Can generative AI transform data quality? a critical discussion of ChatGPT’s capabilities

image: xornortechnologies

By Otmane Azeroual

  Data quality (DQ) is a fundamental element for the reliability and utility of data across various domains. The emergence of generative AI technologies, such as GPT-4, has introduced innovative methods for automating data cleaning, validation, and enhancement processes. 

   This paper investigates the role of generative AI, particularly ChatGPT, in transforming data quality. We assess the effectiveness of these technologies in error identification and correction, data consistency validation, and metadata enhancement. Our study includes empirical results demonstrating how generative AI can significantly improve DQ. The findings suggest that generative AI and ChatGPT have a transformative impact on data management practices, offering new opportunities for enhancing data quality across various applications.

1. Introduction

In the contemporary data-driven landscape, the quality of data is critical for accurate decision-making, operational efficiency, and the dependability of data-dependent systems [1]. Low data quality can lead to incorrect conclusions, operational inefficiencies, and substantial risks [2]. As organizations increasingly handle vast amounts of data, ensuring their quality has become essential.

Traditional data cleaning and validation methods, though effective, are often labor-intensive and susceptible to human error [3]. These methods generally involve manual processes such as identifying and correcting inconsistencies, validating data against predefined standards, and enriching metadata. Despite diligent efforts, human involvement introduces variability and potential inaccuracies, particularly as data volume and complexity continue to grow [4].

The advent of generative AI technologies offers promising solutions to these challenges. Generative AI, exemplified by advanced interfaces like GPT-4, provides novel approaches for automating data cleaning, validation, and enhancement processes [5]. These interfaces excel in natural language processing (NLP) tasks due to their ability to understand and generate human-like text, making them particularly adept at tasks requiring contextual understanding and linguistic capabilities [6].

GPT-4, the fourth generation of the Generative Pre-trained Transformer, has shown remarkable proficiency in various NLP tasks [7]. Its capability to generate coherent and contextually relevant text enables automation in error detection, data consistency validation, and metadata enhancement [8]. Empirical studies reveal that GPT-4’s application in data quality management can lead to substantial improvements.

ChatGPT, a variant of GPT-4, is optimized for conversational tasks and can interact with data dynamically and intuitively [9]. It can automatically correct metadata errors, infer missing information, and enrich data by adding relevant details [10]. Its conversational interface facilitates a more interactive and user-friendly approach to data management, making it accessible to users with varying levels of technical expertise [11].

This paper explores the potential of generative AI, with a focus on ChatGPT, in transforming data quality. We critically evaluate whether these interfaces can be relied upon to enhance data quality. This paper includes an analysis of GPT-4 and ChatGPT’s effectiveness in error correction, data consistency validation, and metadata enhancement, supported by quantitative results and case studies.

The implications of this research are profound. Demonstrating that generative AI can reliably improve data quality could revolutionize data management practices, leading to higher accuracy and efficiency while reducing reliance on manual processes. Furthermore, the scalability of AI-driven solutions could enable more effective management of larger datasets, addressing the increasing demand for high-quality data.

In conclusion, this paper provides a thorough evaluation of generative AI and ChatGPT’s capabilities in enhancing data quality. By establishing their reliability, we aim to support the broader adoption of these technologies in data management, contributing to more accurate, efficient, and reliable data systems.

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