A carregar...
Publicação
Artificial Intelligence in Business Intelligence Systems: Assessing Opportunities, Challenges and Organizational Impact
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 1.71 MB | Adobe PDF |
Autores
Orientador(es)
Resumo(s)
This thesis examines the impact of Artificial Intelligence (AI) on Business Intelligence (BI) workflows, with a particular focus on the opportunities, challenges and organizational implications associated with its integration into data-driven decisionmaking processes. A comparative case study approach is adopted, in which a complete BI pipeline is executed using both traditional methodologies and AIaugmented tools. The research follows a structured methodology comprising exploratory, conceptual, testing and conclusive phases. Two real-world datasets are used to implement and evaluate both approaches across all stages of the BI process, including data extraction, transforming and loading (ETL), data engineering, dashboard development and analytical insight generation. The results are assessed through multiple dimensions, namely efficiency, quality of insights, user experience, cost implications and reproducibility. The findings indicate that AI-powered tools significantly enhance speed and automation, particularly in data preparation and insight generation, while also introducing challenges related to transparency, reliability and governance, commonly associated with the “black-box” nature of AI systems. This research contributes to both academic and practical domains by providing empirical evidence on how AI reshapes BI workflows and by identifying the conditions under which AI can effectively augment or complement traditional analytical processes, highlighting the importance of balancing automation with interpretability and human oversight to maximize the value of AI-driven Business Intelligence.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Artificial Intelligence (AI) Business Intelligence (BI) ETL Processes Data-Driven Decision Making Augmented Analytics (AA) Machine Learning (ML) Data Analytics