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To provide the best experiences, we use technologies like cookies to store and/or access device information. Consenting to these technologies will allow us to process data such as browsing behaviour or unique IDs on this site. Not consenting or withdrawing consent, may adversely affect certain features and functions.
Functional
Always active
The technical storage or access is strictly necessary for the legitimate purpose of enabling the use of a specific service explicitly requested by the subscriber or user, or for the sole purpose of carrying out the transmission of a communication over an electronic communications network.
Preferences
The technical storage or access is necessary for the legitimate purpose of storing preferences that are not requested by the subscriber or user.
Statistics
The technical storage or access that is used exclusively for statistical purposes.The technical storage or access that is used exclusively for anonymous statistical purposes. Without a subpoena, voluntary compliance on the part of your Internet Service Provider, or additional records from a third party, information stored or retrieved for this purpose alone cannot usually be used to identify you.
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The technical storage or access is required to create user profiles to send advertising, or to track the user on a website or across several websites for similar marketing purposes.
Enhancing Data Record Accuracy in Emerging Markets
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.
Challenge:
Improving data accuracy across multilingual datasets while minimizing manual effort presented significant technical hurdles.
Solution:
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 Processing Activities:
– 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).
Outcome:
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.
RAK Free Trade Zone P O Box 16111 Ras Al Khaimah, UAE
Suite 1003-4, Park Avenue, 24A, Blk 6, PECHS, Khi, PK
