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About the Client
The client is a leading provider of uniforms and workwear services across North America. As part of their Oracle Fusion Cloud transformation, the client aimed to ensure clean, accurate, and efficient data migration for customers, suppliers, and inventory.
The Challenges
During the Oracle Fusion migration, the client faced persistent issues with customer and contact data loads:
Dependency Conflicts – Loading customer and contact records simultaneously caused parent-child mapping errors, leading to high load failure rates.
Delays in Timelines – Failed loads forced repeated rework, delaying go-live and impacting downstream project phases.
Alteryx Performance Bottlenecks – ETL jobs were large and inefficient, consuming excessive time and resources.
Manual Rework – Data errors required heavy manual intervention, straining both IT and business teams.
Lack of Sequencing – Earlier approaches didn’t account for dependencies, making the entire conversion cycle inefficient.
The client needed a robust, sequenced strategy that would:
Achieve > 95% load success rates
Reduce ETL execution time by at least 40%
Minimize manual intervention while improving overall data quality.
Discover how one company cut manual work by 65% and made faster decisions with smarter workflows
The Solution
Our team designed a two-pronged approach:
Sequential Load Strategy
Developed separate workflows to load customer records first, validate their success, and only then load related contact data.
Built validation scripts to confirm customer creation before dependent contacts were processed.
Alteryx Optimization
Applied row filters to shrink dataset sizes and improve processing speed.
Removed redundant transformation steps and split heavy jobs into smaller, modular flows.
Introduced cleansing rules for standardized, error-free input data.
Added intermediate validations and logical sequencing to ensure efficiency and reduce rework.
Learn how a leading agribusiness leader transformed dry goods planning with Oracle Cloud solutions
Tools Used
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Business Impact
The revised strategy delivered clear, measurable improvements:
Load success rate improved from 65% → 85% for customer and contact data
ETL execution time reduced by ~60% , freeing system capacity
50%+
Reduction in manual intervention, easing workload for IT and business teams
Zero critical errors during mock go-live for customer data
Increased business user confidence with reduced testing rework and cleaner data flows
Amit K. Kulkarni
Oracle Fusion Technical Consultant
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