AI-Driven Big Data Architecture for Industry Decision Support
DOI:
https://doi.org/10.5281/et9qeh26Keywords:
Decision Support Systems (DSS), Data Lakes, Data Warehouse, Ingestion and Storage architecture, Data Quality, Data Security, Data Governance, Data Science, Surveillance Big Data Architecture, Data-Driven Decision Support System.Abstract
Artificial Intelligence (AI) and Big Data technology have moved from academic research to mainstream adoption, with a growing interest in how businesses can leverage the technology to enhance their decision-making processes. The emergence of sophisticated AI models, such as ChatGPT, emphasizes the necessity of data that is accurate, comprehensive, clean, reliable, and trustworthy. However, the cleanness aspect often poses a challenge. Organizations have suffered reputational damage, financial losses, and even regulatory sanctions due to data breaches, data misuse, data unavailability, or decision inaccuracies resulting from poor data quality. The important yet complex task of keeping the data clean and secure is exacerbated by recent analysis revealing the presence of sensitive data in large multilingual language models. Strategic AI implementation and trustworthy AI decisions can help businesses, governing authorities, ecosystem partners, regulators, and society as a whole to take on the challenges posed by Big Data. Recent research has compared conventional Data Warehouse and Data Lake architectures to support AI Decision Support Systems. A detailed methodological framework prepared following a Data Lake architecture supports the production of basic AI Data-Immune APIs and Machine Learning Models, with decision-support goal formulation driving all the stages of the AI Model Pipeline. Multiple-color-coded sector maps have been generated that aid the identification of data sources, model preparations, AI-based architecture routing, Decision Support System workflows, expected business outcome improvements, and the supporting data-latency channels.
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