Universal, fast, and secure data access is the non-negotiable starting point for any AI data product. AI cannot act on what it cannot discover. Historically, data was trapped within organizational data silos. Frequently, each team had its own systems, formats, and access requirements. This fragmentation not only delays analytics but also limits the contextual breadth necessary for AI models to perform optimally.
Principle: Design your data products to work across boundaries, connecting data wherever it lives, be it in the cloud, on-premises, or using a hybrid environment.
Example: A global retailer wants to launch an AI-powered demand prediction tool. However, their historical sales data is stored on-premises for compliance reasons, while real-time inventory updates reside in a cloud data lakehouse, and marketing campaign data is housed in a SaaS CRM. Rather than moving all this data to a central warehouse—a costly endeavor—an open lakehouse architecture enables federated access, allowing the retailer to build AI data products that draw in and harmonize these disparate sources in real time for more accurate predictions.
What makes a data product?
A data product is a packaged, reusable data asset with comprehensive metadata, clear lineage, and domain context. Examples include:
- A retailer’s historical sales data is packaged with documentation and access controls.
- A financial firm’s credit data with lineage information and business owner details.
- A hospital’s patient records are curated with governance policies and de-identification protocols.
- Manufacturing sensor data organized with quality metrics and descriptive metadata.
1. Data collaboration: Enabling transparency and shared context
AI data products thrive in environments with high collaboration and shared understanding. Siloed work often leads to duplicated effort, incomplete data context, and ultimately, lower-quality outputs. Effective AI requires more than just accessible data—it requires data that is well-documented, trustworthy, and reusable by various teams or agents.
Principle: Build data products to function as discoverable, shareable assets with comprehensive metadata, clear lineage, and domain context.
Example: Consider a financial services firm developing AI-driven risk models. Credit data, market feeds, transaction histories, and regulatory reports originate from different business units. By packaging each of these as a data product with documentation, data lineage, and clearly associated business owners, teams can more easily reuse and assemble these assets for new AI models or regulatory reporting. This approach enables different teams to securely publish, locate, and integrate datasets, thereby accelerating AI experimentation and time to business value.
2. Robust data governance: Security, compliance, and trust
AI amplifies both the rewards and the risks of poor data governance. As AI agents and models process more sensitive data, robust governance becomes increasingly critical. Data products underpin this by embedding access controls, auditability, and regulatory compliance mechanisms by default, enabling organizations to enforce data quality, privacy, and usage policies at scale.
Principle: Make governance intrinsic to your data products. Implement role-based and attribute-based access controls, include audit trails, and support regulations such as GDPR or HIPAA.
Example: A hospital system seeks to create an AI assistant for clinical decision support. Patient data is highly sensitive and subject to laws governing health information privacy. Using a modern data lakehouse platform, patient records are curated as a governed data product with fine-grained access policies. Only authorized medical researchers can access anonymized data; complete lineage tracks what data is entered into any AI training process. Using this approach, and potentially even running models fully on-premises, ensures trust and auditability, making regulatory compliance manageable even for AI initiatives.
3. Open architectures: Avoiding vendor lock-in and preserving optionality
AI and analytics needs evolve rapidly; data products must let organizations adapt without being held hostage by a single technology provider. Leveraging open table formats and interoperable components ensures that data remains portable and that compute engines can be swapped or scaled as necessary.
Principle: Build data products on open standards—such as Apache Iceberg for table formats, and Trino for SQL querying—so your organization can pivot, extend, or optimize as the technology and vendor landscape changes.
Example: A media company wants to incorporate generative AI into its recommendation algorithms. With their data already stored in an open Iceberg table format, they can seamlessly trial various compute engines—whether in the cloud or on-premises—without costly migrations or rewriting ETL jobs. If a new AI framework offers superior cost or features, the data products remain compatible and accessible.
4. Iterative centralization: Federation first, centralize where it counts
Not all data needs to be moved; centralization is no longer a universal goal. AI data products are best created by federating data access first and only centralizing specific critical workloads or high-value datasets for performance or governance reasons.
Principle: Use federated approaches by default and augment with managed centralization (such as materialized views or optimized Iceberg tables) only where it offers clear benefits.
Example: A manufacturing group wants to implement predictive maintenance AI using sensor data from dozens of geographically dispersed factories. Initially, they federate data—accessing existing streams for prototyping. As AI models mature, they identify a subset of high-value signals and centralize these into an optimized, governed Iceberg table for faster model retraining and compliance reporting. Often this occurs without disrupting other ongoing analyses that rely on more distributed data.
5. Data productization: Packaging for AI consumption and reuse
AI workflows accelerate when teams can assemble, compose, and trust data products as building blocks. This is especially important for retrieval-augmented generation (RAG) or agentic AI, which require high-quality, well-documented context, not just raw data tables.
Principle: Treat datasets not as one-off assets, but as reusable products. Include business context, descriptive metadata, quality metrics, and predictable interfaces for access.
Example: An insurer wants to use AI to analyze claim histories, policy details, and customer interactions to spot fraudulent activity. By curating each data source as a product with domain-specific annotations and business rules, data scientists are spared the effort of transforming raw tables for each iteration. Instead, they create AI-ready data products. This includes usage guidelines and performance metrics, which can then be easily reused in new AI models or adapted to serve analytic dashboards as needed.
6. Contextualization and lineage: Explainability as a first-class requirement
AI models, especially those of the agentic and generative types, require understandable and explainable data inputs. Data products must carry not only the data itself, but clear context about its source, transformation history, and intended use. This supports AI explainability and trustworthiness, which is crucial for business adoption and regulatory scrutiny.
Principle: Build traceability and robust documentation into every data product. This approach enables the answer to the questions “where did this data come from, who owns it, and how has it been used?”
Example: A bank needs to explain how its AI-powered customer onboarding process arrives at risk scores. Every data product feeding the AI model, whether it includes checks or transaction histories, incorporates explicit lineage, metadata, and documentation. If a regulator inquires, the bank can trace every AI decision back to its constituent data sources, thereby supporting both compliance and customer trust.
7. Automation and assisted workflows: Accelerating data product lifecycle
With rising data volumes and intense business pressure for rapid AI innovation, the ability to automate or assist in the creation and maintenance of data products is a competitive advantage. AI agents and workflow tools can help with metadata enrichment, standardization, and ongoing data quality monitoring.
Principle: Leverage automation for cataloging, documentation, tagging (e.g., PII detection), and policy enforcement to enable data products to scale with minimal overhead.
Example: A large telecommunications company implements an AI-driven data catalog that auto-tags new datasets for potential sensitivity and suggests owners for undocumented assets. When a new dataset arrives, AI-assisted workflows prompt the data owner to enrich metadata and create a data product with recommended access policies. This ensures that as the number of data products grows, governance and context keep pace with the scale, supporting both rapid AI deployment and regulatory obligations.
How AI enhances data product creation
AI typically does not produce raw data from scratch. Rather, it transforms, enriches, and generates insights from existing data using the following approach:
- Data transformation: AI can clean, normalize, and structure raw data
- Pattern recognition: AI identifies trends and correlations in datasets
- Predictions and forecasting: AI generates future projections based on historical data
- Content generation: Generative AI creates new text, images, or other content
- Metadata enrichment: AI can automatically tag and categorize data
- Decision outputs: AI produces recommendations, risk scores, or classification results
- Synthetic data: AI can generate realistic but artificial datasets for testing or training
AI systems also generate metadata about their own operations, including confidence scores, model performance metrics, and audit trails of the decisions made.
8. Support for hybrid and multi-cloud data environments
Many enterprises operate hybrid or multi-cloud data estates, often due to regulatory requirements or historical business practices. Data products should be designed and managed in a way that is agnostic to the underlying physical or cloud infrastructure.
Principle: Data product infrastructure must connect, govern, and orchestrate usage across cloud, on-premises, and SaaS systems. Importantly, this must occur without compromising on performance or compliance.
Example: A government contractor needs to build AI agents that operate on sensitive data both in the cloud and in secure, air-gapped data centers. A data product platform that spans these environments, offering unified governance and policy enforcement, allows the organization to innovate with AI in the cloud while ensuring sensitive workloads remain on-premises for compliance.
9. Align data products with business objectives
The value of AI data products is realized when they directly support strategic business goals. Data teams should prioritize building data products not only for theoretical reusability, but also to answer real business questions, power concrete AI and analytics use cases, and maximize relevance to decision-makers.
Principle: Design data products with clear alignment to AI use cases that move the business forward, whether that’s customer retention, fraud reduction, or supply chain optimization.
Example: A transportation company aims to reduce fuel costs by utilizing predictive AI. Data engineers and business analysts jointly identify the key data, such as vehicle telematics, route histories, and driver behavior metrics, and curate these as data products with business-relevant KPIs and transformation logic built in. As the AI team iterates, they quickly assemble new models using these trusted sources, measure impact, and present explainable results to leadership, closing the loop between data, AI, and business value.
Common AI product implementations
AI products are solutions that leverage artificial intelligence to deliver specific capabilities. They’re often built on data products and include:
- AI-powered demand prediction tools (like the retailer’s example above)
- Clinical decision support systems (like the hospital’s AI assistant)
- Predictive maintenance solutions (like the manufacturing sensor analysis)
- Customer recommendation engines
- Fraud detection systems (like the insurer’s claims analysis)
- AI assistants for business workflows
- Risk assessment models (like the bank’s onboarding process)
- AI-driven data catalogs that auto-tag datasets (like the telecommunications example)
The rise of AI data products reflects a growing recognition that data access, collaboration, and governance must advance in tandem to drive AI-driven innovation. By focusing on open architectures, embedded governance, robust metadata, and an iterative, business-aligned approach, organizations can ensure that their AI strategies are not hindered by legacy bottlenecks or fragmented systems. Instead, they build foundations that unlock business value.
Organizations that invest in building high-quality, governed data products now position themselves to lead in the next era of enterprise AI, where success depends not only on powerful algorithms, but on the trustworthy, explainable, and business-aligned data that fuels them.
