Your AI’s on a Junk Food Diet — Let’s Fix Its Data Nutrition Fast (on a Ramen Budget).

Webinar: November 6th, 2025; 12 pm EST / 4 pm GMT

Stop feeding your AI garbage calories.  
Your analytic engineers are vibe coding late into the night. Your LLMs are confidently serving up garbage insights. Your predictive models—once your pride and joy—are degrading faster than you can debug them. And your standard reports—no one trusts them. And your data team? They're drowning. More pipelines. More models. More stakeholders are screaming for AI magic. But there's never enough time, and there's definitely no budget for another six-figure "enterprise solution." (Ever been to Monte Carlo? Yeah, it’s an expensive trip.)

Sound familiar? You're not alone. And we have answers.

The Brutal Truth About Data Quality in the Age of AI
AI moves fast. Dangerously fast. While you're manually writing test cases, bad data is already poisoning your models. By the time you catch it, the damage is done—failed deployments, lost trust, wasted sprints. The old playbook is dead. Manual testing can't keep pace with AI's hunger for data. You need comprehensive coverage—2 tests per column, 3 tests per table—across hundreds of tables. Yesterday.
You've tried the usual suspects:  Great Expectations, Soda Core, Deequ, dbt-tests, and Manual SQL tests. They're good tools. But they weren't built for the AI era's brutal pace. You need something that fights AI with AI. Something that has a simple UI to share the burden and influence. We will compare and contrast.

What You'll Discover in This No-BS Webinar
We'll expose why data engineers are burning out trying to keep pace with impossible data-quality demands, and how LLMs amplify bad data into catastrophically wrong insights that destroy trust overnight. You'll understand why even your tried-and-true predictive and descriptive analytics are failing without a solid foundation in data quality.

You will learn how to generate comprehensive DQ test coverage in hours instead of months, build intelligent data quality dashboards that truly drive action rather than just look good, and master the skill of influencing stakeholders to care about data quality before disaster occurs, even without formal authority. This isn't just theory; it's practical survival skills for data teams under pressure.

Data Quality Built For The LLM Addicted Organization:  Open Source DataOps Data Quality TestGen

• Auto-generate hundreds of intelligent tests in minutes
• Use profiling to provide semantic  context that makes tests smarter than LLM-generated SQL
• Create custom tests through a UI that your data stewards can actually use
• Show how AI-powered test generation runs circles around manual approaches with total test coverage
• And, with open source DataOps Observability, you can monitor all your pipelines and data in one unified dashboard
  

We’ve been building data quality systems at scale with skeleton crews and shoestring budgets for two decades. We’ve learned what works, what wastes time, and what you can implement before your next standup.

Register Now—because your next model is training on bad data. Every day you delay, bad data compounds. Models drift. Trust erodes. Competitors with better data quality pull ahead.

But here's the thing: You don't need more time or money. You need smarter approaches and tools that do the heavy lifting for you.
Join us on November 6:  No money. No time. No excuses. Just better data → better AI.

Can't make it live? Register anyway to receive the recording and all materials within 24 hours of the webinar.  Or send your AI recording bot.