Developers are leveraging generative AI to improve data quality, addressing challenges in building trustworthy AI applications. Generative AI models, while effective at processing human-like responses, often struggle with using internal company data, a critical component in enterprise settings. To maximize the value of generative AI, accurate, timely data is essential for applications like employee PTO inquiries or customer service queries. Data governance plays a crucial role in ensuring this accuracy by applying metadata to describe the structure and provenance of data, creating a foundation for higher-quality AI systems.
Generative AI can also assist developers by automatically generating metadata, streamlining a traditionally laborious process. Although human oversight remains necessary to ensure accuracy, AI-generated metadata improves efficiency and scalability in managing large datasets. Data streaming platforms, designed to handle real-time data, offer a suitable environment for applying metadata at the point of data production. By integrating AI into workflows and enhancing data governance, developers can boost innovation and productivity while mitigating operational risks. The industry is increasingly recognizing the importance of governance, with developers playing a key role in shaping the future of AI-powered data management systems.
