the Portuguese Directorate-General for Education and Science Statistics (DGEEC), applying the CRISP-DM methodology (Cross Industry Standard Process for Data Mining) and the Power BI tool. The results demonstrate that BI enables an integrated and dynamic understanding of graduates’ profiles, training areas, and the evolution of educational supply, supporting the identification of meaningful patterns and forecasts. It is concluded that the adoption of BI solutions in Higher Education fosters data-driven management, improves institutional efficiency, and enhances the alignment between academic training and labour market needs, becoming a key factor for institutional competitiveness and sustainability.
Higher Education in Portugal faces the challenge of aligning its educational offer with the actual demands of the labour market, in a context shaped by digital transformation and the increasing importance of analytical skills. In this scenario, Business Intelligence (BI) technologies emerge as strategic tools to support decision-making, enabling Higher Education Institutions (HEIs) to analyse data, identify trends, and optimise their academic strategies. This dissertation aims to understand how Business Intelligence contributes to strategic decision-making in Higher Education, enhancing institutional adaptability to the dynamics of the labour market. To achieve this, the research combines a bibliometric analysis of international scientific literature with an empirical study based on data from the Portuguese Directorate-General for Education and Science Statistics (DGEEC), applying the CRISP-DM methodology (Cross Industry Standard Process for Data Mining) and the Power BI tool. The results demonstrate that BI enables an integrated and dynamic understanding of graduates’ profiles, training areas, and the evolution of educational supply, supporting the identification of meaningful patterns and forecasts. It is concluded that the adoption of BI solutions in Higher Education fosters data-driven management, improves institutional efficiency, and enhances the alignment between academic training and labour market needs, becoming a key factor for institutional competitiveness and sustainability.
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Business intelligence Ensino superior Mercado de trabalho Tomada de decisão CRISP-DM Power BI Higher Education Labour Market Decision-Making Power BI