Decision supporting approach based on suitable chatbot system for big data analytics

Evan Asfoura and Gamal Kassem

Data and Metadata, 2024, vol. 3, .564

Abstract: Introduction: The increasing reliance of organizational decision-makers on advanced information systems and analytical tools highlights the transformative potential of big data analytics in modern business environments. As organizations accumulate vast amounts of data, the ability to harness this information effectively has become critical for informed decision-making and strategic planning. However, the complexity of big data analytics and the evolving demands of business environments pose challenges, particularly for managers navigating data-driven cultures. Effective utilization of these tools requires comprehensive training and support, especially for newly appointed managers Objective: . This paper presents a chatbot-based system designed to bridge the gap between decision-makers and big data analytics. By leveraging natural language processing (NLP) and machine learning, the proposed chatbot facilitates interactive learning and real-time engagement with analytical insights. This system empowers decision-makers to navigate analytical outputs efficiently, fostering improved decision-making processes. Methods: The research adopts a design science methodology to develop and evaluate this innovative approach. Initial findings suggest that the chatbot enhances accessibility and usability of analytics tools, reduces the technical burden on managers, and promotes a more effective data-driven decision-making culture Results: chatbot-based decision support solution demonstrated its potential to transform decision-making processes in data-driven organizations. By addressing the feedback gathered during this evaluation phase, future iterations of the system can further enhance its utility and effectiveness. Conclusions: This study contributes to the growing discourse on integrating artificial intelligence tools in organizational decision-making and highlights their potential to transform managerial practices in a data-intensive era.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:3:y:2024:i::p:.564:id:1056294dm2024564

DOI: 10.56294/dm2024.564

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