Thoughts and rants from a veteran CDP contractor and interviews with my peers.

Diverted by design? What the CDP split means for the mid-market

The CDP market didn’t just split into platforms and agents. It quietly diverted the mid-market along the way. This piece looks at what changed, why many teams now hesitate, and how CEPs became the safer path for getting things done.

In last week's part 1, I described how Gartner finally named the split inside the CDP category in their Magic Quadrant for Customer Data Platforms 2026. Platformization on one side. Agentification on the other.

Two futures, both serious, both demanding.

That still leaves a simple question hanging:

Who is this actually built for?

Because when you read this year’s Magic Quadrant carefully, the most telling signal isn’t who moved up or down, which opens another can of worms. It’s the kind of organization the category quietly assumes as the norm.

Large teams. Strong data foundations. Time to

customer data platform

The CDP split is official (and it’s not the one vendors were selling)

For years, CDPs have been stretched like the industry’s Spandex. Data foundation one moment, orchestration layer the next, now even the “context brain” for AI. In this year’s Magic Quadrant, Gartner finally names the split. And it changes how everything should be read.

Reading this year’s Gartner Magic Quadrant for Customer Data Platforms gave me a familiar feeling.

Not, not that of reading a short novel, with its 46 pages. Not excitement. Not disbelief. More the quiet recognition you get when something you’ve been circling for a while finally lands on paper. The conclusions didn’t come out of nowhere. They arrived right on time for the category.

For years now, CDPs have been asked to stretch in every possible direction. Think of them as the industry’s Spandex. One moment they’re a data foundation. Then an orchestration layer. Then

martech

Extending Martech’s Law: why growth leads to decay

Martech stacks rarely fail. They decay. Over time, more human effort is required just to keep the same outcomes. This article explores why ROI erodes quietly, how entropy shows up in real stacks, and why I built the Second Law of Martech scan.

When I wrote The real Martech ROI equation, I was trying to articulate a frustration I kept hearing, and feeling myself. On paper, the business case made sense. The tools were implemented. The dashboards existed. The licenses were paid. And yet, the return felt harder to realise year after year.

Not because the technology stopped working, but because everything around it seemed to require more effort.

That article stayed with me longer than I expected. Especially one unanswered question: if the investment is already sunk, why does value still feel like it’s slowly leaking away?

The answer I kept

Martech Stack Builder

Why you haven’t heard from me lately, and why Martech Stack Builder v0.9 needs you

I’ve been quieter than usual lately. No podcasts, fewer articles. Not because I ran out of things to say, but because I was head-down building something that refused to stay small. Stack Builder v0.9 is the first moment where it makes sense to invite others in, before calling anything finished.

I’ve been quieter than usual lately.

No podcasts. No long articles. No hot takes. That wasn’t a content strategy. It was a side effect of being completely head-down building something that refused to stay small.

Alongside my client work, I’ve been spending most of my free headspace on Martech Stack Builder. What started as a lightweight visual tool quickly turned into something heavier, more opinionated, and frankly more demanding than I expected. Writing about it too early felt wrong. The product itself wasn’t stable enough yet, and I didn’t want to narrate something that was

artificial intelligence

I didn’t plan to build a Martech product business, but here we are

What began as an experiment turned into real, deployed Martech. AI made micro viable, but not automatic. Generalist experience, security awareness, and relentless QA mattered more than tools. It’s disciplined systems work, just faster.

What started as a side exercise, mostly curiosity-driven, has quietly turned into a real line of business. Today, roughly twenty percent of my revenue comes from building small, focused Martech tools. Not prototypes. Not demos. Actual, deployed, and maintained software.

That alone would have surprised me a year ago, even when Chiefmartec's Scott Brinker and Frans Riemersma shared their Hypertail 'micro SaaS' vision in their State of Martech 2025 report.

When CDP work runs into reality

About nine months ago, a client approached me for something familiar: help selecting and setting up a Customer Data Platform. This is still

Podcast

Why AI strategy fails for the same reason data strategy did

Tiankai Feng on the human behaviors that determine whether your AI implementation accelerates dysfunction or actually solves problems.

Tiankai Feng has been doing a publication tour for his new book, Humanizing AI Strategy, and he noticed something odd about the conference circuit. He keeps getting invited specifically because he talks about the human side of AI, which apparently makes him an outlier. Everyone else is presenting on architecture, model comparisons, and technical capabilities.

This feels like a replay of what happened with data strategy about five years ago. Companies invested in platforms and governance frameworks while assuming the technology itself would solve their problems. It didn't. Most data strategy failures traced back to human behavior, organizational dysfunction, poor

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When CDP work runs into reality