Ensuring Data Quality For Scalable AI

Here are a few simple truths about Data Quality: 1. Data without quality isn't trustworthy 2. Data that isn't trustworthy, isn't useful 3. Data that isn't useful, is low ROI Investing in AI while the underlying data is low ROI will never yield high-value outcomes. Businesses must put an equal amount of time and effort into the quality of data as the development of the models themselves. Many people see data debt as another form of technical debt - it's worth it to move fast and break things after all. This couldn't be more wrong. Data debt is orders of magnitude WORSE than tech debt. Tech debt results in scalability issues, though the core function of the application is preserved. Data debt results in trust issues, when the underlying data no longer means what its users believe it means. Tech debt is a wall, but data debt is an infection. Once distrust drips in your data lake, everything it touches will be poisoned. The poison will work slowly at first and data teams might be able to manually keep up with hotfixes and filters layered on top of hastily written SQL. But over time, the spread of the poison will be so great and deep that it will be nearly impossible to trust any dataset at all. A single low-quality data set is enough to corrupt thousands of data models and tables downstream. The impact is exponential. My advice? Don't treat Data Quality as a nice to have, or something that you can afford to 'get around to' later. By the time you start thinking about governance, ownership, and scale it will already be too late and there won't be much you can do besides burning the system down and starting over. What seems manageable now becomes a disaster later on. The earliest you can get a handle on data quality, you should. If you even have a guess that the business may want to use the data for AI (or some other operational purpose) then you should begin thinking about the following: 1. What will the data be used for? 2. What are all the sources for the dataset? 3. Which sources can we control versus which can we not? 4. What are the expectations of the data? 5. How sure are we that those expectations will remain the same? 6. Who should be the owner of the data? 7. What does the data mean semantically? 8. If something about the data changes, how is that handled? 9. How do we preserve the history of changes to the data? 10. How do we revert to a previous version of the data/metadata? If you can affirmatively answer all 10 of those questions, you have a solid foundation of data quality for any dataset and a playbook for managing scale as the use case or intermediary data changes over time. Good luck! #dataengineering

The biggest risk to AI isn’t the model. It’s your messy data. Ask yourself:  Would you bet your business on the quality of your data today? Here’s the 9-step roadmap I use to help companies get AI-ready 👇 Levels 1–3: Build the foundation ↳ Define the goal ↳ Audit your data ↳ Clean & standardize Levels 4–6: Structure + centralize ↳ Ensure quality & accuracy ↳ Structure & label ↳ Integrate & centralize Levels 7–9: Govern + scale ↳ Secure & govern ↳ Prepare for scale ↳ Monitor & improve The truth: AI projects don’t collapse at the “AI” layer. They collapse because leaders skip the boring data work. Most leaders ask: 👉 “Which model should we use?” The better question: 👉 “Is our data clean, structured, and governed?” Next step: ↳ Run a data audit this week. ↳ Fix one weak spot. ↳ That’s how you de-risk AI. 🔁 Repost to help more people cut through the AI hype ➕ Follow Gabriel Millien for clarity on AI + transformation

Scaling AI is less about model performance; it's about the infrastructure discipline and data maturity underneath it. One unexpected bottleneck companies often hit while trying to scale AI in production is “data lineage and quality debt.” Why it’s unexpected: Many organizations assume that once a model is trained and performs well in testing, scaling it into production is mostly an engineering and compute problem. But in reality, the biggest bottleneck often emerges from inconsistent, incomplete, or undocumented data pipelines��especially when legacy systems or siloed departments are involved. What’s the impact: Without robust data lineage (i.e., visibility into where data comes from, how it’s transformed, and who’s using it), models in production can silently drift or degrade due to upstream changes in data structure, format, or meaning. This creates instability, compliance risks, and loss of trust in AI outcomes in the regulated companies like Banking, Healthcare, Retail, etc. What’s the Solution: • Establish strong data governance frameworks early on, with a focus on data ownership, lineage tracking, and quality monitoring. • Invest in metadata management tools that provide visibility into data flow and dependencies across the enterprise. • Build cross-functional teams (Data + ML + Ops + Business) that own the end-to-end AI lifecycle, including the boring but critical parts of the data stack. • Implement continuous data validation and alerting in production pipelines to catch and respond to changes before they impact models. Summary: Scaling AI is less about model performance and more about the infrastructure discipline and data maturity underneath it.