Enterprise data teams didn’t plan to create access bottlenecks. Most of today’s friction is the byproduct of growth: new warehouses, catalogs, BI tools, and AI workloads layered in to move faster. Each addition made sense on its own. The breakdown happens at the moment of use. Most data consumers don’t...
Over the last few years, data catalogs have delivered on an important promise: they’ve made it dramatically easier for organizations to understand what data they have. Teams can search across domains, see descriptions and quality signals, and finally get visibility into assets that were previously hidden inside warehouses, pipelines, and...
For decades, enterprise data strategies have been built around a simple assumption: people are the primary consumers of data. Analysts request access. Executives review dashboards. Data scientists train models. Governance, access controls, and approval workflows all evolved to support that human-centered reality. Even as data platforms modernized, that operating model...
Most AI initiatives don’t fail because of bad models or weak tooling. They fail quietly, upstream, before the system ever has a chance to prove itself. Teams invest heavily in frameworks, infrastructure, and talent, only to watch momentum slow to a crawl once projects move from experimentation into real operational...
In the early days of data engineering, access was predictable. A handful of power users—engineers and analysts—pre-calculated dashboards for the masses. The permutations of “who can see what data” were limited, static, and typically managed ahead of time. Then came the “Chat with your Data” revolution. Today, LLM agents have...