Data has become a cornerstone for business innovation and strategic decision-making. As organizations accumulate vast amounts of information, from customer transactions to transactional data, the need to make this data accessible is more pressing than ever. A central component of the emerging landscape is the data product.
But what exactly is a data product, and why should businesses care?
Defining a data product
At its core, a data product is a curated, accessible wrapper built around high-quality datasets and bundled with relevant metadata. They are designed for easy discovery and consistent use. Unlike raw data, a data product is usually created with a specific intent, usually also for a specific domain team. This team produces the data and ensures its quality, documentation, and readiness for consumption. As such, a key feature of a data product is its sense of ownership, governance, and accountability.
Enhancing data portability and collaboration
This structured approach means that consumers can rely on the data’s integrity and portability. It also means that data products represent a new way to weave data access within an organization, enhancing its uptake and impact. The teams that understand the context aren’t always the ones who build the data pipeline. Data products fix this issue. The people who created the data product are those who know the datasets best and are closest to the data.
This solves issues around data collaboration, traditionally one of the most significant problems in data. For this reason, adoption of data products has significant implications for the data itself, the teams, and the ability to reliably meet data compliance and governance standards.
Components and characteristics of a data product
A data product is essentially an assemblage. Inside the package, it is made up of several distinguishing components.
Components of a data product
The curated dataset is the main data asset. It’s been aggregated, cleansed, and structured for a clear business purpose.
The metadata provides rich context about the dataset. This includes data definitions, lineage, ownership, and quality metrics. (Learn more about metadata.)
Access patterns give clear information on how to query or integrate with the data product. This makes consumption straightforward.
Governance and security features include built-in access controls and compliance rules. These ensure secure and auditable data use.
Discoverability comes from documentation and cataloging that help users find and evaluate whether the data product fits their needs.
Data products apply product thinking to data
Notably, a data product applies product thinking to data. It is typically actively managed over its lifecycle, with deliberate versioning, stakeholder feedback, and continuous improvement. This parallels approaches taken in product management lifecycles, and can be considered a crossover between data management and product management.
Want to know more about data products? Check out this video.
Why data products matter for AI and governance
The rise of artificial intelligence has changed how organizations think about data products. The characteristics that make data products valuable for analytics also make them essential for AI initiatives. Rich metadata, clear ownership, documented lineage, and quality controls already matter for analysis, but they matter even more when AI is involved.
How do data products help AI projects?
AI models require more than just data. They need context. When a data product includes comprehensive metadata about field definitions, data quality metrics, update frequencies, and business logic, it becomes far more valuable for training and deploying AI models. This metadata provides the semantic understanding that AI systems need to generate accurate, trustworthy outputs.
AI hallucinations and context
Consider the challenges posed by AI hallucinations and inaccurate outputs. These issues often stem from AI models lacking proper context about the data they’re working with. Data products solve this problem by packaging data with the rich contextual information that AI needs. The metadata embedded in data products acts as guardrails, helping AI understand not just what the data says, but what it means, where it came from, and how it should be used.
LLMs and metadata
This matters especially for generative AI and large language models. When these systems can access data products with detailed metadata, they can provide more accurate responses, cite their sources properly, and avoid making unfounded assumptions. The governance layer built into data products ensures that AI applications access only the data they’re authorized to use, maintaining compliance while still allowing innovation.
Data products as the foundation for governed AI
As organizations use AI across the organization, data governance becomes critical. Data products provide a natural framework for governing AI access to enterprise data. Each data product comes with built-in access controls, audit trails, and compliance documentation. When an AI application queries a data product, the organization maintains visibility and control.
Data quality
The governance advantages extend beyond security. Data products let organizations set clear data quality standards that AI applications depend on. When a business user interacts with an AI assistant powered by data products, they can trust the outputs because the underlying data has been curated and validated by subject matter experts who documented it thoroughly.
Data lineage
Data products also let organizations trace AI decisions back to their source data. This data lineage matters for regulatory compliance, model explainability, and building trust in AI systems. When an AI model makes a recommendation or prediction, organizations need to know what data informs that output. Data products provide this transparency.
The Starburst approach to data products
Modern platforms like Starburst are engineered to build data products at enterprise scale. Starburst enables teams to connect to diverse data sources across data warehouses, data lakes, on-premises systems, or in the cloud. It minimizes data movement.
Universal data access to curated datasets
Using universal data access, Starburst’s distributed SQL engine allows organizations to provide analytics and AI access closer to real-time. This approach reduces the need for complex, replicated storage infrastructure.
Manage data products
With Starburst data products, domain teams can easily create, curate, and manage data products. The platform provides guided workflows to define queries, assign comprehensive metadata, and establish ownership and governance policies. Teams can then publish these data products to make them discoverable across the organization. (Learn more about Starburst data products.)
Data federation
Under the hood, Starburst data products are defined by SQL queries that can span multiple sources, leveraging data federation. Using this approach, it is possible to aggregate data and create new views or materialized views. These virtualized datasets are surfaced as data products, complete with metadata and governance controls.
The result is a governed data layer that both human analysts and AI applications can consume with confidence. Security is enforced through access control. Users and AI systems see only what they’re permitted to. Integration with governance tools assures compliance and traceability.
Unlocking AI with metadata-rich data products
The true power of data products for AI lies in their metadata. Modern AI applications perform dramatically better when they understand the context behind the data, from predictive analytics to generative AI assistants.
Starburst data products capture this context comprehensively. Beyond basic schema information, they include business glossaries, data quality metrics, ownership information, update schedules, and usage patterns. This metadata transforms raw data into AI-ready assets.
Data product + AI model
When an AI model or agent accesses a Starburst data product, it receives not just the data but the full context needed to use it appropriately. This lets AI deliver more accurate insights, explain its reasoning, and operate within governance boundaries. Organizations can deploy AI applications knowing they’re built on a foundation of trusted, well-documented data.
The metadata also helps AI find relevant data products on its own. An AI assistant can search for the right data products to answer a user’s question, identify which products contain the required information, and intelligently combine multiple data products. All because of the rich semantic information embedded in each product.
The benefits of data products
By adopting the data product approach, organizations can realize several key benefits.
Enhanced domain ownership
Domain teams can independently deliver and iterate on data assets, reducing bottlenecks and promoting innovation across both analytics and AI initiatives. This increased agility matters when organizations need to move quickly.
With explicit ownership, data products are curated, reviewed, and continuously improved. This creates the reliable foundation AI applications require, improving data quality and trust across the organization.
Access controls
Fine-grained access controls and metadata ensure regulatory requirements are met and data use is auditable, whether accessed by humans or AI. This provides consistent governance.
AI adoption benefits
Rich metadata transforms data products into assets that AI can understand and use effectively, reducing the risk of hallucinations and improving output quality. The data becomes AI-ready.
Universal data access through data federation
Data products can be federated across business units, cloud platforms, and storage technologies, supporting enterprise expansion of both analytics and AI. This scalability helps organizations grow.
Reliable, easy-to-find, and well-documented data products give both human users and AI applications what they need to generate actionable business insights faster. This enhances business value.
Data product vs. data catalog
It is important to distinguish between a data product and a traditional data catalog. A catalog lists datasets, their locations, and metadata for discovery. A data product, in contrast, is the active asset itself. It’s curated, governed, and published intentionally for consumption, not simply indexed.
While catalogs help users find data, data products provide the governance, quality, and metadata structure that make data truly usable, especially for AI applications that need deep contextual understanding.
In Starburst’s implementation, creating queries that define and generate views is central. Organizations aren’t just cataloging what exists. They’re building purpose-built, governed data products that serve as the foundation for AI and analytics.
Security, governance, and accessibility
Security and governance are fundamental in the data product model. Starburst integrates access control at every level. It inherits privileges from source systems and layers in additional controls as needed.
This matters as AI applications become common across organizations. Data products ensure that AI systems operate within appropriate boundaries, accessing only approved, up-to-date, and trusted data. This blend of security and accessibility means both analytic teams and AI applications spend less time wrangling disparate data sources and more time extracting value.
The governance framework also provides the audit trails and compliance documentation needed for regulated industries deploying AI. Organizations can demonstrate that their AI applications are using data appropriately and that all access is properly authorized and logged.
Starburst and Trino: powering data products
Starburst is powered by Trino, an open-source distributed SQL engine. It allows fast querying across virtually any data source. Starburst Enterprise enhances Trino with enterprise-ready features.
These include advanced performance optimizations, a wide range of high-speed connectors for leading sources, and built-in security features. It also provides robust management tooling and the comprehensive metadata management needed for AI applications. (Learn more about Starburst Enterprise.)
These capabilities let organizations avoid the need to consolidate all data into a monolithic warehouse. By using data products, organizations provide their teams and AI applications with the trusted, timely, metadata-rich data needed for competitive advantage.
To learn more about creating and managing data products at scale, visit starburst.io.
Data product FAQs
What is a data product?
A data product is a curated, high-quality dataset that includes relevant metadata. It’s designed for easy discovery and consistent use. It is intentionally created and maintained by a domain team to support specific business purposes. Stakeholders like analysts, data scientists, business users, and AI applications consume it reliably.
What is an example of a data product?
An example is an “Invoice Data Product” in a financial services company. The accounts receivable team gathers and standardizes invoice data from multiple sources. They enrich this data with payment status and customer information. They provide detailed metadata and documentation. Finance analysts use this data product for forecasting and compliance. AI applications use it to answer questions and generate insights with full context and governance.
Why are data products important for AI?
Data products provide the rich metadata and context that AI applications need to work accurately and reliably. The comprehensive documentation, quality metrics, and business definitions embedded in data products help AI systems understand not just the data itself, but what it means and how it should be used. This reduces AI hallucinations, improves output quality, and allows organizations to use AI with proper governance.
What is the difference between a data product and a data catalog?
A data catalog is an inventory that helps users discover available datasets. A data product is an actively managed, curated data asset with embedded governance, quality controls, and rich metadata designed for consumption. While catalogs help find data, data products make data usable and trustworthy, especially for AI applications that require deep contextual understanding.
