Essays on data, work, and personal growth that help you simplify without flattening what matters.

Between the "AI will kill BI" hype and major platforms each claiming to offer the definitive, unified approach to enterprise data, I wanted to understand what is actually coming next for the field.

What to do when the easy wins are gone...

A fresh way to look at your career in data & AI

The economics of AI, the loss of mastery, and why it should matter.

On impact, value, and what data analytics work actually looks like in mature data functions.

It is March 2026, I am looking for a new role, and the market is tough as hell.

I have been raiding other engineering disciplines for ideas that analytics has been too slow to borrow.

Dashboard engineering is the place where structural weaknesses become most visible. When requirement engineering cuts corners, when data engineering lacks rigour, when governance is absent, the dashboard is where all of it surfaces.In the following, I present a distilled diagnostic: five failure modes, their root causes, and their antidotes.

My feed is full of infographics reminding us that a data stack is not a data strategy. I agree with the premise , most organisations mistake “buying tools” for “making choices.”

Since the recent surge of interest in artificial intelligence (AI), I observe an increasing tendency to use AI terminology to describe tools and services. I would like to offer a different perspective on why data remains indispensable, even in this AI-driven era.