The term “AI-ready data” has quickly become one of the most overused buzzwords in enterprise technology today. Every vendor claims to provide it — yet most organizations are left wondering: What does AI-ready data actually mean, and how do you build it in a way that delivers lasting business value? At Denodo, our answer is simple: AI-ready data is not about bigger storage or faster pipelines. It’s about delivering business-aligned data products, built on a logical data management layer, enriched with semantics, and immediately consumable by both humans and AI agents.
Data Products: Evolution, Not Revolution
Data products aren’t new — they gained prominence alongside the data mesh movement. At a high level, data products can be thought of as curated, re-usable, and readily consumable data assets designed to address business requirements and objectives. At Denodo, we’ve been champions of this approach for years. The current AI boom has only highlighted the fact that AI agents and applications thrive not on raw data, but on data products that provide meaning and context. The same rich semantics that help a business analyst understand customer churn are the same semantics an AI agent needs to generate accurate, contextual insights.
While the concept is straightforward, it can be complex and challenging to build data products collaboratively and at scale. The conversation must evolve beyond initial hype to embrace proven principles and strategies.
Start with the Business Domain and Context
The starting point for any successful data product is business context, not technology. Too often, organizations build data assets in isolation, disconnected from the language of the business.
This is where a semantic layer, provided by solutions such as the Denodo Platform, becomes essential. It translates technical schemas into business terms that can be easily understood by humans and AI. When extended into a data product framework, this semantic layer enhances data usability, facilitates interaction between AI systems and users, and enables more effective, data-driven decision-making grounded in fact and data.
End-to-End Development and Collaboration
Building data products is not a one-off exercise. It requires continuous end-to-end collaboration — from business stakeholders defining requirements, to engineers designing the data products, to analysts and AI systems consuming the data views and products. Like a well-managed consumer goods supply chain, the delivery of high-quality data products, on time, relies on the close coordination and teamwork of many groups and personas.
While most data management solution providers focus on the needs of data engineers and the development of data pipelines, Denodo recognizes the need to support multiple users through this complex delivery lifecycle and has developed features based on the different needs and skill levels of these users.
For a practical demonstration, my colleague Raúl recently delivered an excellent webinar showcasing how Denodo accelerates the end-to-end data product lifecycle. In just 30 minutes, he highlights key steps along the data product delivery lifecycle through the lens of different personas, including how the Denodo Platform simplifies the process of data modeling, data protection, and data delivery.
AI as Your Development Partner
While AI agents can greatly benefit from a set of well-curated data products, AI can also play a crucial role in the development of these data products.
A perfect example of this is how Denodo Assistant can accelerate and simplify data product delivery within the Denodo Platform. By providing contextually enriched assistance at the right time for the right user, AI can become a valuable teammate in the development process. By making the entire development cycle more efficient, AI-based assistance enables the delivery of higher-quality data products in less time, with less effort.
AI-based augmentation provided by Denodo Assistant can help with everything from data discovery, to metadata enrichment, to query optimization. This enables teams to focus on higher-value activities while AI handles the routine, tedious tasks.
With our most recent 9.3 release, we have expanded the capabilities of Denodo Assistant by adding automated tag suggestion. We believe that the ability to automate and streamline the tagging process will help enrich the metadata layer even further and enable more powerful AI agents and applications.
Discoverable, Useful, and AI-Agent Ready
Data products must be easily discoverable and be consumption-ready, rather than storage-focused. This distinction is crucial. Traditional data management emphasizes where data lives and how it’s stored. Denodo, with our focus on data products, shifts this perspective. We prioritize how data is consumed and by whom. This consumption-first mindset enhances operational value by increasing utility across a wide variety of use cases.
To better serve and deliver data products to AI agents and applications, we recently enhanced our MCP support with the Denodo AI SDK. We believe that by supporting new open standards, such as MCP, we can accelerate the development of powerful, data-driven agentic ecosystems.
With the 9.3 release, we are also extending the data product paradigm and have relaunched our data catalog offering as the “Denodo Data Marketplace”. Beyond the rebranding exercise, we have recently made enhancements to support domain-based navigation and reference lineage, to further simplify data product discovery and consumption for both technical and business users.
From Data Projects to Data Products
The shift from data-project-thinking to data-product-thinking represents more than a change in terminology — it’s a fundamental transformation in how organizations create and capture value from their data.
When you treat data as a product — with a clearly understood lifecycle, well defined consumers, and measurable outcomes — you stop building isolated solutions and start creating an ecosystem of reusable assets. Each new data product strengthens your foundation for AI innovation, reducing time-to-insight from weeks to hours.
We have built the Denodo Platform with this vision in mind, and in future posts, we will explore and highlight how the Denodo Platform enables each of the strategies discussed above. Ultimately, we believe that the organizations that can make this fundamental shift — from thinking about data projects to building data products — will be the ones that thrive in this AI era.
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