The days of learning about data problems from an angry business analyst should be over. With TestGen’s table monitoring, you’re watching everything automatically and continuously.
Data Quality Testing Is At The Core of Four Critical Data Team Processes
What Is The Common Thread Running Through Four Critical Data Team Processes? Data Testing!
Stop Paying The Data Quality Tax
Why Data Observability costs a fortune, why it shouldn’t, and why paying $100,000/year for commodity algorithms is insane!
Data Quality vs. Data Observability: The Pets and Cattle of Your Data Estate
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.
How TestGen Complements Microsoft Purview for Enterprise Data Quality
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 Sins of Data Teams
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.
The Data Consultant’s Growth Playbook: Accelerating Client Acquisition and Retention with DataKitchen’s Open Source TestGen
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
The Seven Deadly Sins of Data Quality
Confess your data sins. Find redemption. Maybe even laugh about it.
The Data Errors Shame Game: Why Data Engineers Avoid Harsh Truths
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.
The Ostrich Problem: Your Data Team Thinks Their Job Ends at Deployment.
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.
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The Equation For AI Success: DT + DX + CTX = 10xMarch 10, 2026
