Vol. 3 No. 2 (2023): Journal of AI-Assisted Scientific Discovery
Articles

Improving Accuracy in EDI Data Mapping through AI-Driven Rule Generation

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  • SaiKumar Reddy,
  • Rishi Preetham T
SaiKumar Reddy
IS Application Specialist at Senior EDI Analyst, USA
Rishi Preetham T
Lead Cyber security Specialist at Criag International Ltd., USA
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Published 29-08-2023

Keywords

  • EDI Data Mapping,
  • Artificial Intelligence
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
SaiKumar Reddy and Rishi Preetham T, “Improving Accuracy in EDI Data Mapping through AI-Driven Rule Generation”, Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, pp. 638–659, Aug. 2023, Accessed: Jan. 04, 2025. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/272

Abstract

Electronic Data Interchange (EDI) plays a critical role in streamlining business transactions, yet challenges in data mapping often lead to inefficiencies and errors. Traditional rule-based mapping approaches require extensive manual intervention, making them prone to human error and inconsistencies. AI-driven rule generation offers a transformative approach to enhance the accuracy and reliability of EDI data mapping. By leveraging artificial intelligence, organizations can automate the creation and optimization of mapping rules, significantly reducing the reliance on manual processes. AI models analyze historical data, recognize patterns, and suggest regulations that adapt to changes in data formats and business requirements. This speeds up the mapping process and minimizes errors, as the system continuously learns and improves from new data inputs.

Furthermore, AI-driven solutions can handle complex mappings more efficiently, identifying edge cases that human operators may overlook. Integrating machine learning algorithms ensures that mapping rules evolve with dynamic business needs, enhancing overall flexibility and scalability. As a result, businesses experience fewer data mismatches, improved transaction accuracy, and reduced costs associated with correcting mapping errors. AI-driven rule generation democratizes the EDI mapping process, allowing even non-technical users to oversee and manage data mapping tasks effectively. This innovation represents a significant leap forward for industries reliant on EDI, such as manufacturing, logistics, healthcare, and retail, where precision in data exchanges is critical for operational success. By embracing AI-driven rule generation, businesses improve their data integrity and enhance their ability to respond to market changes with agility. Integrating AI into EDI data mapping offers a sustainable path to maintaining accuracy, reducing operational friction, and improving business relationships in a world increasingly dependent on seamless digital communication.

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