> For the complete documentation index, see [llms.txt](https://london.worldiaday.org/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://london.worldiaday.org/community/rafaela-ellensburg.md).

# Rafaëla Ellensburg

<div align="left"><figure><img src="/files/DTXwywAL0WNpsFzWzd3s" alt="" width="290"><figcaption></figcaption></figure></div>

### Talk at [London 2026](/events/london-2026.md)

#### Content as Linked Data: Growing a spine to carry meaning at scale

Most organisations treat their content the way they treat a messy inbox: technically it's all there, but nothing connects, nothing surfaces, and nothing scales. Marketing, Customer Service, and Product teams each build their own islands of information — duplicating effort, fragmenting the experience, and leaving content structurally unfit for the AI-driven world being built around it.

Content as Linked Data (CaLD) is a paradigm shift in how we think about what content is. Rather than a collection of keyword strings and unstructured documents, content becomes a network of real-world entities and concepts — semantically connected to products, services, consumers, and every other domain that gives it meaning. No asset is an island. Every piece of content is an inherent part of the business and the digital experience it shapes.

Drawing on her experience structuring and modelling content in complex enterprise environments, Rafaëla will walk through the principles behind CaLD, how to diagnose bottlenecks within the value chain, and what it actually takes to fix them through techniques both humans and machines can navigate.

This talk is a call to action for information architects, content strategists, and designers who want to build something more durable than a content library: a network of meaning that connects people, technology, and business in a way that lasts... and scales.

### Biography

Rafaëla Ellensburg is a Content Engineering Consultant with over a decade of experience in e-commerce management, omnichannel content coordination, and data-driven content strategy. At Albert Heijn, the largest grocery retailer in The Netherlands, she leads the establishment of robust Content Engineering foundations at the intersection of digital, data and tech. In 2025, she founded The Content Engineering Agency to help other organisations structure content as linked data so MarTech can truly scale with more context and less chaos. Rafaela is passionate about creating scalable structures for content that deliver relevant customer experiences with optimal efficiency.

Her explorations in metadata, domain modelling, content modelling and building taxonomies, combined with her holistic perspective on content as linked data for unified digital experiences, make her a thought leader in the field of content engineering and digital experience optimisation.

### Connect

* Website: <https://www.the-ceagency.com/>
* LinkedIn: <https://www.linkedin.com/in/rpellensburg/>
* Instagram: <https://instagram.com/theceagency>


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://london.worldiaday.org/community/rafaela-ellensburg.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.
