Author: Kevin

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…

For years, access governance has operated on a simple assumption: Review access periodically and hope the environment hasn’t changed faster than your governance process. That model made sense when: That world is gone. Modern environments change constantly. New: …appear faster than traditional governance processes can evaluate them. Which means the future of governance probably isn’t…

At this point in the series, we’ve talked a lot about visibility, exposure, and risk. And that’s necessary. But eventually, every governance conversation runs into the same wall: “This sounds great… but people still need to get their jobs done.” That tension is real. Because the fastest way to make governance unpopular is to make…

So far in this series, AI has mostly been the thing exposing the problem. And fairly so. AI amplifies access models.It traverses systems quickly.It exposes weak governance faster than most organizations are prepared for. But here’s the part that gets overlooked: AI can also become one of the most effective tools for understanding access complexity.…