Think about the last time you made an online purchase or booked a doctor’s appointment. Behind the scenes, there’s a lot of data flying around – things like your address, payment info, or medical history. If that data is wrong or outdated, it can cause delays, mistakes, or even security issues. Traditional ways of managing data are like fixing a leak after the water has already spilled – slow and reactive.

What if there was a smarter way? A system that keeps an eye on data all the time, spots problems right away, and even fixes them automatically? That’s what this paper is all about: a vision for a new, AI-powered approach to keep data clean, accurate, and reliable – without waiting for errors to pile up.

The demand for trustworthy, timely, and clean data has never been higher. Traditional data quality management relies heavily on manual rule creation, data stewards, and reactive clean-up. This process is slow, error-prone, and fails to keep pace with today’s real-time, high-velocity data environments.

We propose a paradigm shift: Autonomous Data Quality — an AI-driven, self-healing ecosystem that not only detects issues but intelligently corrects them using business context, historical patterns, and real-time feedback. This paper outlines the architecture, use cases, and Collibra integration plan to bring this ground-breaking concept to life in the enterprise landscape.