Why Data Products Matter
The true revolution of artificial intelligence begins with data, which serves as the bridge toward an ecosystem of autonomous agents that are transforming the way we work. In today’s paradigm of artificial intelligence, the conversation focuses almost entirely on models and the technology behind them. However, little is said about the fact that no technology can generate sustainable value without a solid foundation: data products. These products transform the natural entropy of information into organized, governed assets prepared to be consumed by machine learning and AI processes.
Data Products and Domains: The Key to Scalability
A data product is not simply a file, dataset or pipeline. It is an organizational asset designed with a clear business objective, a responsible team and a defined lifecycle. Like any other digital product, it has users, use cases, and success criteria. The difference is that instead of offering functionality, it delivers reliable, ready-to-use data for people and systems.
Applying the product concept to data means giving it structured planning and maintenance under a defined owner who evaluates results, incorporates business feedback, and prioritizes according to strategic objectives. The benefits are clear: datasets stop being isolated collections and become trustworthy and secure assets with explicit capabilities for discovery, exploration and comprehension.
A data product only makes full sense within a business domain. Customers, finance, operations, products or people are examples of domains that group data products according to functional responsibilities. This approach avoids technical silos and ensures scalability as each domain becomes a “unit of truth” with defined responsibilities around quality and alignment with business objectives. In this way, data products are no longer merely technological assets but become strategic components that strengthen collaboration between business and technology.
Governance, Quality and the Data Product Lifecycle
Trust in data products must rely on a robust framework of governance and quality. This requires clear ownership, access and integrity policies with control metrics to audit performance. The traceability and simplicity of the data model make continuous review and monitoring easier.
"Data products transform the natural entropy of information into organized, governed assets prepared to be consumed by machine learning and AI processes."
This framework translates into cross-organizational trust: business areas know that when they consume a product, they are using validated and reliable information. That trust is the foundation upon which artificial intelligence can truly deliver value.
At the same time, data products follow a defined lifecycle that includes design, development, deployment, monitoring and evolution. Each stage incorporates business feedback to ensure long-term relevance allowing the construction of a domain-based portfolio that can be combined and extended according to strategic needs. This approach prevents the proliferation of ad hoc solutions and enables sustainable data architecture for the entire company.
From Data Products to a Collaborative Agent Ecosystem
Agents, in order to be autonomous and accurate, need a solid base of reliable and structured information. This is where well-designed data products provide value, as they can be consumed directly by agents to perform tasks with specific business context. The transformation of a data product into consumable knowledge for agents often relies on RAG architectures, which vectorize the information and make it available for AI entities to query.
A RAG (Retrieval-Augmented Generation) combines a language model with a search layer connected to an external knowledge base, allowing AI to query specific information beyond its original training. This prevents hallucinations and ensures that agents work with data that is updated, reliable and context-rich.
With this foundation, it becomes possible to move from a simple thematic assistant, such as one specialized in finance or customers, to collaborative agent architecture. Each agent is enriched with the context of the data products from its domain and can interact with other agents to execute more complex tasks: a finance agent coordinating with a risk agent to analyze operating margins or an operations agent collaborating with a customer agent to access validated transactional data.
In this way, data products stop being just analytical inputs and become the catalyst for ecosystems of agents that operate autonomously, working together with context and precision to optimize processes, accelerate decision-making and unlock new value streams for the organization.
The Future in This New Context
This approach redefines the role of CIOs and data leaders. It is no longer only about managing infrastructure or producing reports but about designing ecosystems of domain-based data products that feed intelligent agents and accelerate business impact.
Organizations that successfully make this transition will be better prepared to optimize operations through specialized agents with deep business context, accelerate innovation by reusing data products across multiple use cases and reduce structural costs thanks to more efficient and scalable architectures.
In this model, data products are the foundation and agentic AI is the engine that transforms them into a competitive advantage. Each agent, enriched by the context of domain data products, can collaborate with others to solve more complex tasks. The result is an orchestration of autonomous agents working together guided by high-quality data to optimize processes, improve decisions and accelerate value creation for the company.
