Summary
These sources explore the evolving landscape of business intelligence (BI) and how the integration of Artificial Intelligence (AI) is revolutionising the way businesses use data. They focus on the benefits of AI in BI, such as improved decision-making, data exploration, and insights generation. The sources highlight the challenges associated with implementing AI in BI, including concerns about data quality, security, and user adoption. They analyse the various architectures of hybrid AI-driven BI systems, outlining the strengths and weaknesses of rule-based and AI-powered approaches. The sources also investigate the potential of Large Language Models (LLMs) in streamlining data analysis and reporting through natural language processing and text-to-SQL methodologies. They provide valuable insights into the current state-of-the-art LLM-based approaches, including DIN-SQL, DSP, NSQL, GPT, CoPilot, and LLaMa, comparing their performance and cost-effectiveness. Overall, the sources emphasize the importance of adopting a hybrid approach, combining the precision of rule-based systems with the flexibility and adaptability of AI, to unlock the true potential of data for strategic decision-making.
