Many organizations continue to manage data as a project output rather than a reusable enterprise asset. As AI adoption accelerates, that mindset is becoming increasingly difficult to sustain. Data as a Product (DaaP) offers a more scalable operating model by turning data into a trusted, reusable business capability that can drive value across the enterprise.

Instead of treating data as a byproduct of systems, DaaP applies product thinking to enterprise data, making it more Findable, Accessible, Interoperable, and Reusable (FAIR). This model improves data quality and trust while accelerating the adoption of AI, analytics, and automation.

DaaP combines data, metadata, code, and supporting infrastructure into consumption-ready data products designed for stakeholders across and even beyond the organization.

For example, a retailer may create a Customer Purchase Data Product that combines online sales, in-store transactions, loyalty activity, and inventory data into a single trusted asset. AI systems can use that product to personalize promotions, predict demand, and optimize inventory decisions. AI agents may also acquire external data, such as weather patterns or local event information, through smart contracts to further improve sales forecasting and operational planning.

The result is reduced duplication of data efforts, improved efficiency, faster AI-driven insights, and new opportunities to securely monetize retail data with partners and suppliers.

Yet despite these advantages, many organizations still struggle to justify investments in DaaP initiatives. Because data management operates as foundational infrastructure, its business impact is often indirect or difficult to measure, causing initiatives to lose momentum or fail to secure long-term support.

This article outlines a structured way to articulate the value of DaaP and build the organizational alignment needed to drive adoption.

Benefits across data producers, consumers, and governance teams

The value of DaaP becomes easier to understand when examined through the operational realities of the teams that create, consume, and govern enterprise data every day.

1. Producer efficiency and cost suppression

Adopting DaaP helps data producers reduce costs and improve efficiency by consolidating demand, promoting reuse, and minimizing duplication. According to McKinsey, adopting DaaP results in “Total cost of ownership, including technology, development, and maintenance costs, can decline by 30%.”

Platform and infrastructure costs

• Reduces storage and compute expenses by eliminating duplicated datasets.
• Minimizes pipeline execution costs by avoiding repeated transformations of similar data.

Engineering, development and integration costs

• Avoids the need to rebuild and retest similar data projects repeatedly.
• Cuts down on development time and effort via reusable pipelines and standardized transformation logic.
• Streamlines integration efforts by aligning data products to global standards and ensuring interoperability.

Support and enforcement costs for governance, security and compliance

• Lowers overhead by reducing data footprints and centralizing ownership and accountability.
• Simplifies support through well-managed, standardized data products.

2. Consumer empowerment and time-to-value

By providing FAIR data products, DaaP helps consumers access high-quality data faster and with less effort, improving discovery, context, and decision-making:

• Discovery and assessment costs: Reduces time and efforts spent searching and identifying relevant datasets by centralizing and standardizing data product listings with proper metadata. What used to take weeks or months can be reduced to minutes.

Cuts evaluation time from days or weeks to hours with well-documented metadata and sample data, enabling faster decision-making through clear context, lineage and usage guidance.

• Access and enablement costs: Accelerates access through self-service platforms and automated, role-based approval workflows, reducing the time and dependency on manual provisioning and support teams.
• Automation and scalability costs: Interoperable and modular approaches allow for future-ready architectures while machine-readable formats support agentic access and automation, reducing manual integration and enabling scalable consumption across systems.

3. Automated stewardship and governance 

By embedding governance into the design of data products, DaaP enables more effective and less resource-intensive stewardship and governance.

Data quality and remediation costs

• Reduces data cleansing efforts from multiple teams.
• Minimizes manual remediation work, saving labor costs and freeing up steward capacity.

Governance and oversight costs

• Improves visibility and control through built-in observability metrics and lineage tracking.
• Lowers governance overhead by automating policy enforcement across platforms and pipelines.

Compliance and risk avoidance costs

• Reduces exposure to regulatory and security risks through clear ownership, accountability and embedded controls at the data product level.
• Improved control over data and associated compliance reduces risk of fines and penalties.

Use case-driven business value

The operational efficiencies created by DaaP are important, but the larger strategic value emerges when organizations begin enabling new business capabilities, faster decisions, and AI-driven innovation at scale.

1. Better data leads to better decisions

High-quality, trusted data enables better decision-making across the business. This leads to tangible improvements such as identifying better-fit suppliers, optimizing business processes, and rationalizing enterprise software usage.

2. Faster time to value

With DaaP, “New business use cases can be delivered as much as 90% faster.” Whether it’s reconfiguring supply chains during disruptions, capturing upsell and cross-sell opportunities or realizing the value earlier from cost saving initiatives, the ability to move from insight to execution rapidly enhances competitiveness, improving both top line and bottom line.

3. Revenue and profit growth through data-driven products

Beyond internal operational gains, mature DaaP capabilities can also create entirely new forms of business value. Organizations increasingly use data products to enhance customer experiences, differentiate offerings, and unlock new revenue opportunities.

• Direct revenue generation: Customers may pay for data products directly, creating a new revenue stream.

For example, smartwatch users purchase advanced health analytics reports about themselves.

• Indirect revenue growth and profit gains: Enhanced features powered by data insights can increase customer satisfaction and loyalty, leading to higher sales revenue, higher customer loyalty and retention.
• Strategic data monetization and sharing: Packaging data into products enables internal collaboration, external monetization through value-added services, and strategic expansion. Trusted information becomes a scalable asset that fuels innovation and unlocks new business models.

For example, component providers can derive tangible business value by accessing insights on how their parts perform within the supply chain. A data product that enables suppliers to see where their components have passed or failed allows them to reduce defect rates, optimize quality while avoiding costly integration failures and driving savings for component providers.

Value from intelligence at scale enabled by AI/ML

DaaP enables AI by making enterprise data trusted, discoverable, governed, and reusable at scale. By treating data like a managed product, with clear ownership, quality standards, semantics, and APIs, DaaP provides the reliable foundation AI needs for accurate insights, automation, and autonomous decision-making.

With GenAI, data products can enable tailored and context-aware insights.

AI agents can monetize data products by autonomously discovering, purchasing, accessing, and exchanging data through smart contracts. The AI agent identifies the right data product based on business needs, evaluates quality and policy requirements, then executes a smart contract that automatically handles licensing, payment, access control, and revenue sharing.

This creates a scalable machine-to-machine economy where trusted data products can be consumed and monetized in real time with minimal human intervention.

Compounding value: The data flying wheel effect

The impact of adopting the DaaP extends beyond incremental gains. It creates a compounding effect. As an organization realizes cost savings, drives operational efficiency and improves decision-making, these gains are reinvested into the business, fueling a continuous improvement cycle of innovation.

This is the Data Flywheel in action: a self-reinforcing loop of efficiency, innovation and growth.

In summary, the valuation of DaaP hinges on recognizing that its adoption is a long-term journey. Organizations can start small by delivering quick wins to demonstrate value early and create a self-reinforcing data flywheel gradually through iterative development.

Over time, this compounding effect positions the enterprise for sustainable competitive advantage, making DaaP not just a technical investment but a strategic imperative.

*A follow-up article will explore practical first steps for DaaP adoption, organizational and change management considerations, and frameworks for measuring DaaP success and ROI.

About the Author:

Jenna Zhou, an Enterprise Architecture, Data and AI leader with a proven track record at Fortune 500 companies. Currently a Distinguished Engineer (Data Management and AI) with Dell where she has been growing her leadership and expertise through Product MDM, Customer MDM, Enterprise Data Quality, EMC Integration, GDPR, Data Governance and Strategy, Data Products, Data Marketplace, and Data Mesh, etc. Most recently, Jenna has built the Enterprise Information Architecture team from the scratch, spearheading Data as a Product initiative and is currently leading the Data Management Technical Authority team, influencing cross functional, cross BU teams and executives on future ready enterprise data management directions.

Before joining Dell, Jenna was with Eli Lilly where she initiated and achieved executive buy-in on data centric IT transformation well ahead of the industry. Jenna also briefly worked for Lenovo as the Director of Enterprise Architecture and re-established the Enterprise Architecture function there.