Automated Data Quality Management Through Machine Learning With Anomalo
Data Engineering Podcast
Machine Learning
One of the challenges of machine learning is that it can be unpredictable. I was curious what your process has been for being able to validate the logic and the statistical models of those machine learning approaches. And we actually do this in the data warehouse. Will create temporary tables that int uce these issues and mask the underlying data. O ranges from simple things like onu value increases or dropping rows, to things that are more nuanced.
Summary
Data quality control is a requirement for being able to trust the various reports and machine learning models that are relying on the information that you curate. Rules based systems are useful for validating known requirements, but with the scale and complexity of data in modern organizations it is impractical, and often impossible, to manually create rules for all potential errors. The team at Anomalo are building a machine learning powered platform for identifying and alerting on anomalous and invalid changes in your data so that you aren’t flying blind. In this episode founders Elliot Shmukler and Jeremy Stanley explain how they have architected the system to work with your data warehouse and let you know about the critical issues hiding in your data without overwhelming you with alerts.
Announcements
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
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- Your host is Tobias Macey and today I’m interviewing Elliot Shmukler and Jeremy Stanley about Anomalo, a data quality platform aiming to automate issue detection with zero setup
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Anomalo is and the story behind it?
- Managing data quality is ostensibly about building trust in your data. What are the promises that data teams are able to make about the information in their control when they are using Anomalo?
- What are some of the claims that cannot be made unequivocally when relying on data quality monitoring systems?
- types of data quality issues identified
- utility of automated vs programmatic tests
- Can you describe how the Anomalo system is designed and implemented?
- How have the design and goals of the platform changed or evolved since you started working on it?
- What is your approach for validating changes to the business logic in your platform given the unpredictable nature of the system under test?
- model training/customization process
- statistical model
- seasonality/windowing
- CI/CD
- With any monitoring system the most challenging thing to do is avoid generating alerts that aren’t actionable or helpful. What is your strategy for helping your customers avoid alert fatigue?
- What are the most interesting, innovative, or unexpected ways that you have seen Anomalo used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on Anomalo?
- When is Anomalo the wrong choice?
- What do you have planned for the future of Anomalo?
Contact Info
- Elliot
- Jeremy
- @jeremystan on Twitter
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Closing Announcements
- Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used.
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Links
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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