We announce our third book, The DataOps Way To Data Quality & Data Observability.
Data quality is not a technology problem. It never has been. The data industry has spent years chasing the next tool, platform, or vendor promise, yet data errors still dominate the daily work of data engineers. Teams keep blaming each other when pipelines break, and business stakeholders quietly distrust the numbers they see.
After working with data teams across many industries, we’re convinced the real barriers to data quality are cultural, organizational, and process-related. Teams get stuck in reactive habits: scrambling to fix errors after they happen, pointing fingers across silos, and treating data quality as someone else’s problem. Our third book, The DataOps Way to Data Quality and Data Observability, aims to identify these patterns and offer a better way honestly.
That better way is DataOps. It applies Agile, DevOps, and lean manufacturing ideas to data and analytics work. DataOps isn’t a product you can buy; it’s a set of habits and disciplines that, when followed consistently, turn data quality from a constant crisis into a managed, measurable, and ever-improving part of your data environment. The book covers organizational failure patterns like warring tribes, the data shame game, and the ostrich problem, alongside technical practices that give teams real control: shift-left testing, systematic test coverage, continuous monitoring, and data observability that catches failures before they reach customers.
The main reason data teams struggle with data quality isn’t a lack of technology; it’s a lack of knowledge, vocabulary, and process frameworks to use that technology well. That’s why we wrote this book. We want every data team, regardless of budget or organizational maturity, to have a clear, honest guide for doing this work well.
We also built two free, open-source tools: DataOps TestGen for data quality testing and profiling, and DataOps Observability for pipeline monitoring. The book gives you the vocabulary, the frameworks, and the organizational playbook. The tools give you the means to act on them the same day. Used together, they close the gap between understanding the problem and actually fixing it. This book and these tools share the same belief: better data quality is achievable for every organization and worth pursuing. We hope you find not just ideas that resonate, but practical steps you can start using today.
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