Issue 42 - Interface Defines the Search Space

On stage at Forward Data Conference

I'm lucky enough to have a recent shower, which flows hot water from a large shower head. It's a no-brainer, and like many people, it gives my brain a space to wander.

Last week I washed myself into a fairly old shower, with two taps: hot and cold.

This one was quite hard to control: the pressure is controlled by the sum of the (position of the) two taps, while their difference controls the temperature.

It took me several attempts to find the good spot, and then my brain couldn't think anything else than "how broken this interface is"...

Indeed, the interface we are given here is:

• Hot = (1,0)

• Cold = (0,1)

while what we want is:

• Pressure = (1,1)

• Temperature = (1,-1)

Unknowingly we apply the transformation in our head: “I'll adjust both taps to increase water pressure while keeping the temperature steady. Oops, now it's too hot. I'll lower the hot tap, but that'll reduce pressure. So I'll turn up the cold tap to maintain the right water flow…”

Beyond the importance of linear algebra to the art of taking showers in older bathrooms, it make me think (again) of how data interfaces are designed.

Is Tableau an optimal interface for driving decisions?

Is Excel an optimal interface for drilling down into data?

Is SQL an optimal interface for doing business analysis?

Is Python an optimal interface for writing data pipelines?

My take is that we should be way more intentional in choosing these interfaces.

Sometimes I feel we are using two-tap interfaces for things that should be brainless, so our actual brain can wander elsewhere.

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📡 Expected Contents

Hex Explore Feature

I only had Hex in the back of my mind, as one out of the many players in the "notebook for analysts" field. The recent Explore feature raised my attention as it seems nicely engineered. I'm about to give it a try soon.
Do you already use Hex? I’m looking for reviews and use cases 👀

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A take on Medallion Architecture

I would prefer naming like staging, model and presentation. This is much closer to what people already know and expresses what actually happens in the layers. Bronze, silver and gold make it easier to explain to non-technical users. That’s the only reason why the product marketers have gone for Medallion architecture, although whether non-technical users really need to understand “how the sausage is made” is another question.

I had the chance to meet David at the Forward Data Conference this week. He wrote this great piece around the Medallion Architecture: it's probably my go-to whenever I'm doing data modeling but like him, I'm starting to think that it's actually not "modeling"...

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External Intelligence

I want to explore an idea I call externalized intelligence as an answer to this question.

Externalized intelligence is when a certain set of knowledge, or a certain type of thinking, is not integrated into the daily activity of the organism and is instead treated as “importable” or “referenceable” on demand.

For example, I know the concept of multiplication, and like everyone else, I know how to multiply numbers in my head up to, say, 47 squared. But what sort of “intelligence” does it take to multiply 153 by 238?

Not the kind I care about. I can defer this knowledge to a calculator or to ChatGPT. I can treat it simply as something external to me that can be summoned at will.

In fact, I would be disturbed if someone were to boast that they spent the weekend learning multiplication tables up to 300 by heart. At a certain point, knowing this begins to make you seem different — alien.

This position as an alien intelligence is exactly the position I think many data professionals find themselves falling into at organizations. While his peers may respect or even admire his skill with the numbers, he is nonetheless isolated as someone who is not quite on the same team. He is other.

Exploring an old legendary post from Stephen. Nothing to add to the quote above.

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📰 The Blog Post

Things have been busy these last weeks, so no blog post for this time 😢.

However, got nice discussions and drafts going on! So expect some reading from me in the coming weeks 😉

Especially a follow-up of this one.

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🎨 Beyond The Bracket

Last week, I got the chance to get on stage at the Forward Data Conference.¹

Me on stage :)

Getting on stage with my "SQL is not designed for analytics" hot take in front of 300 people was stressful 😅. But I felt on the spot when several C-levels, data influencers, and industry veterans came by to congratulate me, relate to my story, and share that they learned something new.

The after-talk discussions were amazing. There's a palpable sense that we're all experiencing FOMO around a new paradigm: Text to SQL is fading, and we're moving toward the "Text to Semantic Layer" era, here are some references:

👉 Arnaud de Turckheim (CastorDoc) highlighted: "If you dump tons of tables and queries on a new recruit, he won't understand anything without context. The same applies to LLM".
👉 David Jayatillake (Cube) gave a terrific talk about "Text to Semantic Layer". It was interesting to see our talks sharing a common ground in that semantic layer space.
👉 Ethan Ding recent TextQL pivot, see this post.
👉 Looker's spiritual successors are emerging: Malloy (open-source project at Meta led by Lloyd Tabb), Omni (developed by ex-Looker team)

We're still in the "evangelism" phase, and the solution landscape remains blurry. But the conversations from last week convinced me that things would unfold soon 👀.

I might deep dive into this subject more in the coming weeks. I'll keep you posted 😉

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This is the last issue of the year 🎄

I'm so grateful for these past months and how writing is now entirely part of my routines.
I launched recently Liminal Duality as a space to extend this passion. I'm even getting some ghostwriting contracting, and I'm eager to continue developing my skills here.
If you're looking to showcase your work, co-write a blog post, or put a vision on paper, do not hesitate to reach out!

Next year is already calling for great things on my side, and I hope the same for you :)
Have a good end-of-year!

See you soon 👋

1. I'll share a recording when it will be available, you can take a look at the slide deck in the meantime.