• Sudheer Devaraju

To keep up with retail globalization, operational and human resources data must integrate seamlessly with the rest of the enterprise. Managing large datasets globally while maintaining compliance with regional standards is a significant challenge for global retailers. This paper presents an implementation of a rule-based automation framework in Workday Studio to optimize data transformations. The framework introduces reusable templates that substantially reduce processing times and error rates while improving scalability. A pilot study with a leading retailer demonstrated a 30% reduction in processing times and a 67% decrease in error rates. Future work discusses the integration of machine learning and streaming analytics for adaptive rule generation and enhanced monitoring.

Data Transformation, Workday Studio, Rule-Based Automation, Human Resource Information Systems (HRIS), Enterprise Resource Planning (ERP), Global Retail, Compliance Management, Data Integration, Workflow Optimization, Reusable Templates, Scalability, Error Reduction, Transformation Pipeline, Performance Optimization, Enterprise Systems, Multi-Country Payroll, HR Data Management, Process Automation, System Architecture, Regulatory Compliance

"Optimizing Data Transformation in Workday Studio for Global Retailers Using Rule-Based Automation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.7, Issue 4, page no.69-74, April-2020, Available :http://www.jetir.org/papers/JETIR2004644.pdf

"Optimizing Data Transformation in Workday Studio for Global Retailers Using Rule-Based Automation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.7, Issue 4, page no. pp69-74, April-2020, Available at : http://www.jetir.org/papers/JETIR2004644.pdf