Data Standardization with LLM
Description
The purpose of this project is to enhance data standardization efforts by leveraging large language models. to improve data cleaning and standardization processes, ultimately enhancing the overall efficiency and accuracy of data
Detailed example
This would result in a higher quality dataset with increased standardization and increasing usability of the insights for staff.
AI / analytics pattern
Agentic AI: AI systems that perform tasks or make decisions autonomously with minimal human intervention.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Improve data cleaning and standardization processes within our data systems. ? Enhance the accuracy and reliability of data stored. Streamline workflows by automating certain tasks related to data cleaning and standardization. ? More specifically, the ultimate scope of this project includes integrating into the existing infrastructure of FluLIMS. The data as well as rules . For example, sometimes we have misspelled locations or various ways of referring to the same place (i.e., ATL vs Atlanta) and we would like to standardize that. ? By implementing this proposed project, we anticipate significant improvements in data cleaning and standardization processes within FluLIMS, leading to enhanced efficiency, accuracy, and overall effectiveness in managing flu-related information.? This will most likely lead to up to at least 50% reduction in time and effort to clean data. This can be used for other cleaning other data or other processes that LLM would be useful for.
Controls / human review
ATO: Not reported; PIA: Not published