The Significance of O’Reilly’s Data Quality Fundamentals

In November of 2020, O’Reilly Media first approached us with the idea to author Data Quality Fundamentals: A Practitioner’s Guide to Building More Trustworthy Data Pipelines. It was an inflection point for a fledgling company that had only just begun to establish the category of data observability.

We knew it wouldn’t be an easy feat, but we also knew it would be worthwhile – and important Poor data quality is one of the foremost challenges of our industry, and certainly one of the most painful.

It speaks to the very core of our professional identity. Can it be trusted? Is it good enough to make an impact? Ostensibly we are referring to the data, but at times we may as well be speaking into a mirror.

A recent Wakefield Research survey found data professionals spend an agonizing 40 percent of their time managing data quality, and poor data quality impacts upwards of 26 percent of their companies’ revenue. 

We knew the best practices and methodologies we had collected from our personal experience, as well as that of our colleagues and customers, could help data teams start to change that equation. And we knew with that change would come a data renaissance. 

Now, roughly two years and 300 pages later, I’m thrilled to announce Data Quality Fundamentals is now available both online and in print. 

It’s the first book of its kind to help data engineers and analysts understand the critical factors underpinning poor data quality; apply cutting edge technologies to their existing data stacks; and build resilient, observable systems to prevent data downtime from happening in the first place.

In this book, you will learn:

• Why data quality deserves attention now 
• How data engineers and analysts can architect more reliable data ecosystems
• What it takes to identify, alert for, resolve, and even prevent data downtime 
• Technical solutions for conducting root cause and impact analysis on data pipelines
• The critical differences between data quality monitoring and data observability
• Real-world case studies in achieving high quality data from companies like Intuit, Uber, and Fox
• How data lakehouses, data mesh architectures, automation, and other trends will impact the future of reliable data

To access the book for free online you can visit our website. If you are interested in obtaining a print copy for free, I encourage you to register for our upcoming IMPACT summit and keep your eyes on your inbox.

It’s our hope this book will prepare the next generation of data teams as they drive data product development and analytics strategy forward. It has been an honor to work and learn from other experts, including practitioners and data leaders from some of the most innovative companies, about the processes, culture, and teams they’re building to achieve data trust at scale. 

I’m excited to see what the future holds, because this is just the beginning.

Interested in learning more about data observability and data quality? Schedule a time to speak with us or book a demo using the form below!

Our promise: we will show you the product.