Blog
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
Data quality problems don't just appear out of nowhere. They fester in organizational cultures where dysfunction becomes normalized, where warning signs are ignored, and where the path of least resistance leads to ever-growing data debt. After years of working with...
The Data Errors Shame Game: Why Data Engineers Avoid Harsh Truths
Here's something we don't talk about enough in data engineering: shame. The quiet, corrosive feeling that if something is wrong with the data, it must be your fault. And if you're the one who finds the problem, you're the one who'll have to answer for it. This shame...
The Ostrich Problem: Your Data Team Thinks Their Job Ends at Deployment.
Your team has gone full ostrich. Heads buried deep in the sand, convinced that if they can't see the production data errors, those errors must not exist. The pipeline is deployed. The tests passed. Job done, right? Meanwhile, you're the one fielding angry Slack...
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? Teams responsible for data used to train AI models (e.g., LLMs) face a persistent problem: piles of raw, imperfect data. Pressure builds to process quickly,...
The 2026 Open Source Data Profiling Software Landscape
🔥 TLDR: What You’ll Learn in This Article AI has fundamentally changed the stakes: bad data no longer just breaks dashboards—it can actively mislead your LLMs and automated decision systems. Data profiling has re-emerged as the most essential first step in protecting...
The 2026 Data Quality and Data Observability Commercial Software Landscape
With 50+ vendors to choose from, data quality software has never been more powerful, more plentiful, or more confusing—until now. Let's be honest: the data quality and observability market has become a jungle—and it's consolidating fast. Datadog swallowed Metaplane to...
Sure, Go Ahead And Feed That Data To The LLM … What Could Possibly Go Wrong?
Data teams today face a harsh reality: frequent pipeline failures, reactive fixes, and poor data quality often lead to bad decisions. Just when we thought we understood our data challenges, Large Language Models (LLMs) radically expand the use and scope of data ......
Webinar: Data Quality, DataOps, and Large Language Models
Data Quality, DataOps, and Large Language Models Struggling to bring order to the data and AI chaos?The reality for many data teams is often an unproductive mix of broken pipelines, reactive problem-solving, and “good enough” data that leads to poor...
You’re Thinking About Data Products All Wrong
Data Products Are a 'How,' Not a 'What.' When data team leaders hear "data products," they immediately think of the stuff they produce: dashboards, datasets, models, and warehouses—but focusing on the "what" completely misses the massive shift that data products...
The 2026 Open-Source Data Quality and Data Observability Landscape
When AI Meets Bad Data, Everyone Loses: A Definitive Guide for Data Engineers, Data Quality Professionals, and Data Team Leaders; October 2025 Your LLM just told the CEO that revenue is up 40% when it's actually down. Your analytic engineers are vibe coding late into...
Webinar: The FITT Way To Data Products: A New Data Architecture For A Product-Centric World
Transform Your Data Engineer Team from Firefighters to Data Product BuildersShift your data-architecture-focused thinking to a data-engineering-productivity-focused design. FITT democratizes system ownership, enables junior developers to contribute confidently, and...
DataOps Data Quality TestGen Expands: Now Supporting BigQuery and Apache Iceberg
We're excited to announce two major expansions to DataOps Data Quality TestGen Enterprise that bring intelligent data quality testing to even more of your data ecosystem. Whether you're working with Google BigQuery or managing file-based data through external tables,...
