New look but still the same mix of business-serious insights and party-worthy takes on data, exposing the raw, unfiltered thinking behind real data wins.
Seven years is a long time to live with something you know needs fixing.
Especially when fixing it means spending a small fortune and hoping you'll like the result.
Then something changed.
Rob has a million dollar story to tell: https://t.co/vUtO7O7Njc
A project can be perfectly on track and still be headed in the wrong direction.
That’s why “working backward from the answer” matters a whole lot more than it sounds like it should.
Especially with AI.
🔗https://t.co/v5DkiAZIPW
Rob’s AI found a rule.
Tiny problem: nobody wrote it.
It had started as something much smaller, somewhere else, and somehow arrived in a new place with a promotion nobody approved.
New Episode:🔗https://t.co/LdyqZdmoFF
You know your technology is becoming a commodity when it’s a dropdown.
Opus? Sonnet? Pick one.
And that got Rob thinking. If the LLM is swappable and the really useful software built around it can be copied, where exactly is the moat?
🔗https://t.co/E5Jd4OOB1k
AI can read everything your company knows. Excellent.
Is everything your company knows organized, accurate, current, consistent, and understandable? Ah.
Data people, you know where this is going.
Listen now: https://t.co/tmBRdCyH0d
“Botsitting” sounds made up.
Rob thought so too. Then multiple journalists wanted to interview him about it.
That’ll get your attention.
Turns out, the ridiculous name describes something pretty interesting.
🔗 https://t.co/bXHLKOr8WQ
Power BI found the people who see a new tool and immediately wonder what else it can do.
AI just found them again.
John Perl, Tim Rodman, and Trent McKinster join Rob to compare notes on Fair Game and a much bigger toolbox.
Listen now: https://t.co/unZttQdaGO
There have always been crafters.
AI didn't create them.
It just made everyone else notice.
🎙️ The Crafter's Edge, with Pedro Rossello
🔗https://t.co/q7PqwCLDPw
"Everyone's sitting poolside and Satya's talking about the deep end."
That might be the best description we've heard of where the AI conversation is today.
This week's episode asks a simple question:
Should businesses spend more time in the shallow end?
🔗https://t.co/YmN3agZXUX
For the last two years, AI has been an all-you-can-eat buffet.
The next phase may be choosing what's actually worth ordering.
That's this week's conversation on Raw Data by P3.
https://t.co/Smt6fXv14t
AI has a funny way of making old problems look brand new.
Data quality.
Business context.
Tribal knowledge.
Those weren't AI problems yesterday, and they aren't AI problems today. AI just has a way of exposing them faster.
🔗https://t.co/68M0XGutpS
We've spent two years asking:
"How smart is this AI?"
We're about to start asking a much better question.
"How much AI does this job actually need?"
🔗https://t.co/1LqqUGge9B
For years, semantic models were something BI people cared about.
Everyone else just wanted the dashboard to work.
Now AI shows up, starts asking questions, and suddenly all those semantic model conversations aren't optional anymore.
Funny how that works.
🔗https://t.co/xPHKkdC7Xy
The "How Long Has Rob Been Sick?" KPI has officially retired.
The "How Many Pages Has Rob Written?" KPI has retired too.
Which means there's finally a book title to reveal, a pre-order bundle to announce, and a few stories to tell along the way.
🔗https://t.co/UVrXw4bURJ
For years, semantic models were treated like backend plumbing.
Necessary. Kinda boring. Definitely not the star of the show.
Now AI shows up and people are suddenly realizing those models might be the only reason any of this works at scale.
🔗https://t.co/F8PaS5JQ7R
Leadership keeps saying the same thing: spend more on AI.
No target. No use case. Just go do something.
Meanwhile, the real wins are coming from people fixing the annoying Tuesday afternoon problems nobody else touched.
🔗https://t.co/zb6mpr1bJW
We changed the podcast handle.
We're now @RawDataWithRobCollie
Same podcast. Same conversations. Business, AI, leadership, and the occasional topic nobody expected to matter until it did.
See you there.
This episode isn’t really about healthcare.
It’s about what happens when AI stops waiting for systems to cooperate.
And just goes around them.
🔗https://t.co/zlvVslVONT
Same example. Completely different reactions.
One person sees real work getting done. Another thinks it’s overhyped nonsense.
They’re not arguing about the same thing. They’re operating with different context.
🎧https://t.co/FKoafrs5BX
This starts as a job hunt story.
It doesn’t stay one.
Because once the system is in place, the use case stops mattering.
Search
Filter
Verify
Track
Adjust
And that pattern is everywhere.
This is the shift from "help me think" to "handle the busy work."
🔗https://t.co/VHN6pUrwmm