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.
