Exclusive Preview: Chapter 3 of the Anomalo + O’Reilly Book on Automating Data Quality Monitoring at Scale

June 21, 2023

While nearly every business today relies on high-quality data, no two businesses have exactly the same data quality needs. Looking to measure the ROI of data quality monitoring at your organization and understand the factors that go into this equation? Download “The Business Impact of Data Quality Monitoring,” Chapter 3 of our new O’Reilly book for data leaders and practitioners at growing enterprises. 

Click here to download Chapter 3 for free

Chapters 1 and 2 are included in your download, covering the negative effects of data quality issues and the pros and cons of different monitoring approaches. While the full book will be published later this year, we’re continuing to give away more “early preview” chapters as they become available. Follow us on our blog and LinkedIn for updates!

About the book

Automating Data Quality Monitoring at Scale is based on everything we’ve learned from building Anomalo. In Chapter 3, we offer advice for teams trying to decide if it’s the right time to invest in automated data quality monitoring.  

You’ll learn:

  • What the “4 V’s” of your data can tell you about your data quality needs
  • Why automated data quality monitoring can make your AI/ML models safer
  • How monitoring helps you comply with regulatory requirements in your industry
  • The pieces of the data stack that you should have in place before you add monitoring 
  • The features that different stakeholders require from an automated data quality monitoring platform
  • Quantitative and qualitative ways to measure ROI

…and much more. 

Note that the contents of this preview will almost certainly change as we continue to craft the book and get feedback from early readers. If you have ideas to share or notice anything amiss, please let our editorial team know by reaching out to gobrien@oreilly.com.

Written By
Rich Taylor
Try Anomalo with your team for free.
Lorem ipsum dolor sit amet, cour adipiscing elit ullam congue.
Data observability might be sufficient if you’re in the early innings of your data journey, but if you’re using data to make decisions or as an input into ML models, as our customers are, then basic checks are not enough to ensure your data is accurate and trustworthy.
Jobin George
Staff Solutions Consultant, Cloud Partner Engineering