Data As a Product vs Data As A Service: Frameworks For Data Leaders

The way organizations consume and deliver data has transformed dramatically over the past decade. In 2025, the rise of data products has become one of the defining trends for enterprises—especially in finance, retail, and healthcare—where trusted, high-quality data is a competitive advantage. At the same time, data as a service (DaaS) continues to play an essential role in making data broadly accessible through scalable, API-driven delivery.

These two approaches—data as a product (DaaP) and data as a service (DaaS)—are sometimes used interchangeably, but they are not the same. Each offers unique benefits and challenges. Understanding when to apply one or the other (or both in tandem) is critical for modern data leaders building governed, AI-ready data ecosystems.

Key takeaways

• Data as a service (DaaS) focuses on delivering raw or processed data on demand, usually via APIs or cloud platforms.

• Data as a product (DaaP) packages data into governed, discoverable, and reusable “products” with clear ownership and business value.

• DaaS works well for scalability and broad access; DaaP ensures trust, discoverability, and alignment with business outcomes.

• Many enterprises combine the two approaches: using DaaS to power distribution and DaaP to ensure quality, governance, and reuse.

• Platforms like Alation bridge these models, offering both a data marketplace for data products and the governance scaffolding to operationalize data services.

Data as a product vs data as a service: What’s the difference?

Before diving into benefits and challenges, it’s important to recognize that DaaP and DaaS are not competing frameworks but complementary ones. 

Both exist to streamline data management, improve decision-making, and ensure that data consumers—from analysts to machine learning models—can trust and act on information. The difference lies in how each approach addresses business needs, governance, and delivery.

Data as a product (DaaP)

Data as a product is a design and governance operating model. It treats data like a product—with clear ownership, documentation, metadata, and guarantees for quality and usability. It’s about packaging curated, governed datasets for reuse across the enterprise, complete with context, lineage, and business value.

Examples include:

• A customer 360 view packaged for analytics, marketing, and service teams.

• A churn prediction dataset curated and trusted for AI modeling.

• A revenue forecast data product that comes with lineage, definitions, and business owner accountability.

Unlike DaaS, which focuses on flow and delivery, DaaP emphasizes product development, discoverability, and alignment with organizational strategy.

Data as a service (DaaS)

Data as a service is a delivery model. It provides on-demand access to data—often via cloud-based APIs, subscriptions, or managed platforms. Think of it as data plumbing: the infrastructure and pipelines that let consumers tap into datasets without needing to manage them locally.

Examples of DaaS include:

• A financial analyst accessing stock market feeds through a cloud API.

• A marketing team subscribing to a third-party demographic dataset.

• A developer embedding weather data into an app via an external API.

In short, DaaS prioritizes accessibility and scale, offering rapid data access without requiring end users to worry about the underlying data architecture or data engineering.

Data as a product (DaaP): Benefits, challenges, and when to use

DaaP is rooted in the data mesh methodology introduced by Zhamak Dehghani, which emphasizes treating data as a product owned by domains. By making those closest to the data accountable for its usability, organizations move away from central bottlenecks and toward federated, domain-driven responsibility for data assets. This approach ensures that data is not just delivered but designed, governed, and maintained with the same rigor as any business product.

The difference between data as a product and data products

It’s important to clarify: data as a product (DaaP) is the overarching approach or philosophy. A data product is the actual asset that results—a reusable, governed, and discoverable dataset. For more detail, see this explainer on the distinction.

Common challenges of DaaP

Adopting DaaP is transformative, but not without its hurdles. Organizations often encounter these roadblocks:

• Cultural change: Requires shifting from project-driven to product-driven thinking.

• Ownership gaps: Without clear accountability, governance and quality may suffer. To address this, many organizations appoint data product managers who oversee data product usability, value, and compliance.

• Upfront investment: Establishing product frameworks, marketplaces, and contracts takes time, new processes, and buy-in across leadership and data engineering teams.

A KPMG study found that only 35% of respondents have achieved extensive value from their data product initiatives, underscoring the importance of time and people investment in the program’s success.

Benefits of DaaP

When executed well, DaaP delivers powerful benefits that extend far beyond traditional project-based data management:

• Trust and reusability: Products are published with metadata, lineage, and governance, ensuring end users and data consumers can rely on consistent outputs.

• Business alignment: Each product maps to a clear outcome—such as improving customer experience or reducing risk—making data directly relevant to strategy.

• Scalable innovation: Products are modular and reusable across domains, enabling machine learning models, analytics, and dashboards to be powered by the same trusted data.

Recent research confirms this momentum: adoption of internal data marketplaces grew 71% year over year, with 95% of organizations either already operating or planning a self-service marketplace. Even more compelling, 78% report significant or game-changing benefits from this shift.

Together, these benefits transform DaaP into more than a governance framework; it becomes a methodology for building trusted enterprise AI.

Common DaaP use cases

Organizations turn to DaaP when they need trusted, governed data at scale:

• Powering AI and machine learning models with rich metadata and domain context.

• Standardizing critical data elements (CDEs) for compliance and regulatory reporting.

• Replacing ad hoc pipelines with reusable, well-documented data assets that scale across data platforms.

• Delivering consistent, trusted inputs to business dashboards and analytics applications.

Decision criteria: Is DaaP right for your use case?

To determine whether DaaP is the right fit, leaders should ask:

• What is the long-term value? Will packaging this dataset as a product accelerate reuse and reduce duplication?

• Who owns the product? Is there a domain team or data product manager accountable for quality and usability?

• Will AI and analytics depend on this dataset? If so, does it need the governance and context of a product?

• Does the product need to serve multiple audiences? Cross-domain usage signals that a productized approach will deliver greater ROI.

• How critical is trust? If the dataset informs compliance, risk, or executive-level decision-making, DaaP should be the default.

Adopting DaaP is not just about creating new assets. It is about embedding trust, clarity, and scalability into the data architecture itself. Enterprises that commit to this approach establish a roadmap that aligns data development with business outcomes—building a foundation for enterprise AI, advanced analytics, and confident decision-making.

Data as a service (DaaS): benefits, challenges, and when to use

DaaS emerged with the broader cloud shift, as enterprises moved from on-premises data warehouses to elastic, subscription-based models. Its value lies in scalability, interoperability across diverse data sources, and ease of integration into existing data pipelines and dashboards. 

But DaaS also has inherent challenges: it often functions like a DIY model where raw data arrives quickly, yet data engineers and scientists must still cleanse and enrich it to make it accurate, trustworthy, and usable for end users. Product managers and BI teams may get rapid data access, but without consistent metadata and definitions, dashboards and models can diverge—slowing decision-making and creating rework across the data platform.

Common challenges of DaaS

While DaaS accelerates access, it also introduces friction for the teams that must clean and contextualize the data. Common challenges include:

• Quality blind spots: APIs and feeds don’t always include sufficient metadata, making it hard to validate accuracy or fitness for specific needs.

• Limited context: Consumers may get data rapidly but lack lineage, business definitions, or data models—leading to inconsistent analytics and duplication.

• Vendor dependency: Reliance on external providers can limit flexibility, introduce rate limits/SLO risks, and increase costs through opaque pricing tiers.

• DIY burden on teams: Data engineering and analytics teams must still perform transformation, deduplication, schema alignment, and data integration—adding time and operational risk.

• Schema drift and breaking changes: API updates or new data fields can silently break downstream data flow, automation, and machine learning models.

• Compliance and residency questions: Without clear policy mapping, external feeds can complicate controls across regulated domains and geographies.

Benefits of DaaS

Despite these hurdles, DaaS offers clear advantages that make it attractive to many enterprises:

• Scalability: Cloud-native services can elastically meet demand.

• Accessibility: Simple API and connector patterns speed data access into applications and workflows.

• Speed to value: Reduces the need to operate ingestion infrastructure, accelerating prototypes and proofs of concept.

• Breadth of data sources: Access to rich third-party data assets (market, location, risk, behavioral) that would be costly to collect organically.

Common DaaS use cases

Enterprises rely on DaaS for a wide range of scenarios, particularly when they need rapid access to external data at scale, broad coverage across diverse data sources, and the flexibility to plug that data directly into applications and workflows:

• Powering operational dashboards and logistics with telematics, weather, or traffic feeds.

• Risk and fraud scoring using consortium or bureau data with near–real-time updates.

• Real-time personalization and targeting via event streams and behavioral signals.

• Market and competitive intelligence for FP&A and category management.

• Rapid feature exploration for demand forecasting or pricing optimization.

Decision criteria: Is DaaS right for your use case?

To decide whether DaaS is a good fit, data leaders should ask questions such as:

• Is the primary data external (or faster/cheaper to obtain externally) versus harvested internally?

• What freshness is required? Do API SLOs, latency, and rate limits meet your needs?

• How much transformation is needed to make the data usable in your data architecture (e.g., harmonizing keys, aligning data models, resolving identifiers)? 

• Do governance and compliance requirements (PII, residency, industry regs) allow use of the provider and region?

• Can the data be joined cleanly to your internal data warehouse/data platform with stable identifiers to remain interoperable?

• What is the total cost of ownership? Consider subscription pricing, egress, storage, and the engineering effort to productionize pipelines.

• Will the feed create or reduce silos? Can you publish a governed, reusable internal product from the service to avoid one-off integrations?

In practice, DaaS works best when paired with strong governance. Without standards for metadata, ownership, and lineage, teams may subscribe to different feeds, build parallel pipelines, and encode conflicting rules—quickly recreating silos. 

Our recommendation? By cataloging service-derived datasets, attaching data contracts, and documenting lineage, organizations turn raw feeds into governed, reusable products. This ensures consistency for data consumers across domains. Platforms like Alation make this possible by operationalizing DaaS within a data mesh–aligned approach, linking services to discoverable products that teams can trust and reuse.

Choosing the right approach: DaaP, DaaS, or both?

Choosing between DaaP and DaaS doesn’t have to be binary. The right approach depends on your current data maturity, business needs, and technology stack. Enterprises often start with DaaS for accessibility, then evolve toward DaaP for governance and reuse. A balanced roadmap often leverages both: DaaS to deliver data flow at scale, and DaaP to ensure the data products themselves are trustworthy, valuable, and aligned to strategy.

By now, you should have a clear sense of which framework best fits your use case. Use the decision criteria from the earlier sections as a guide: evaluate ownership, governance requirements, business alignment, data freshness, and integration complexity. From there, apply the following tips to make adoption a success.

Product-thinking framework (if adopting DaaP):

• Define roles with accountability. Appoint data product owners and stewards who are responsible for ensuring product quality, usability, and compliance.

• Establish a lifecycle framework. Manage each data product from creation, publication, and monitoring through to retirement, ensuring sustainability and version control.

• Set measurable success criteria. Track usage rates, monitor quality indicators, and evaluate whether products deliver the intended business outcomes.

Service-oriented design (if adopting DaaS):

• Build on an API-first architecture. Design modular, scalable services that allow data to flow seamlessly into applications, analytics platforms, and AI models.

• Enable discovery through self-service portals. Provide business and technical teams with easy access to subscribe to services without creating IT bottlenecks.

• Monitor performance and usage. Establish observability across APIs and services, measuring availability, latency, and consumption patterns to maintain reliability.

Data products as enablers of enterprise AI

Data products are no longer just a governance mechanism—they are foundational to enterprise AI. By embedding metadata, lineage, and quality guarantees into reusable products, organizations create trusted datasets that fuel analytics and machine learning. This structured approach ensures that AI models are trained on consistent, high-quality inputs, reducing bias and accelerating adoption.

At the same time, AI can enhance governance itself. Intelligent agents can monitor pipelines for anomalies, enrich incoming data with metadata, and automatically map lineage across systems. This symbiotic relationship—data products powering AI, and AI reinforcing governance—creates a feedback loop that drives both innovation and trust.

Learn how to make data products a success by learning about the common mistakes to avoid.

DaaP case study: Discover Financial Services

Before adopting DaaP, Discover Financial Services faced a common enterprise challenge: analysts and business users had to hunt across silos of raw data, spending significant time cleaning and reconciling it before analysis. This slowed decision-making, created duplicate reports, and made it harder to trust results.

By implementing Alation’s data catalog and marketplace, Discover shifted to a DaaP approach. Governed data products replaced scattered datasets, giving analysts a centralized, discoverable hub for trusted assets. Metadata, lineage, and business context accompany each product, streamlining compliance and improving confidence.

The results were significant:

• Reduced duplication: Fewer redundant datasets and reports.

• Faster analysis: Weeks of manual data engineering were replaced by automated, reusable data products.

• Improved accuracy: Business units could rely on the same governed definitions, boosting the reliability of dashboards and machine learning models.

• Direct business impact: Customer experience, risk management, and compliance all improved because teams could trust and act on data more quickly.

Discover’s experience illustrates how DaaP creates not only technical efficiencies but also measurable business value. Read the full Discover case study to learn more.

Drive long-term business value with a DaaP approach

While DaaS ensures agility and scalability, DaaP drives trust, alignment, and reusability—all critical for enterprise AI and long-term data strategy. In practice, the two approaches often complement each other: DaaS handles delivery, while DaaP delivers trusted, governed datathat leaders can use to drive business value.

Alation bridges these models through the Alation Data Products Marketplace, which enables organizations to:

• Package high-quality, governed datasets into reusable products.

• Deliver trusted data through APIs and self-service portals.

• Apply metadata, lineage, and contracts to both products and services.

By embedding DaaP principles into your architecture, you establish a foundation for scalable data flows, governed interoperability, and automated governance. This not only simplifies compliance but also accelerates AI model development, speeds analytics, and empowers end users with accessible, trustworthy data assets.

Organizations that embrace this methodology gain a competitive edge: they reduce silos, align data pipelines with business strategy, and create a living roadmap for continuous innovation. In a world where Amazon-like data marketplaces are becoming the norm, DaaP ensures your enterprise can meet specific needs today while adapting to tomorrow’s challenges.

Now is the time to move beyond fragmented delivery and transform your data into a trusted, reusable, and valuable enterprise product that fuels AI, analytics, and decision-making at scale. Book a demo with us to get started.