The Essential Guide to Building Data Products You Can Trust

Modern organizations are drowning in data but starving for insights. Data teams have invested heavily in data warehouses, pipelines, and modern data architecture, but turning raw data into business value remains a challenge. Many have turned to data products as a solution.

A data product is a curated, governed, and reusable data asset designed to generate business value and be easily consumed by humans, applications, and AI/ML models.

Unlike raw tables sitting in a warehouse, data products are packaged for usability, with helpful documentation and compliance details. They bridge the gap between data availability and outcomes, ensuring end users—from analysts writing SQL to executives relying on dashboards—can trust and apply data confidently. By treating data as a product, organizations build an ecosystem that enables discoverability, interoperability, and end-to-end insight delivery.

Why data products matter

Adopting a product mindset for data leads to measurable results: faster time-to-insight, stronger governance, and better alignment between data teams and the business. According to research from McKinsey, companies using data products see speed and efficiency gains, with use cases delivered as much as 90 percent faster, and a 30 percent drop in ownership costs.

This shift requires more than just better tools. It demands a new way of thinking about ingestion, curation, governance, and delivery. In the following sections, we’ll explore the five pillars of effective data products and what it takes to make them successful.

The 5 key pillars of building effective data products

1. Discovery

Discovery ensures valuable data products are visible and usable. Without it, even the best-designed assets sit idle. Effective discovery combines catalog search, semantic layers, business glossaries, and relationship mapping. End users should be able to locate data products by searching business concepts—not just technical names.

Lineage and impact analysis are equally important. Understanding the end-to-end journey of a dataset—how it was ingested, modeled, and transformed—gives both analysts and decision-makers confidence.

While a data catalog serves as an inventory of assets—cataloging raw, unprocessed data like ad-hoc customer lists, one-off Tableau reports, database views, and manual spreadsheets—a data products marketplace takes a fundamentally different approach. 

The marketplace facilitates both search (exploration of available options) and discovery (identifying the right data product for a specific use case) by focusing on packaged, ready-to-use data products like Customer 360° datasets, HR attrition risk models, and sales forecasting API outputs.

The key distinction lies in purpose and preparation. A catalog answers "What data exists?" while a marketplace answers "What business problems can I solve?" The marketplace transforms the discovery process from a technical search through raw assets into a business-oriented exploration of solutions. Users can filter by business domain, browse recommended products for their role, and access contextual information that helps them understand not just what data is available, but how it can create value for their specific needs.

Discovery is the foundation of usability. It creates an environment where data teams can focus on solving problems, not searching for datasets, and where data products become discoverable building blocks across the business ecosystem.

Actionable tip: Implement semantic search capabilities that allow users to find data products using business terminology. Invest in metadata enrichment and business glossaries to bridge the gap between technical assets and business value, making discovery intuitive for non-technical users.

2. Curation

Curation transforms raw data into polished, business-ready assets. This involves technical curation (quality checks, schema validation, aggregates), business curation (glossary terms, usage rules), and governance curation (privacy and retention policies). These contextual details (or metadata) make the data product more usable.

Curated metadata ensures discoverability, standardizes data modeling, and creates interoperability across the ecosystem. AI-assisted tools can help iterate faster by suggesting glossary terms or related datasets, reducing manual effort for data engineering teams.

Curation turns ingestion into insight. By enriching raw data and adding business context, organizations ensure that their data products are not just technically sound but meaningful and actionable.

Actionable tip: Establish automated curation workflows that validate data quality, enrich metadata, and apply business context at the point of ingestion. This reduces manual effort while ensuring consistency and accelerating time-to-market for new data products.

3. Govern

Governance ensures data products remain trusted and compliant throughout their lifecycle. This step includes access management, quality monitoring, lifecycle ownership, and federated governance models that balance central oversight with domain autonomy.

Governance doesn’t have to be restrictive. Done well, it accelerates adoption by automating the right behaviors and giving people the signals they need to use data compliantly in workflows. When embedded into data pipelines and workflows, governance creates products that are safe to use and easy to maintain.

Actionable tip: Implement governance as code, embedding policies directly into data pipelines and product creation workflows. This approach makes compliance automatic rather than manual, reducing friction while maintaining trust and regulatory alignment.

4. Delivery

Delivery makes curated, governed products available in ways that meet diverse needs. Some users want self-service APIs; others prefer dashboards, SQL queries, or visualizations. Delivery must be flexible, scalable, and tailored to consumption patterns.

The NBA's approach exemplifies this tailored delivery model. As Jeff Cruz, Technical Data Product Manager at the NBA, explains: "The goal of the platform is to make it easier for users to find what they need — and to prevent redundant work. We want users checking out our products, using our products, giving us feedback on what should be displayed and how." By providing a central location for discovery through their internal data product marketplace, the NBA's portal helps break down silos between teams, fostering collaboration instead of duplication. "It's been a net positive having everything in one place," Cruz underscores.

This approach demonstrates how tailoring data products to unique audience needs—from marketing teams seeking customer insights to finance teams requiring revenue analytics—creates measurable value. By serving diverse consumption patterns through a unified platform, organizations can eliminate redundant development while ensuring each team gets data in their preferred format and workflow.

Ongoing performance optimization—such as caching, query tuning, and elastic infrastructure—ensures usability at scale. Documentation and training further improve adoption by guiding end users to the right assets.

Delivery is where value meets usability. Without effective delivery, even the best-designed data products fail to reach the right audience and drive business outcomes.

Actionable tip: Create multiple consumption interfaces for the same data product—APIs for developers, dashboards for executives, and SQL access for analysts. Monitor usage patterns to optimize delivery methods and identify opportunities for new product formats.

5. Improvement

Improvement is about iteration. Data products should evolve based on usage analytics, feedback, and changing business needs. Monitoring for anomalies, drift, and adoption trends enables proactive updates.

For the NBA, stakeholder involvement drives data product success. Acting as a bridge between business and technical teams, Cruz emphasizes asking the right questions upfront: What problem are you solving? Who is it for? Why does it matter? This discipline ensures each product starts with a clear purpose, audience, and metrics for success.

From there, products move through the full lifecycle — planning, design, development, testing, deployment, and maintenance. Crucially, they are never “finished.” Instead, the team applies agile principles, working in sprints and releasing new products every three weeks. Every six weeks, they host a showcase to share updates across the organization, preventing redundant development and aligning teams.

This approach shifts data delivery from static reporting to product management. Like software products, data products need roadmaps, enhancements, and ongoing investment. Improvement ensures data products remain relevant. By creating a feedback loop, organizations build an ecosystem that adapts continuously, improving both trust and impact.

Actionable tip: Establish product review cycles with clear success metrics, user feedback, and roadmap planning. Treat data products like software — with versioning, release notes, and continuous improvement.

Do’s and don’ts of scaling data products in an enterprise

Scaling data products is not just about technology; it’s about aligning people, processes, and strategy. Below are key practices to follow—and pitfalls to avoid—when building a sustainable, enterprise-wide data product ecosystem.

Do: Start with high-impact business problems

The most successful initiatives begin with pressing business challenges. Rather than asking, “What can we do with the data we have?”, organizations should ask, “What problems matter most to solve?”

For example, Kenvue began by focusing on their “big rocks”—four priority use cases that tackled critical business needs. This focused approach not only delivered measurable outcomes quickly but also built organic demand for additional data products.

Key steps:

• Identify pain points where manual processes slow the business.

• Target areas where teams repeatedly recreate similar analyses.

• Choose use cases that are both feasible and high-impact.

Starting with business value creates early wins, proving the concept and building momentum across the organization.

Do: Bridge technical and business languages

Data products succeed when they’re understood by both technical and business audiences. Too often, documentation is written in “tech speak” (schemas, field names, ingestion details) that alienates business users.

This challenge is exemplified by Swire Coca-Cola's experience with inconsistent metrics. As data leader Bharathi Rajan explains: "When I started, there were different versions of OTIF [On-Time In-Full]… there was no consistent way of calculating it." This inconsistency led to confusion and undermined confidence in reporting across the organization.

Swire Coca-Cola's solution demonstrates the power of bridging technical and business languages.. Instead of allowing multiple versions of the calculation to exist, they brought stakeholders together to agree on a single, standardized definition.

"We said, OK, what's the right calculation? What's the standard that needs to be across all functions? And then we took that metric and put that into Alation. And now there's one standard calculation," Rajan explains.

Best practices:

• Pair technical owners with business stakeholders to co-create product documentation.

• Include glossaries, business rules, and real-world usage examples alongside SQL schemas or data modeling details.

• Highlight usability by focusing on the “why” and “how” rather than just the “what.”

By bridging this language gap, data teams make products accessible to end users, improving adoption and trust.

Do: Establish clear ownership and accountability

Every data product needs defined owners who are accountable for its quality and usability. The NBA, for instance, uses a dual ownership model: business product owners capture requirements, and technical product owners manage implementation and delivery.

Key elements of ownership:

• Defined service level agreements (SLAs) for performance and availability.

• Documented escalation paths and support contacts.

• Continuous iteration based on feedback from end users.

Ownership ensures accountability, preventing products from deteriorating over time and reinforcing trust in the ecosystem.

Don’t: Underestimate change management

Transitioning to a data as a product mindset is as much cultural as it is technical. Many organizations assume adoption will follow naturally—but without clear communication, incentives, and training, usage stalls.

Pitfalls to avoid:

• Ignoring the need to retrain technical teams to think in terms of usability and end-to-end outcomes.

• Failing to demonstrate tangible wins to business users.

• Overlooking the leadership role in reinforcing adoption.

Change management is essential. Scaling data products requires both cultural readiness and leadership commitment.

Don’t: Retrofit legacy assets without strategic consideration

Not every existing dataset should become a product. Legacy assets often lack metadata, documentation, or governance, making them poor candidates for scaling. Simply “wrapping” them as products can introduce technical debt.

Better approach:

• Use legacy assets as input to design new, fit-for-purpose products.

• Apply modern data modeling and governance practices to ensure scalability.

• Iterate with feedback from end users before scaling widely.

Resist the temptation to retrofit everything. Focus instead on building truly productized assets designed for modern needs.

Don’t: Neglect the discovery layer

Even the most valuable products are useless if people can’t find them. Discovery is not just a cataloging exercise—it’s about helping users connect the dots between products and their business value.

Swire Coca-Cola's success with data products demonstrates this principle in action. Business users can now easily find and understand the data they need through Alation's intuitive interface. The platform provides not just access to data, but context about what the data means, how it's calculated, and how it should be used. This comprehensive approach to discovery transforms the user experience from hunting for data to confidently selecting the right solution for their business needs.

Best practices:

• Invest in semantic search that goes beyond table names.

• Provide context with lineage, glossary terms, and recommended visualizations.

• Track usage patterns to improve discoverability and relevance over time.

Discovery underpins adoption. Without it, teams duplicate work, governance breaks down, and the ecosystem becomes fragmented.

The do’s and don’ts of scaling highlight a simple truth: building trusted, enterprise-wide data products requires more than pipelines and governance. It takes deliberate prioritization, a balance of technical and business perspectives, clear ownership, and cultural readiness. By avoiding common pitfalls and focusing on usability and discoverability, organizations create products that end users actually want—and trust—to use.

Examples of data products by industry

Data products are not just curated datasets—they are AI-friendly, metadata-rich assets that fuel predictive analytics and machine learning while enforcing compliance and governance. They serve as reliable, governed inputs that end users, applications, and AI models can trust.

A strong illustration comes from the BBC, which built a Customer 360 data product to unify search behavior across mobile, web, and TV platforms. By standardizing definitions and applying quality checks, the BBC created a single, trustworthy product that gave analysts and product managers a consistent view of user behavior as it relates to search. This enabled better personalization, faster insights, and greater trust in business metrics.

Kenvue, a leading consumer health company, took a similar approach at scale by creating a data product marketplace. They designed intuitive “solution pages” that combine business context, technical documentation, and governance rules—all accessible within just a few clicks. By bridging the gap between technical detail and business usability, Kenvue empowered data teams and end users to collaborate effectively. This marketplace became a trusted ecosystem where governed data products could be reused across multiple domains, fueling efficiency and AI readiness.

The BBC and Kenvue examples highlight how data products unlock value across industries. From consumer media to healthcare and financial services, organizations are applying the same principles—governance, discoverability, interoperability, and usability—to create products that are AI-ready and compliant by design. 

Below, we explore how different industries are applying this approach to achieve measurable outcomes.

Financial services

Financial institutions rely on data products to manage risk, improve customer retention, and prevent fraud—all while maintaining compliance.

• Customer retention prediction score: A regional bank built a churn prediction product to identify at-risk customers based on account activity, demographics, and past interactions. By proactively engaging with these customers through personalized outreach, they reduced churn by 19% and preserved $2.1M in deposits.

• Fraudulent transaction alert: A credit union developed a real-time fraud alert API that flagged suspicious debit card purchases. In three months, it prevented $1.7M in fraudulent charges while cutting false positives by 28%.

• Loan default risk score: A regional bank used a default risk data product to assess small business credit applications. The result: a 19% reduction in non-performing loans and $8.2M reallocated to lower-risk lending opportunities.

In financial services, data products combine predictive analytics with compliance safeguards. They enable AI models to assess churn, fraud, and risk in real time, while strict governance ensures fairness, accuracy, and regulatory alignment.

Healthcare

In healthcare, data products drive better patient outcomes while protecting sensitive information under regulations like HIPAA.

• Patient readmission risk score: A regional health system deployed a readmission prediction product for cardiac patients. Targeted interventions reduced readmissions by 19% and saved $2.8M in avoidable hospital costs.

• EHR interoperability API: A multi-hospital network used a data product to unify patient records across facilities. Clinicians gained access to 27% more external histories during care transitions, cutting duplicated lab tests by 15% and improving safety.

• Claims data analytics dataset: A health insurer built a claims analytics product integrating medical and pharmacy claims. The insights identified gaps in chronic disease care, boosting compliance rates and avoiding projected costs of $2.1M.

Healthcare data products balance innovation with security. By feeding AI models with accurate, interoperable datasets, they improve care coordination, reduce readmissions, and enable population health analytics—without compromising patient privacy.

Technology

Technology companies use data products to retain customers, optimize products, and ensure service reliability in cloud and SaaS environments.

• Customer churn prediction model: A SaaS provider implemented a churn prediction data product using feature adoption and support interactions. The model helped cut churn by 14%, preserving $2.6M in annual recurring revenue.

• Feature adoption metrics dataset: A SaaS analytics company built a product to track usage across advanced features. By targeting underutilized features with in-app training, adoption grew by 35% and premium upgrades rose 12%.

• SLA compliance monitoring feed: A cloud provider developed an SLA monitoring product that surfaced latency issues before they impacted customers. The system preserved 99.99% uptime while avoiding costly SLA penalties.

In technology, data products enable proactive, AI-ready insights that directly support customer retention, product innovation, and reliable service delivery. With metadata-driven explainability and compliance controls, they ensure trust at scale.

Summary table: Industry use cases for data products

These use cases demonstrate that data products are more than polished datasets. They are AI-friendly, metadata-rich assets that power predictive analytics, inform machine learning, and ensure compliant usage across sectors. By packaging data with lineage, governance, and explainability, organizations can trust data products as reliable, secure inputs for both human and machine decision-making.

The Data Products Builder Agent from Alation

Modern organizations face a fundamental challenge: fragmented, undocumented data overwhelms teams while manual workflows slow critical decisions. Business users struggle to find trusted data—often resorting to unapproved sources that risk bad decisions. Without clear, tailored data products, silos persist, and data engineers drown in repetitive requests, ultimately stalling AI initiatives.

Alation's Data Products Builder Agent addresses these challenges by helping organizations package, document, and prepare data products faster. The AI-powered agent recommends relevant assets, generates comprehensive documentation, and surfaces crucial trust signals like certifications and policies—enabling teams to review, refine, and approve before publishing to a governed marketplace.

What sets Alation's approach apart is its foundation on open standards. The platform delivers trusted data products with built-in governance, versioning, and certification based on the Open Data Products Specification (ODPS). This ensures that data products are not only trusted and machine-readable but also interoperable across systems, powered by extensible metadata designed for AI-ready delivery.

The platform supports smarter AI through its integrated "Chat with Your Data" feature, which builds on a knowledge layer of trusted data products in your data marketplace. Rather than relying on generic AI tools that lack metadata context, this feature provides accuracy improvements of up to 60% by operating on your specific metadata, business definitions, and governance frameworks. Every response includes transparent explanations showing SQL queries, data sources, and business definitions, ensuring explainable AI rather than black-box decision making.

This comprehensive approach enables organizations to boost adoption and value by continuously improving data products through real-world feedback and usage insights. Teams can monitor product performance through dashboards, prioritize enhancements, and refine offerings within the governed Data Products Marketplace—creating a sustainable ecosystem that adapts to changing business needs.

Conclusion: Build trusted data products with Alation

The shift to treating data as a product is about more than technology—it’s about creating AI-friendly, metadata-rich assets that are discoverable, governed, and easy to use. By investing in discovery, curation, governance, delivery, and continuous improvement, organizations can build data products that fuel both human decision-making and machine learning models.

For organizations ready to make this shift, the rewards are tangible—faster time-to-insight, lower costs, and the ability to innovate with confidence. Data products are not just datasets; they are the foundation for an intelligent, secure, and future-ready enterprise.

Learn more about how to publish, find, and use high-quality, reusable data products via Alation's Data Products Marketplace.

FAQs: Data Products Explained

1. What is a data product?

A data product is a curated, governed, and reusable data asset that provides business and technical value.

Unlike raw datasets, data products are enriched with metadata and designed for end-to-end usability. They include clear documentation, interfaces for SQL or APIs, and built-in governance. This makes them discoverable, interoperable, and fit for modern data architectures like data mesh.

2. Why are data products important?

Data products turn raw data into trusted, usable assets for analytics, machine learning, and decision-making.

They bridge the gap between technical infrastructure and business insights. By embedding context, quality checks, and usability, they empower data engineering teams and data teams to deliver meaningful value faster—and scale their work across the ecosystem.

3. How do data products differ from raw data?

Raw data is unprocessed and often siloed; data products are refined, documented, and ready to use.

Raw tables in a data warehouse might require manual queries and modeling. Data products, by contrast, come with schemas, usage examples, and visualizations—making them accessible to end users, analysts, and ML models alike.

4. What role does data engineering play in data products?

Data engineering builds the pipelines and models that underpin reliable data products.

Data engineers handle ingestion from sources, apply transformation logic and aggregates, enforce quality checks, and feed data warehouses or feature stores. These efforts ensure that products meet governance, usability, and performance standards.

5. How do data products support machine learning?

Data products provide structured, stable, and quality-controlled data for ML workflows.

Machine learning requires clean, consistent data—often via feature stores or engineered datasets. Data products enable ML teams to consume trusted inputs and feed back performance metrics into continuous improvement loops.

6. How does a data mesh relate to data products?

In a data mesh, each domain builds, owns, and manages its own data products.

Data mesh promotes decentralized, domain ownership and federated governance. Each domain team treats data as a product, improving alignment between data architecture and business needs while boosting discoverability and interoperability.

7. What are the essential attributes of a data product?

Core attributes include being discoverable, reliable, interoperable, self-describing, and governed.

These traits—often summarized by the FAIR principles (Findable, Accessible, Interoperable, Reusable)—ensure that data products are easy to find, trustworthy, and scalable across diverse teams and use cases.

8. How do data products improve discoverability?

They become easy to find via catalogs with metadata, semantic search, and documentation.

Without good discoverability, end users waste hours hunting for the right data. By surfacing lineage, business terms, and usage patterns, data products become more accessible and adoption rates improve.

9. Can legacy datasets become data products?

Sometimes—but modernizing legacy systems into fit-for-purpose products is usually better.

Legacy assets often lack metadata, documentation, and governance. Rather than retrofitting, it’s more effective to use them as inputs for new data products crafted to meet current standards, usability, and ownership models.

10. What’s the best way to scale data products across an organization?

Scale through clear ownership, federated governance, usability design, and continuous iteration.

Successful scaling involves domain-aligned ownership, product-management thinking, streamlined data pipelines, ML-readiness, and tools for discoverability. Ongoing monitoring and feedback loops keep products relevant and valuable.

11. What are some real-world examples of data products?

Common examples include Customer 360 products, operational dashboards, machine learning feature stores, and regulatory reporting datasets.

For instance, the BBC created a Search Metrics data product to unify user behavior across mobile, web, and TV platforms, while commercial real estate firms have built lease renewal propensity products to automate and accelerate tenant decision-making. These examples show how data products serve end users in both customer-facing and operational contexts.

12. How do different industries use data products?

Industries apply data products in unique ways based on their ecosystems and priorities.

• Retail: Customer 360 products for personalization and churn analysis.

• Finance: Regulatory compliance products with audit-ready lineage and aggregates.

• Healthcare: Clinical trial reporting products with strict privacy governance.

Technology: AI-ready feature stores that power real-time machine learning.