customer data platform - Martech Therapy (Page 6)

Contextual CDPs: Contextual Fluency

Part 6 of 6: Being contextual is also about competence.

Over the last five posts, we’ve moved from speculative to strategic to practical. We’ve looked at how memory, inference, and model context protocols can enhance personalization, orchestration, governance, and architecture in and around the CDP. So, where does that leave us?

Right here.. yes, with the last post in the series, but more importantly, with the people, with YOU!

Because it’s one thing to build a system that can understand context. It’s another thing entirely to make that understanding usable across your organization. That’s what this final piece is about, turning contextual awareness into team

Contextual CDPs: Composable, not chaotic

Part 5 of 6: Contextual CDPs need lightweight structure, not heavyweight rebuilds

By now, we’ve explored what context can unlock in a CDP, from dynamic profiling to real-time orchestration and trust. We’ve talked about layering memory and inference without blowing up your current stack. So in this fifth part, let’s look at what happens when you want to make these contextual layers stick… without making everything feel like a pile of duct-taped services.

Because that’s the risk. Composability gives us incredible flexibility, but without some architectural discipline, it becomes hard to explain, harder to maintain, and impossible to govern.

Let’s explore how to structure context-aware systems in

Agentic AI & the art of letting go

When control gets in the way of progress

Paul suggested the idea of doing something different. No intro. No big kickoff. Just two people talking.

That’s how this episode with Paul Meinshausen (CEO at Aampe) started and I’m glad it did. Because I fumbled at the beginning (you can skip ahead in the podcast...).

Feeling like a one-trick pony, I tried to steer the conversation toward my usual topics: CDPs, AI, use cases, and organizational challenges. But it quickly became clear I was trying to force an idea, a vision, onto something I hadn’t fully wrapped my head around yet. Paul helped me take a

If the warehouse can think, what’s left for the CDP?

A critical reflection on Databricks, data gravity, and what comes next.

Nope, this isn’t an analysis of a press release. We’ve seen all Databricks partners do that generously all over LinkedIn. It’s a reflection, a personal one (I had some spare time over the weekend), on something that’s been building quietly in the background and what it means now that someone has taken a concrete step forward. That someone is Databricks.

Last week, Databricks launched something called Data Intelligence for Marketing. And sure, you could skim the headline and file it under “AI feature drop.” But that’s not what caught my attention.

What stood out was

Are MCPs the USB-C of AI? Tealium Thinks So

A conversation with Tealium’s Sav Khetan and BJ Allmon about MCPs, AI integration, and the role of CDPs in a multi-agent world.

In this episode of Diagnosis Demo, I sit down with Sav Khetan and BJ Allmon from Tealium to explore their recent implementation of MCPs (Model Context Protocols). We discuss what MCPs mean for real-time AI integration, how Tealium positions itself as an AI enabler, and why the future might just belong to multi-agent architectures. We also demo a real use case that shows how fast and powerful Tealium's setup really is.

Watch to see:

A working demo using Tealium’s Moments API and MCP integration in a simple chatbot
The difference between MCP and A2A protocols
Why data orchestration, not

Contextual CDPs: Building the bridge

Part 4 of 6: What connects today's CDPs to tomorrow’s agent-driven context

If the first three parts of this series explored what’s possible when context becomes the driving force behind customer data, then this one asks a more grounded question:

How do we actually get there?

I’ve spoken with teams who are genuinely excited by the idea of using memory, inference, and context to make their CDPs smarter. But many feel stuck between inspiration and implementation. They see the potential, but don’t quite know where to start.

Let’s be honest, the martech stack is already complex enough. The last thing anyone wants is to bolt on a new

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