The same “pets vs cattle” philosophy that transformed Devops explains exactly when to invest in data quality dashboards versus when to rely on automated anomaly detection—and why you need both.

DataKitchen’s TestGen and Microsoft’s Purview complement each other: Purview serves as the governance and catalog “source of truth,” while TestGen is the deep, automated data-quality and testing engine that writes thousands of data quality tests in seconds.

The five organizational problems are systemic failures of culture and structure that lead to warring teams, a corrosive blame culture, “data blindness” regarding pipelines, a short-term project mindset, and the chaotic scattering of business logic. Learn how to identify and fix below.

A Consultant’s Guide to Using DataKitchen’s Open Source Data Quality and Data Observability Tools: How to Find New Clients, Strengthen Relationships, and Deliver Better Results

Confess your data sins. Find redemption. Maybe even laugh about it.

Many professionals would rather *not* know about data quality problems. Isn’t finding and fixing issues the job? Yes … but organizational dynamics around data errors punish the messenger. Here’s how to fix that dynamic.

What to do when your team doesn’t care about data errors in production? The “deploy and forget” ostrich mindset is one of the most corrosive patterns in data engineering teams. Here’s How to Change That.

How does a data team prevent poor data from poisoning AI when they have piles of raw and imperfect data?