In A Masterclass In The Six Types of Data Quality Dashboards, you’ll learn how to build all six powerful data quality dashboard types in under an hour using 100% open source tools.
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.
Peter Piper on the Four Ps of AI Data Quality: Purge, Patch, Push Back, or Pass
How does a data team prevent poor data from poisoning AI when they have piles of raw and imperfect data?
The 2026 Open Source Data Profiling Software Landscape
Data profiling has re-emerged as an essential first step in protecting AI-driven organizations from data-induced failures. Most open-source profiling tools stop at describing data; almost none automatically convert profiling insights into actionable data hygiene checks.
The 2026 Data Quality and Data Observability Commercial Software Landscape
With 50+ vendors to choose from, data quality and data observability software has never been more powerful, more plentiful, or more confusing—until now.
Sure, Go Ahead And Feed That Data To The LLM … What Could Possibly Go Wrong?
Welcome to Analysis-A-Palooza: The Festival No Data Engineer Asked For
Webinar: Data Quality, DataOps, and Large Language Models
AI is changing the world — in this webinar we show how Large Language Model drive the need for DataOps, Data Quality, and Data Observability
You’re Thinking About Data Products All Wrong
When data team leaders hear “data products,” they immediately think of the stuff their team creates. But focusing on the “what” completely misses the mark. Data products aren’t about what you create, but “how” you build, maintain, and continually improve your data deliverables to your customers.
The 2026 Open-Source Data Quality and Data Observability Landscape
We explore the new generation of open source data quality software that uses AI to police AI, automate test generation at scale, and provides the transparency and control—all while keeping your CFO happy.
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The Data Errors Shame Game: Why Data Engineers Avoid Harsh TruthsDecember 31, 2025
