Free Early Release of Chapter 1: The Data Quality Imperative
We're excited to share the first chapter, The Data Quality Imperative of our book Automating Data Quality Monitoring at Scale, published by O'Reilly.
This chapter will give you a valuable understanding of the impacts of poor data quality and how modern organizations should approach data quality as a long-term, continuous effort.
Data quality issues are inevitable, and unfound data quality issues can create long-term shocks and scars in your organization. Data quality issues need to be found as they occur and resolved as quickly as possible.
Automated data quality monitoring can help you detect issues, triage bugs quickly, and address root causes before anyone is affected.
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Co-Founder and CTO at Anomalo
Prior to Anomalo, Jeremy was the VP of Data Science at Instacart, where he led machine learning and drove multiple initiatives to improve the company's profitability. He’s applied machine learning and AI technologies to everything from insurance and accounting to ad-tech and last-mile delivery logistics.
Previously, he led data science and engineering at other hyper-growth companies like Sailthru. He’s also a recognized thought leader in the data science community with hugely popular blog posts like Deep Learning with Emojis (not Math). Jeremy holds a BS in Mathematics from Wichita State University and an MBA from Columbia University.
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Head of Content at Anomalo
Paige has worked with clients such as Airbnb, Grammarly, and Samsara, as well as successful startups like CodeSignal, Tecton, Clerky, and Fiddler.
She specializes in communicating complex software engineering topics to a general audience and has spent her career working with machine learning and data systems, including 5 years as a product manager on Google Search. She holds a joint BA in Computer Science and English from UC Berkeley.
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