Customer Data Platform articles and analysis

How to write a data request someone can act on

A request rarely gets refused. It gets built, exactly as written. Here are the six decisions to name before you send one. One page, free, no forms.

Sponsored by Databricks. Databricks commissioned and funded this guide and reviewed it before publication for factual accuracy. They did not get to change the conclusions.
Full disclosure at the foot of this post.

A badly written data request rarely gets refused. It gets built.

That is the part people miss when they complain about the queue. You ask for lapsed customers, three weeks later you get lapsed customers, and the team who built it did exactly what the message said. Then the campaign underperforms and nobody can point at the mistake, because there was not one.

Lapsed meant a

Same word, different meaning

2 cranes. Same words, different meaning. Martech's vocabulary is no different.

The words marketing and data engineering both use and define differently, and what each side hears instead. One page, free, take it into the meeting.

Sponsored by Databricks. Databricks commissioned and funded this guide and reviewed it before publication for factual accuracy. They did not get to change the conclusions. Full disclosure at the foot of this post.

A client of mine moved from head of IT to head of Martech at a national UK company. Technical background, ran engineering teams for years, nobody's idea of a novice. The first honest thing he said to me was that he didn't know how to talk to data engineering.

That surprised me, and then it stopped surprising me.

He had the vocabulary, and the

Identity Resolution: a practitioner's guide to who owns the match

Identity resolution is a set of business decisions about who counts as the same person. Five approaches, and what each one costs you in ownership.

In part 4 of the Agentic CDP series I made a claim and then moved on rather quickly.

Identity resolution stops being a data-team hygiene project and becomes a precondition for anything agentic being trusted with a budget.

That deserved more than the paragraph it got, so here is the longer version.

Your agentic CDP will optimize whatever you measure

Every agentic CDP will be sold on goals-based marketing. That assumes a goal an agent can chase and a signal fast enough to learn from. Most teams have neither.

Martech Therapy: CDP strategy and agentic AIMatthew Niederberger

Right-time marketing and the speed of context decay

"Real-time" hides three different clocks. What it means from a data warehouse, why context decay is the real metric, and where five CDPs draw the line.

Every customer data vendor sells real-time. It's on the homepage, in the demo, in the answer to question fourteen of that RFP in front of your nose. The word carries a lot of weight, yet most of that weight stays out of view.

Ask what real-time actually means once the data lives in a warehouse, and that single word splits into three separate jobs that you can remember with the CDP acronym, too.

The first is collect: getting an event into the system at all. A click, a purchase, a page view, arriving through an SDK, a

Did CEPs just hand the data layer to Databricks?

Three engagement platforms made the same bet in the same week. Whether it pays off is more contingent than the announcements suggest.

In 2019, I had a client running both Segment and Iterable, and the recurring question was where to build the audiences. Segment could build them. It could not act on them. Only Iterable could turn an audience into a journey, so that is where the audiences got built. Segmentation followed the system that could do something with it. Segment lost that job, and before long it was replaced by Hightouch. Sound familiar?

I think about that sequence whenever the industry decides it has finally settled where customer data belongs, because the answer never stays put.

The latest move arrived in

Databricks CustomerLake: the data lakehouse is now the CDP

Databricks launched CustomerLake, a lakehouse-native agentic CDP. What it means for composable vendors, Databricks customers, and the next data platform.

Databricks announced CustomerLake at its Data + AI Summit just now, and the rumor is confirmed: it is a customer data platform. A real one. In 2026 no less. Marketer-facing, built natively inside the lakehouse, with agents doing the work that used to need a data team and a few weeks of lead time. The first agentic CDP a lakehouse has built for itself.

Only 16 people guessed right on my LinkedIn poll.

To be fair, I saw it before the keynote. Databricks invited me to a preview and asked what I made of it, which I took as a

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