Content is a Data Product

About this Article

Published November 4 2025
By Michael Uzquiano

Tags:
cms, content, data product

A New Paradigm for Content Management

In the age of digital transformation, the lines between content and data are blurring fast. Traditionally, content management systems (CMS) have treated "content" -- such as articles, images, product descriptions, documents -- as curated artifacts, crafted and edited by content teams. Very often, these assets were built by hand and targeted for web sites with human consumers.

Meanwhile, the data product world has been busy defining best practices around treating data assets as robust, reusable, and composable "products" with APIs, schemas, and lifecycle governance.

But as content production becomes a more automated, data-driven, and collaborative process, it's time to recognize what leading-edge organizations already know: content is a data product.

And further yet, it is a data product that is consumed not only by humans, but increasingly by AI and others data ingestors.

From Streams of Data to Content-Driven Experiences

Let’s start with how modern content is produced. In a digital organization, a piece of content rarely originates from scratch. Instead, it is assembled:

-Product descriptions are synthesized from inventory databases, pricing feeds, and marketing copy repositories. -News articles may weave in real-time analytics and multimedia assets pipelined from various teams or services.

Landing pages might draw product shots from a digital asset management (DAM) system, personalized headlines from an AI model, and user-generated reviews from a different internal service.

Technologies like Apache Spark make it possible to combine ("converge") multiple data streams—structured and unstructured—into a unified, real-time content pipeline. These pipelines aggregate, transform, and enrich data sources on the fly, feeding CMSs that power your website, mobile apps, or marketing automation.

Once these data streams have converged, content workers step in. Marketing teams may tweak copy, designers add images, compliance reviewers check legal phrasing, and product managers update specifications.

This collaborative workflow fits the schema of data product development:

Defined Interfaces: Content isn’t just dumped into a CMS and forgotten. It’s versioned, reviewed, and advanced through a workflow pipeline—with changes tracked, dependencies managed, and outputs tested.
Collaboration Layer: Just as data product teams use collaboration tools (e.g., code review, test harnesses), content teams use editorial workflows, commenting, and inline editing.

Content as a Data Product

Gitana applies the same rigidity to data products as it does to content. Content is itself a first-class data product and it bears the same characteristics and attributes that allow for data products to be verifiable and testable.

These include:

Composability: Content is modular, assembled from many smaller pieces (text modules, images, embeds) just like data products composed of tables, views, or microservices.
Well-Defined Schema: Headlines, body text, author, publish date, tags, image references, and structured metadata all conform to schemas—often validated by the CMS or the API that delivers content to frontend consumers.
APIs: Modern CMS platforms expose content via REST, GraphQL, or custom endpoints, making content available as a service (CaaS), no different from the data APIs of a data product platform.
Security and Governance: Who can update, approve, or publish content? These rights are tightly managed and auditable, and content items can have their own access rules, aligning with the fine-grained governance found in data product platforms.
Lifecycle and Promotion: A piece of content can be staged in test/QA environments, validated, and promoted to production via deployment pipelines—just like any data or software artifact.

Content is not a loose, unstructured blob. It is entirely different from the messy unstructured (and often structured) data that large businesses retain. Rather, content is formed from these data. It is composed nad well-defined. It is the signal separated from the noise.

It is a well-defined, governed asset with lifecycle management, operational controls, APIs, and stakeholders. It lives, moves, and evolves within the organization as a "composable unit of work" -- tracked, tested, and deployed with the same rigor as a data product or microservice.

Consider the role of content in the following capacities:

QA to Production Deployments: Staged content is reviewed, tested, and promoted—ensuring high quality and compliance.
Audit Trails and Rollbacks: Every change is logged, and past states can be restored, ensuring accountability.
Automated Testing: Automated validations (e.g., broken links, policy compliance, accessibility checks) can run on content, treating it as a testable artifact.