Here is the hard reality most data teams are facing as we close 2025:

We are drowning in “successful” work that changes nothing.

We have better governance frameworks. We have cleaner dbt projects. We have decentralized ownership. We have technically perfect workflows. But ask yourself: Do we have data on our own impact?

We track the reliability of our pipelines, but not the reliability of the decisions made using them. We celebrate that dbt enabled better data transformation, but we forget that dbt is supposed to enable better business decisions.

I just published a comprehensive guide on implementing Success Metrics for data teams - the missing piece most organizations struggle with.

Covers practical frameworks including: - How to calculate actual Data ROI (with real implementation examples) - The 5 W's method for designing actionable KPIs - Data Utilization measurement (we found one company using only 48% of their 32TB) - Change management strategies that ensure adoption

Includes real implementation lessons from helping a company transition from measuring everything to measuring what matters. The Data ROI calculation methodology alone could save teams months of trial and error.

No fluff - just frameworks you can implement immediately.

I am now working on forming a new data strategy framework. I think that one of the biggest issues we are seeing out there is the rush to GenAI and create automation. However, in reality, people tend to ignore the data that is supposed to be used.

So I started my blog Cooking Data, and slowly, using Vibe Coding, I am building tools to translate the philosophical conversation into actionable tools. I am always happy to learn and discover how the team handles the data strategy and answers the business goals with it

Hey HN,

Long-time lurker here. Been working on a podcast focused on the nitty-gritty of data challenges, cutting through the hype. It's called Data Breakthroughs.

The pilot episode is now live, and I had a great conversation with Ilya Vladimirskiy, a Fractional Data Leader & Consultant at Lab IV (as per his profile). We tackled a common frustration: how to keep data pipelines reliable and maintain trust in data for decision-making.

We didn't just skim the surface; we dug into the real-world implications and started brainstorming practical solutions. Think of it as trying to debug the messy reality of data systems.

My aim is to create a space for honest discussions, moving beyond abstract theory to actionable insights. Hopefully, it'll be useful for anyone wrestling with data reliability and team collaboration.

Keen to hear your thoughts and any feedback you might have. What are the data pipeline nightmares you've encountered?

#dataengineering #analytics #datateams #podcast #realworldproblems

Organizations waste hundreds of hours re-checking data they don't trust. In this final newsletter of my data strategy series, I share the five pillars of data trust and practical implementation techniques that reduced "Is this data correct?" tickets by 78% in three months. The centerpiece is the "Morning Confidence Dashboard" concept that transformed how teams interact with their data. If you've ever struggled with data mistrust killing your ROI, I'd appreciate your thoughts on this approach.

This is my recent article exploring how data quality will become the primary differentiator between organizations that thrive and those that struggle in the era of AI agents. I discuss why companies with the cleanest, most reliable data will win - not necessarily those with the most advanced AI models. The article includes:

Real-world examples of AI failures (like the $1 Chevy Tahoe incident) Assessment of how data governance must evolve Practical checklist for organizations to evaluate their AI readiness Analysis of changing roles for data professionals

I'd appreciate your thoughts and experiences with these challenges.