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Dun & Bradstreet’s South Asia Middle East Africa division faced challenges with accuracy in 25 million data records across diverse languages like Arabic and French.
This study details the technical approach taken to effectively address these issues.
Improving data accuracy across multilingual datasets while minimizing manual effort presented significant technical hurdles.
1. Evaluating & Selecting Language Models:
– Compared Large Language Models (LLMs) such as Gemini and OpenAI for parsing accuracy, speed, cost efficiency, translation quality, and scalability.
2. Implementing and Optimizing the Data Pipeline:
– Developed a robust data pipeline using Python on a Google Notebook.
– Utilized advanced techniques like prompt engineering to refine data processing workflows.
– Data Transformation: Systematically normalized, standardized, and corrected data records.
– Data Enrichment: Enhanced data with additional relevant information.
– Validation: Automated validation flags to enhance data reliability.
– Translation: Applied advanced translation algorithms for non-English data.
– Geographical Focus: Initially focused on Pakistan, expanding to other regions.
– Cost Efficiency: Managed processing costs effectively via tokenization (at approximately $200 per 1 million entries).
Through meticulous technical implementation, Dun & Bradstreet significantly enhanced data record accuracy and operational efficiency, leveraging prompt engineering and innovative approaches to deliver reliable insights for informed decision-making in emerging markets.
