Empower Your Data Governance: Harness the Power of Large Language Models
Large Language Models (LLMs) are currently a trending topic, offering innovative perspectives across various applications. This article will explore practical use cases on how to leverage the potential of LLMs to enhance and streamline your data governance processes.
Introduction and context
Data governance revolves around identifying vital data and ensuring its suitability to drive positive business outcomes, uphold regulatory compliance, and optimize operational efficiency. The data steward plays a pivotal role in this process, tasked with managing, maintaining, and guaranteeing the quality and reliability of an organization’s data assets.
Once critical data elements are identified, the initial steps involve establishing definitions and data quality requirements. Subsequent stages include implementing these quality rules across all relevant systems, ensuring that the data associated with each element is fit-for-purpose.
To illustrate the potential of large language models, such as GPT-4, in assisting data stewards with these tasks, let’s engage in a quick exercise. We’ll focus on the data element ‘Social Security Number’, exploring how GPT-4 can help define quality requirements for this critical data element and generate code for its quality rule implementation.
Data Element Definition
Prompt: “Please provide the definition of the social security number”
Completion:
Data Quality Rule Requirements
Prompt: Please provide data quality requirements for SSN
Completion:
Data Quality Rule Implementation
Prompt: Please provide JavaScript code for data quality rule implementation for SSN
Completion:
Conclusion
Large Language Models (LLMs) have the potential to enhance data governance programs and aid data stewards in increasing process efficiency. The primary focus of data governance is metadata, which carries fewer security concerns compared to corporate data, although security remains a critical consideration before implementation. Moreover, while the content generated by LLMs may appear sophisticated, it should not be used ‘as is’. Instead, it requires careful validation and tailoring to ensure it aligns with the specific needs of the organization.
Disclaimer: The views and opinions expressed in this article are those of the author and do not necessarily reflect the opinions or positions of any entities author represents.
