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Enhancing Data Quality through Generative AI: An Empirical Study with Data
  • Pan Singh Dhoni
Pan Singh Dhoni
Five Below

Corresponding Author:[email protected]

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Abstract

In today’s increasingly data-driven landscape, organizations are shifting their focus toward leveraging data analytics for strategic decision-making. As data becomes a cornerstone of operational and strategic activities, the quality of this data has emerged as a non-negotiable aspect for organizations. Lack of attention to data quality can not only result in considerable revenue losses but can also cripple the effectiveness of analytics, causing misinformed decisions and strategic errors. Against this backdrop, this empirical study delves into the innovative avenue of utilizing Generative Artificial Intelligence (AI) as a mechanism for enhancing data quality.
The research aims to explore multiple facets of organizational operationsâ\euro”ranging from technical infrastructure to business strategyâ\euro”to ascertain the potential advantages offered by Generative AI. Utilizing a mix of qualitative and quantitative methods, we conducted in-depth interviews, case studies, and simulations to evaluate the impact of Generative AI on data quality.
Our findings reveal a multi-layered benefit structure. Notably, we found that Generative AI is not a replacement for existing, traditional methods of data quality assurance but serves as a powerful supplement. It augments traditional methods by increasing the accuracy of data, thereby offering a more reliable foundation for analytics. Additionally, the use of Generative AI can streamline workflows, enhancing productivity among various roles including solution architects and software developers. Moreover, it facilitates a more nuanced and accurate requirement gathering process, enabling businesses to fine-tune their data analytics strategies more effectively.
In conclusion, our study establishes that integrating Generative AI into data quality management processes can not only resolve immediate issues surrounding data accuracy but also lead to long-term organizational benefits, such as higher efficiency and more effective decision-making. This research serves as a pioneering step in the intersection of Generative AI and data quality, setting the stage for future studies and real-world applications.