Best Practices for Delivering AI-Ready Data Products Using Snowflake Internal Marketplace

The pressure to move from experimenting with AI to delivering measurable results has never been higher. Executives and boards everywhere are increasing their expectations, asking teams to show the ROI and prove that these AI investments can increase efficiency, help with smarter decision-making and drive innovative product development. And no AI strategy is complete without a foundation of reliable, accessible, contextualized data. Unfortunately, many organizations and business leaders struggle to make high-quality data easily accessible, meaning that even the most sophisticated AI strategies fall flat.

The challenge with AI readiness and why data products matter  

Success with AI relies on more than just ML model training, algorithms and compute power. It actually starts with data. Not just raw data, but highly curated, context-rich data products that can be built, shared and easily accessed by business teams and machines. Traditional approaches to sharing these data products present roadblocks such as:

• Siloed data trapped across departments
• Duplicative efforts and wasted time trying to locate reliable data sets
• Access bottlenecks from overburdened central data teams
• Uncertainty around data ownership, completeness and trustworthiness

Simply put, raw data isn’t enough. AI demands contextual, curated, trusted and high-quality data that teams can trust and use confidently.

That’s where data products come in. These aren’t just data sets; they are carefully curated collections enriched with metadata, semantic models and business-friendly definitions. With consistent, trusted sources of data, organizations can align initiatives, train effective models and make AI truly actionable across the business.

Building and delivering data products with Snowflake Internal Marketplace 

To address this challenge, Snowflake Internal Marketplace offers a centralized, secure hub where teams can discover, share and consume trusted data products. Unlike traditional, fragmented sharing approaches, Internal Marketplace can:

• Make data easily discoverable and reusable across teams

• Reduce duplicative efforts and accelerate AI readiness

• Shift organizations from relying on ad hoc raw data sharing to using a strategic approach centered around trusted, certified, usable data products

• Create a trusted internal catalog, not just a data dump

Critically, Internal Marketplace leverages native Snowflake capabilities such as data sharing and data product listings, empowering teams to build, share and consume AI-ready data across clouds and regions with confidence. It is also part of the broader Snowflake Horizon Catalog, which provides not only discovery and collaboration capabilities but also governance and security solutions. Horizon Catalog enables the support of trusted, curated data products that are available on Internal Marketplace and provides the features that allow for governed accessible data upon which AI use cases can be built. 

Best practices for leveraging Snowflake Internal Marketplace

As organizations increasingly embrace a data product-focused strategy and explore the power of an internal marketplace, it's crucial to understand that while best practices offer a strong foundation, sometimes you need tailored configurations. 

At Snowflake, we advocate for data domains to own their data products, fostering accountability and expertise. However, the specifics of your internal marketplace, from setting up listings and defining roles to leveraging data sets and driving consumer adoption, might require adjustments to align with your unique architectural landscape and organizational structure. 

With that in mind, we've collected nine best practices to guide you. These recommendations aim to provide a starting point and key considerations for maximizing the value of data products within Snowflake Internal Marketplace, particularly as you drive your AI initiatives.

1. Identify business outcomes and align them with your data product strategy 

Why it matters: To maximize the value of your data products, start by tying them directly to your most critical business priorities, such as revenue growth, customer acquisition, product adoption or operational efficiency. Without this alignment, teams risk building isolated data assets that don’t drive real outcomes. A business-aligned approach helps ensure your data product efforts are focused, measurable and scalable. And remember — it's okay to start small! Pick a specific use case, build a data product to support it, and grow from there. 

How to do it: Start by mapping three to five high-priority business initiatives (or start with just one). For each, identify the specific data sets or insights required to achieve success, then list the data products you’ll need to enable those outcomes. If you’re supporting multiple initiatives, look for overlaps across the use cases that require shared, foundational data products. That overlap becomes your priority list to begin building, so you can support high-impact, reusable use cases from day one.

2. Build curated data products using organizational listings in Internal Marketplace  

Why it matters: Raw, fragmented data sets create friction for data consumers and introduce risks to AI and analytics initiatives. Curated data products help reduce complexity and standardize definitions and enable you to deliver reliable, ready-to-use resources aligned to your business needs. 

How to do it: Use organizational listings to package and securely share trusted, AI-ready data products. Through the Snowsight interface, you can create a listing, define access privileges and set discovery parameters so teams can easily find and consume the data. Listings can support a wide range of use cases, including customer segmentation, model training and revenue forecasting, by providing governed, curated access to high-quality data sets built for immediate use.

3. Integrate semantic models for true AI readiness

Why it matters: Structure and context are essential for AI to reliably consume and generate insights from data. Semantic models provide that foundation — enriching data products with machine-readable business definitions, relationships and terminology. Without them, AI models may rely on inconsistent interpretations of raw data, undermining accuracy and scalability.

How to do it: Design your data products with robust semantic layers from the start. Snowflake semantic views capabilities help you extend semantic definitions across your organization. This helps ensure data products are discoverable, context rich and optimized for AI consumption, so you can reduce ambiguity, improve data quality and accelerate AI model development.

4. Establish trust with domain representation through organizational profiles in Internal Marketplace

Why it matters: In large enterprises, countless teams publish and consume data. Without clarity on ownership and data quality, trust erodes and adoption stalls. Data provider profiles — also called organizational profiles — bring structure and transparency, ensuring consumers know exactly where data comes from and who is accountable for its maintenance.

How to do it: Create organizational profiles within the Internal Marketplace that are tied to business units such as sales, marketing or product so users can identify and trust business domains. These profiles are a core part of Snowflake’s Uniform Listing Locator, making it easy to publish, discover and query organizational listings without mounting them. Profiles build trust, simplify discovery and promote accountability, making it easier for teams to find and use reliable data products with confidence.

5. Implement secure and governed access workflows

Why it matters: Not all data should be accessible to everyone. Balancing broad data discovery with strong governance is critical for both compliance and maintaining organizational trust. The right access workflows enable you to safely democratize data, preventing unauthorized use while still making it easy for approved users to find and request what they need.

How to do it: Control data access with precision by using capabilities such as "discoverable, not accessible" (DNA), allowing only certain users to discover data products without immediate access. This enables you to effectively showcase available data sets while maintaining security. For actual data consumption, Request for Access Workflow streamlines the entire process: it defines who can discover data, designates approvers for access requests and automates review and fulfillment, helping to protect sensitive data while giving authorized users frictionless yet secure access to valuable AI-ready data products.

6. Design data products for immediate consumption

Why it matters: Teams need structured, ready-to-use data products that can be integrated directly into models, pipelines and other programs. They don't want to extensively rework or manually prepare the data. Building self-service, AI-ready data products helps teams move fast, focus on value creation and reduce time to insight.

How to do it: Collaborate closely with data consumers (from marketing, sales, product and so on) and your analytics teams to understand their data requirements, including things such as tables, schema formats and enrichment needs. Package data products with those needs in mind, ensuring they’re properly formatted, semantically aligned and immediately consumable for model training, marketing programs, revenue modeling or real-time AI workflows. Leverage user-defined functions (UDFs) to apply consistent business logic or enrich raw data as part of your data product creation. With UDFs, you can embed reusable transformations within listings to reduce manual effort, improve standardization and ensure consistent access.  

7. Drive internal awareness and adoption of data products

Why it matters: Even the best data products in the world provide zero value if teams don’t know they exist or how to use them. Internal adoption is critical to maximizing ROI on your data product efforts. Increased awareness drives reuse, reduces duplication and accelerates AI project timelines.

How to do it: Launch an internal enablement strategy to promote data products available on Internal Marketplace:

• Announce new data products through Slack, email newsletters or intranet sites

• Host interactive workshops, demos or "marketplace roadshows" to showcase available assets

• Publish success stories highlighting how teams are using data products for AI model training, customer 360 initiatives, revenue forecasting and more

The more teams engage, the more data product evangelists you will create and the faster your organization will scale trusted data and AI-driven outcomes.

8. Establish data maintenance strategy

Why it matters: Stale, outdated or low-quality data products erode trust and diminish AI effectiveness. Ongoing maintenance ensures your data products stay fresh, relevant and aligned to evolving business and AI needs.

How to do it: Operationalize ownership and upkeep with clear processes:

• Assign data product owners and data stewards to manage each product

• Define refresh schedules, quality monitoring and version control policies

• Establish deprecation processes to safely retire obsolete or unused products

A formal maintenance strategy keeps your data products reliable, reducing risk and enabling teams to consistently access high-quality, AI-ready data.

9. Automate data product creation for scale

Why it matters: As your organization matures, manually creating and updating listings slows you down. Automation accelerates scale, consistency and responsiveness to evolving data needs.

How to do it: Use Snowflake Programmatic Listing APIs to create, update and manage organizational listings via SQL and YAML manifests. This enables rapid rollout of new data products across teams and regions, while keeping listings consistent, governed and AI ready. Coupled with listing auto-fulfillment, your teams can maintain a global, scalable data product ecosystem that supports both operational efficiency and AI initiatives.

Trusted, discoverable data products to power AI initiatives 

AI is transforming how businesses operate. And reliable, curated, easily accessible data is at the heart of any successful AI initiative.

Building data products on Snowflake Internal Marketplace helps you get ready for the next phase of your AI strategy. It provides the foundation for AI success, empowering teams to discover, share and consume AI-ready data products with confidence. By following the nine best practices outlined in this blog post, your organization can break down data silos, reduce duplicative efforts and fuel AI projects with the high-quality, trusted data it demands.