Data quality monitoring is a process that manages and ensures high-standard data within an organisation.
Performed through strategies like automation and full-stack monitoring, companies set their own data quality metrics and KPIs to measure and evaluate it.
In this episode of the EM360 Podcast, Analyst Christina Stathopoulos speaks to Jeremy Stanley, Co-founder and CTO of Anomalo, to discuss:
- Data quality monitoring in a data reliant market
- Using unsupervised ML
- Changes in the last 5 years
Anomalo automatically detects data issues and their root causes, before anyone else. Data quality complaints take a toll on data teams and engineering. Anomalo helps you observe and get ahead of data issues, find and analyze root causes, and share them with your company – so everyone can feel confident in the data driving your business.
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I like the third approach Jeremy mentions about monitoring data, and using ML to sample data to detect a drift in this sample over time. Good episode guys! 👏
The example Jeremy mentioned about how the solutions at Anomalo have helped a company with their data quality monitoring was fascinating!
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