Owen Whitlock

AI agents need data dictionaries rebuilt for automated readers, not human ones.

It unifies queries across scattered data without copying anything.

Agents need governance built into the data layer, not bolted on afterward.

Find the right fit for your team's budget, pipeline complexity, and growth trajectory.

Staleness cost determines whether agents need streaming or batch processing.

Bridging the gap between fresh data and AI systems that actually understand what it means.

Bad data kills AI projects before models ever get a chance to fail.

Internal AI agents need data marketplaces built for machine consumption, not human intuition.

Batch and streaming ingestion tools move data from source systems into warehouses and lakehouses.

Power BI's semantic layer excels for analysts but struggles with autonomous AI agents.

Grounding AI agents in a governed semantic layer lifts accuracy from 40% to 83% on data queries.

Data governance, not algorithms, determines whether enterprise AI actually works in production.

Most AI projects fail due to fragmented, undocumented data—not model limitations.

Traditional catalogs built for humans don't serve AI pipelines at runtime.

Four governance layers help AI teams pick the right open source tools.

Dynamic masking evaluates sensitivity at query time, not copy time.