Automated Data Wrangling Solution for Tax Categorization
Table of contents
Business Objective
A global financial advisory leader, managing data for a workforce of 286,000+, sought to overhaul its manual tax categorization workflows.
The objective was to eliminate the labor-intensive process of standardizing and mapping financial data from a vast array of disparate client ERP systems.
The goal was to establish an intelligent, automated pipeline capable of ingesting diverse data formats and utilizing machine learning to categorize transactions with enterprise-level precision.
The Solution
OLSYS engineered a three-stage automated data orchestration pipeline that integrates high-speed ETL processes with advanced machine learning.
By combining direct ERP connectors with a Big Data processing engine, we transitioned a fragmented manual workflow into a streamlined, cloud-native system that standardizes and categorizes tax data in real time.
Key elements:
- Direct ERP Integration: Automated data collection modules that pull directly from client systems, eliminating manual file transfers and human error.
- Big Data Transformation: Leveraged Spark and Azure Databricks to standardize diverse data formats across global organizational systems at scale.
- ML-Driven Tax Categorization: Machine learning algorithms that automate category mapping, replacing the most time-intensive manual phases of the tax lifecycle.
- Cloud-Native Architecture: A .NET Core and Angular framework deployed on Azure using Docker and Kubernetes for unlimited horizontal scalability.
- Real-Time Validation: Automated quality-gate checks that ensure all outputs are accurate and ready for regulatory compliance reviews.
The Impact
The partnership delivered an “intelligent wrangling” platform that has fundamentally redefined the firm’s tax operations.
By automating data preparation and categorization, the organization has shifted from a labor-intensive model to a technology-driven approach capable of managing increasing data volumes with significant efficiency and without increasing headcount.
Results:
- Total Elimination of Manual Mapping: Automated processing has replaced the need for hands-on invoice and transaction categorization.
- Radical Processing Velocity: Data collection, preparation, and categorization are now completed in a fraction of the time required by previous manual methods.
- Superior Accuracy at Scale: The ML-driven validation engine has significantly reduced categorization errors, ensuring higher data integrity for client audits.
Technology Stack
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