Innovations in Data Quality Management in the Oil and Gas Industry

Document Type : Original Article

Authors
Alireza Shahraki 1
Farid Shahraki Moghadam 2
1 Associate Prof., Department of Industrial Engineering, Faculty of Shahid Nikbakht Engineering, University of Sistan and Baluchestan, Zahedan, Iran
2 MSc. Student, Department of Industrial Engineering, Faculty of Shahid Nikbakht Engineering, University of Sistan and Baluchestan, Zahedan, Iran
10.22034/aimj.2025.528978.1640
Abstract
Data analytics offers a wide range of services to assist the oil and gas sector in developing reliable and secure processes. The information and data derived from these processes form the backbone of modern operations and business decisions. Data quality management in the oil and gas industry is crucial for ensuring operational efficiency, safety, and regulatory compliance. In an industry known for its complexity and scale, having high-quality data is vital for minimizing risks, maximizing productivity, and enabling informed decision-making. This study focuses on data quality management by examining the impact of innovative technologies such as big data analytics, machine learning, the Internet of Things (IoT), and blockchain, which play a significant role in addressing data quality challenges. To this end, a theoretical model was tested using partial least squares structural equation modeling (PLS-SEM) based on a sample of 319 companies operating in the oil and gas sector. The results indicate that big data analytics, machine learning, IoT, and blockchain have a positive and significant effect on data quality management in the oil and gas industry. This study contributes to the ongoing discourse on data quality management by providing a comprehensive analysis of current strategies and future directions, thereby enhancing the sustainability and competitiveness of oil and gas companies in a data-driven environment.

Keywords

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  • Receive Date 08 June 2025
  • Revise Date 26 August 2025
  • Accept Date 03 September 2025