AI Transforms Data Quality Engineering for Modern Enterprise

Author(s): Valentin Podkamennyi Originally published on Towards AI. Explore how AI-augmented data quality engineering is revolutionizing enterprise data platforms by shifting from rule-based to self-learning systems. Modern enterprise data platforms are characterized by their petabyte scale, the ingestion of fully unstructured data sources, and their constant evolution. In such dynamic environments, conventional rule-based data quality systems often prove insufficient. These systems rely heavily on manual constraint definitions that struggle to generalize across messy, high-dimensional, and rapidly changing datasets. Artificial intelligence enhancing data quality processes. Credit: UnsplashAI-augmented data quality engineering is transforming data quality from deterministic checks to self-learning systems, employing advanced techniques like deep learning and generative models to handle the complexities of large, unstructured data. These frameworks, such as Sherlock and Sato, utilize context-aware semantic understanding and inline ontology mappings to enhance accuracy and reduce human intervention. By automating and optimizing data cleaning tasks, organizations can achieve reliable data quality, enabling better decision-making and supporting robust analytical models in a dynamic enterprise environment. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI