Continuous Data Quality Monitoring Using AI/ML

Do you know that measuring data quality once is not good enough? With the growing volume and complexity of datasets, the manual and traditional ways of Data Quality don’t scale and automation is required to scale data quality practices. Traditional methodologies are highly routine and repetitive with mostly manual steps that have not scaled with the growth in datasets being ingested, resulting in only a small percentage of data being assessed for quality. You need something automated, something that’s continuously learning, and something that’s specific to your organization’s data culture.

As part of this session, Raj Joseph, CEO of DQLabs shall present a demo of DQLabs.ai and speak about the use of advanced algorithms to identify data quality issues not just once but continuously and also understand volatility in the data – all automated.

Webinar Highlights

• Overview of some common data quality challenges faced especially around the volatility of data.
• Learn how we can leverage augmented analytics using AI/ML to continuously monitor data quality.
• Demonstration of DQLabs.ai – an AI/ML augmented data quality platform with specific use cases around drift/trend analysis.