Data Engineering Challenges in AI-Powered Business Intelligence Platforms

Abstract

Data engineering is essential for the successful implementation of AI-powered business intelligence platforms, yet it presents several challenges that organizations must navigate. One significant challenge is data integration and management, where businesses struggle to ingest and consolidate vast amounts of structured and unstructured data from diverse sources. This complexity is exacerbated by varying data formats and the need for reliable data pipelines, which are crucial for the scalability and performance of AI models. Another critical issue is data quality and cleansing. AI models depend heavily on clean and reliable data, but data engineering often involves dealing with incomplete or noisy datasets. Organizations must invest in advanced techniques, such as machine learning, to automate data cleaning processes and enhance overall data quality. Lastly, explainability and transparency pose significant hurdles. Many AI models generated through data engineering are difficult to interpret, making it challenging for businesses to validate their outputs. Ensuring that AI techniques are understandable and transparent is vital for fostering trust in the decisions derived from these models. In summary, while AI offers transformative potential for business intelligence, the interplay between data engineering challenges such as integration, quality assurance, and model explainability must be effectively managed to unlock this potential