Data Quality and Automation Support Lead
Using AI in Data Quality
AI is knocking on the door of data quality checks – are we ready to use it?
Let’s be honest: adding new data quality rules to your couple of thousands – manually – is not a great fun. Neither efficient, nor proactive. So why shouldn’t we step back a bit and let the machine do it’s part: it can filter, cleanse, discover, suggest – while we can shift focus to the part where human intelligence plays a key role…
If we can use an adaptive rule engine and data validation, discover sensitive data, even set persona-based alerts and notifications, then data quality will improve, reaction time will decrease significantly.
In my presentation I’ll focus on some key aspects and challenges of this approach, using examples from CollibraDQ.
Márton Pinczel In the last 20+ years delivered several reporting- and data-related projects in companies like Daimler, BOSCH, ABI, Whirlpool and more recently in Morgan Stanley, where he leads the Business Analyst Chapter and the Data Quality and Automation Support team currently.
Though the challenges were different, his aim was the same as it is today: synthesize data into business knowledge – on the most efficient way.