Your data quality tools are like that neighbor who won’t stop describing his rash at parties — great at describing problems, terrible at telling you what to do about them. Impact Dimensions cut through the noise by organizing data quality issues into four categories that reveal which problems actually matter, where to fix them cheapest, and why the ones you’re ignoring may be costing you the most.
VC overinvestment causes predatory usage-based pricing and threatens vendor sustainability. How many data engineers will you need to lay off to cover your data observability costs this year?
We read 849 comments across 18 community threads on Reddit, Hacker News, Stack Overflow, and the dbt Community Forum. The #1 reason data engineers don’t test: nobody gives them the time. The #2 reason: the data changes faster than the tests can keep up. Here’s the full breakdown, in their own words.
AI coding tools like Claude Code are generating significant excitement in software engineering. But for data engineers, getting 10 times the productivity isn’t automatic. Just adding an AI agent to a messy pipeline and hoping it works usually leads to failure.
How to Make Data Analysis Ten Times Faster with AI and Large Language Models
Most data quality advice tells you what to measure. This book tells you why your team keeps failing and what to actually do about it.
This article pulls back the curtain on why standard data quality dashboards fall short. We’ll reveal six powerful, and perhaps surprising, truths about data quality dashboard failure.
We asked the r/dataengineering community ‘Why don’t you test?’ And we got roasted. Learn why
Watch this on demand webinar where we named names, broke down exactly what you’re paying for (spoiler: $300K a Year for a Z-Score?), and showed how TestGen delivers the same capabilities without bankrolling someone else’s Series C.
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.
Impact Dimensions Cure A Data Quality Blind Spot
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