Automating data quality monitoring at scale through LLMs and causal inference

Purpose and goal:

Validio tackles poor data quality by enhancing it with AI and ML to monitor and prioritize key data assets, improving business outcomes, cutting operational costs, and reducing carbon footprints from data. This project´s development of AI, particularly LLMs, aims to simplify setup, making data management user-friendly and scalable. New AI features will boost data trust through automated configuration and root cause analysis, suggest monitoring strategies, help understand complex data catalogues, and speed up resolving data quality issues.

Expected results and effects:

Implementing automated data quality monitoring significantly enhances organizational efficiency, especially for data-reliant organizations. It improves trust in data quality, crucial for effective decision-making and ML development. Our solution is estimated to reduce standard data management tasks by 80%.

We aim to detect data issues 90% faster, ensure compliance, and reduce bias in data-driven decisions, Additionally, it promotes sustainable operations by minimizing unused data and scales to handle billions of data points daily, boosting capacity for innovation. Approach and implementation:

Key work packages include developing and integrating custom LLMs for data quality monitoring into the Validio platform, with a focus on semantic understanding and predictive accuracy. The project will use advanced AI libraries and cloud-based Kubernetes clusters for scalable model training. Implementation involves continuous integration and deployment, rigorous testing, and adhering to GDPR and ethical AI standards.

Work packages are broken down into manageable tasks with designated owners for accountability, while a project manager oversees budget and resources.