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Every data and analytics team juggles multiple responsibilities. You are expected to ensure your data is accurate and fit for purpose. You need to prevent problematic data from entering your data environment. You must stop your data pipelines from delivering...

Why Data Quality And Data Observability Tools Cost So Much (And Why That's Ridiculous). You're paying enterprise prices for commodity algorithms. Here's what's really going on. We understand the struggle. Data pipelines fail, dashboards go stale, and someone in...

Data Quality vs. Data Observability: The Pets and Cattle of Your Data Estate If you've spent any time in DevOps circles, you've heard the phrase "cattle, not pets." Randy Bias popularized this analogy around 2012 to explain a fundamental shift in how we think about...

How does DataKitchen's TestGen complement Microsoft's Purview?  Organizations that deploy Microsoft Purview gain a powerful foundation for data governance, cataloging, and lineage across their Microsoft ecosystem. But once teams begin governing data assets, they...

In the first part of this series, we explored the Seven Deadly Sins of Data Quality, focusing on the individual behaviors that sabotage data teams: shame, denial, avoidance, passivity, laziness, gluttony, and ignorance. But here is the uncomfortable truth that nobody...

https://datakitchen.io/partner-request/If you’ve worked in data and analytics consulting, you know the hardest part isn’t always the technical side. The real challenge is finding new clients, growing existing accounts, and proving your value to justify your fees. The...

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...

  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...

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...

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,...