Automating SAP Data Migration with Predictive Models for Higher Data Quality

Rajesh Tirupathi¹, Ashish Kumar², Srinivasulu Harshavardhan Kendyala³, Om Goel⁴, Raghav Agarwal⁵ & Shalu Jain⁶

¹Liverpool John Moores University USA, rajeshtirupathi.publications@gmail.com

²Tufts University, Medford MA, 02155 USA ashisheb1a@gmail.com

³ University of Illinois chin.p8691@gmail.com

⁴ABES Engineering College Ghaziabad, U.P., India, omgoeldec2@gmail.com

⁵Mangal Pandey Nagar,Meerut (U.P.) India 250002,raghavagarwal4998@gmail.com

⁶Maharaja Agrasen Himalayan Garhwal University, Uttarakhand, India,mrsbhawnagoel@gmail.com

Abstract

Data migration is a critical process for organizations implementing or upgrading their SAP systems. It involves transferring data from legacy systems to new platforms, and its success directly impacts business operations and decision-making. Despite the advancements in technology, many organizations still face significant challenges in ensuring high data quality during migration. Traditional methods often rely on manual interventions and predefined rules, which can lead to errors, inconsistencies, and ultimately, unreliable data. This paper proposes an innovative approach to automate SAP data migration using predictive models to enhance data quality, reduce errors, and improve overall migration efficiency.

The methodology adopted in this study includes a comprehensive literature review to assess existing data migration strategies and automation tools. We then develop a predictive model tailored for SAP data migration, which involves data preprocessing, feature selection, and model training using a diverse dataset sourced from previous migration projects. The effectiveness of the predictive model is evaluated through rigorous testing, with metrics focusing on accuracy, precision, and recall. Furthermore, we conduct a case study to illustrate the practical implementation of the proposed automation framework in a real-world scenario.

Results from the case study demonstrate that the integration of predictive models into the SAP data migration process significantly enhances data quality. The predictive model effectively identifies 85% of potential data issues before migration, resulting in a 40% reduction in post-migration corrections and a notable improvement in data integrity. Additionally, the automated approach reduces the time required for data migration, enabling faster implementation of SAP systems and minimizing disruption to business operations.

This research contributes to the growing body of knowledge in data migration by providing a novel framework that combines automation and predictive analytics. The findings emphasize the importance of utilizing advanced technologies to improve data quality and streamline migration processes. The implications of this study extend beyond SAP migrations, offering valuable insights for organizations in various industries facing similar data challenges.

Keywords

Automated migration, predictive models, data quality, SAP, scalability, machine learning, data governance, change management.

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