Author: Kevin

Throughout this series, we’ve talked a whole lot about what prevents organizations from becoming AI-ready. If you’ve recognized some of those challenges in your own environment, you might be wondering: “So… what does an AI-ready data platform actually look like?” It probably looks a lot less futuristic than you expect. In fact, many of the…

Every organization wants trustworthy AI. Reliable answers. Consistent recommendations. Confident decisions. But there’s a question that often gets overlooked: Who owns the information the AI is learning from? That sounds simple. In practice, it’s one of the most difficult questions in modern data environments. Because many organizations have invested heavily in technology while leaving ownership…

For years, metadata was treated like documentation. Important. Useful. Occasionally referenced. Frequently ignored. Most organizations viewed metadata as something that helped humans understand systems after they were built. A data dictionary. A wiki page. A lineage diagram. A catalog entry. Helpful, but rarely essential to day-to-day operations. That assumption is starting to break. Because AI…

Every organization wants to know if it’s ready for AI. The usual questions sound familiar: Those questions matter. But after years of working with data platforms, I’ve become convinced that they’re not the most important questions. Because most organizations don’t fail AI initiatives because they chose the wrong technology. They struggle because they skipped the…

Whenever an AI initiative struggles, the proposed solution is often surprisingly predictable. “We need more data.” The assumption is simple: If some data is good, more data has to be better. Unfortunately, that’s not how AI works. And in many organizations, more data simply means more confusion.                                                                                                               The Definition Problem Consider a simple business question:…

Most organizations assume their data warehouse is one of the safest places AI can learn from. After all, that’s where the “good data” lives. At least that’s the assumption. The reality is often much more complicated. Because many data warehouses contain years of accumulated business logic that nobody fully understands anymore. And AI has no…

Every organization believes it has a data quality problem of some sort. The list is endless. And while those issues are real, they’re often not the actual problem. They’re symptoms. The visible evidence of deeper issues that exist elsewhere in the organization. Treating bad data without addressing the underlying causes is a lot like treating…

In the first article of this series, I argued that most organizations don’t have an AI problem. They have a data problem. But that’s only a small part of the story. Because even organizations with enormous amounts of data often discover that their AI initiatives struggle for a completely different reason: The organization doesn’t understand…

Every executive meeting seems to have the same question these days: “What is our AI strategy?” It’s a very reasonable question. Artificial Intelligence is advancing rapidly, vendors are embedding AI into nearly every product, and organizations are feeling pressure to demonstrate that they are keeping pace. But after working with data platforms for years, I’ve…