What's actually in Google's Knowledge Catalog
Katie Kaczmarek-15 Sept 2026
Insights
AI & ML
Artificial intelligence, machine learning, and automation in analytics and marketing (130 posts)
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How to make an AI agent accurate in BigQuery
A year ago, I wrote about getting a BigQuery warehouse ready for AI ( Easy ways to prepare your BigQuery warehouse for AI) , but didn't look properly into Knowledge Catalog (known as Dataplex at the time). We decided to go back and look through Google's Knowledge Catalog properly: what's actually in there, how it works and what it relates to. What Knowledge Catalog is Knowledge Catalog is Google's metadata layer for BigQuery data (and a few other sources). It sits alongside your tables rather
Katie Kaczmarek-
What's actually in Google's Knowledge Catalog
Google's Knowledge Catalog has been renamed four times. Data Catalog, then Dataplex Catalog, then BigQuery universal catalog, then Dataplex Universal Catalog, and now Knowledge Catalog, as of 10 April 2026. The API, gcloud and IAM roles still all say "dataplex". If you land on a page that mentions Dataplex and wonder whether you're reading something out of date, you're probably not. That's just the product's fifth name in four years. I spent some time looking through the different sections of K
Katie Kaczmarek-
#146 Assimilated by Anthropic: What We Lost When the Harness Died
Anthropic's clampdown ended the OpenClaw era. Dara and Matthew cover Opus 5, fake GitHub identities in the UK safety tests and EU AI Act Article 50, then ask what was actually lost when the custom harness died, and whether the frustration since is their own fault.
Dara Fitzgerald-
#145 Bye-bye internet: a web built for machines, not people
Bots now make more web page requests than humans do. Dara and Matthew cover the OpenAI sandbox escape, Kimi K3 and Apple v OpenAI, then ask what the web becomes when it can no longer serve people and agents at the same time.
Dara Fitzgerald-
#144 The vanishing rung: what's left when agents do the work
As agents take on more of the routine work, what does the analyst's role become? Dara and Matthew cover GPT-5.6, Fable and the cost of AI, then ask where humans add the most value when the resource being automated is intelligence itself.
Dara Fitzgerald-
#143 Stick, twist, or wait for Clippy: A week inside OpenAI
We spent a week testing OpenAI's ChatGPT, Codex and deep research against Claude, including a blind four-way build-off to make a running-race predictor. Where does each tool win, and would we switch? Plus Anthropic's near-trillion valuation and the case for Clippy.
Dara Fitzgerald-
The problem with Agentic Analytics
An AI agent will answer any question about your data, confidently and often wrongly. Agentic analytics works only on a governed semantic layer. Here is why.
Mark Rochefort-
You can't buy data strength. You build it.
Google made data strength central at GML 2026 and now measures it in Google Ads. But it is not a setting you switch on. Here is what actually builds it.
Mark Rochefort-
#142 Google's agentic era: Next '26 and I/O '26 unpacked
Dara and Matthew unpack Google Cloud Next '26 and I/O '26: the Gemini Enterprise Agent Platform, Gemini 3.5 Flash, Omni, Antigravity 2.0 and generative AI search. Plus a breaking-news bulletin on Anthropic's new Fable model, and why agents are the headline but governance is the subplot.
Dara Fitzgerald-
#141 Introducing SEAM: Siloed Data, Central Intelligence
Dara and Matthew cover frontier model updates, Mythos, Google I/O, and AI design tools before officially introducing SEAM - Measurelab's semantic governance layer that sits between LLMs and data sources to deliver consistent, audited answers.
Dara Fitzgerald-
#140 Taming BigQuery costs with Alvin.ai (with Martin Sahlen)
Martin Sahlen, CEO of Alvin AI, joins the Measure Pod to discuss automating BigQuery cost optimisation through billing model arbitrage, with no AI in the engine.
Dara Fitzgerald-
#139 The role of AI and semantic layers in BI (with Colin Zima at Omni)
Colin Zima, CEO of Omni & Looker veteran, joins the Measure Pod to discuss revolutionising analytics by integrating AI into modern data tools.
Will Hayes-
The derelict data warehouse, revisited: why this problem just became existential
AI doesn't fix bad data; it scales it. In 2026, a derelict data warehouse isn't just a nuisance, it’s an existential risk.
Matthew Hooson-
Webinar: We tried talking to our data - here’s what’s actually working (and what’s not)
In this 30-minute session, we unpacked what we had been experimenting with using Google’s conversational AI to spend less time wrangling data and more time actually learning from it.
Will Hayes-
The AI revolution already happened
AI's revolution is already here. We're too busy debating its future to harness the transformational power of today's models.
Matthew Hooson-
#135 Twelve highlights of 2025 and future predictions
Dara and Matthew recap 2025 with 12 top highlights, AI, analytics, and the best moments on The Measure Pod.
Will Hayes-
Ask your data a question: the promise (and reality) of natural language to SQL
NL-SQL tools can transform how you query data - but only if your foundations are solid. Here's an honest look at what works and what doesn't.
Matthew Hooson-
#134 Building AI agents, from consciousness to collaboration (with Daniel Hulme at WPP)
AI expert Daniel Hulme, founder of Satalia, shares his journey from UCL PhD to entrepreneur, discussing AI, consciousness & deep tech.
Will Hayes-