Introduction
As organizations become increasingly data-driven, two terms have gained prominence: **“data product”** and **“data-as-a-product.”** These terms sound similar but represent distinct concepts. A data product usually refers to a tangible data-driven solution or asset (e.g. a report, dashboard, or machine learning model) created to solve a specific problem. In contrast, data-as-a-product refers to an **approach or mindset** – treating data itself with the same care and strategic thinking as a product, including clear ownership, design, and user-centric development. This comparison clarifies the overlap and differences so that both technical and business audiences can leverage these concepts effectively.
What is a “Data Product”?
A **data product** is generally a consumable data asset or tool – the output of data analysis or engineering – that delivers specific value or insights. It is a packaged solution built on data to address a particular business need.
- Specific Purpose: Created to solve a defined problem (e.g., a credit risk model).
- High Quality & Reliability: Built on high-quality, well-prepared data; consumption-ready and reusable.
- Discoverability and Usability: Easily discoverable and accessible, often indexed in a data catalog with clear documentation.
Examples: Analytics Dashboards, Recommendation Engines, Curated Customer 360 Datasets, Fraud Detection Models.
What is “Data-as-a-Product” (DaaP)?
Data-as-a-Product (DaaP) is a mindset and methodology rather than a specific deliverable. It means applying product management principles to data itself – treating data as a first-class product that is designed, developed, and serviced for users.
- Product Thinking: Understanding who the customers of the data are and curating/packaging data to meet their needs.
- Clear Ownership: Each important dataset or domain has a dedicated owner accountable for its quality and evolution.
- Lifecycle Management: Data is versioned, iteratively improved, and changes are clearly communicated (e.g., via data contracts).
DaaP is the **approach**; data products are the **tangible deliverables** that result from applying this philosophy.
Conclusion
In today’s data-rich environment, simply having data is not enough – success comes from how well that data is harnessed and delivered. A **data product** is the **concrete realization** of data’s value, while **Data-as-a-Product** is the **philosophy** that ensures those data products are built on a solid foundation: high-quality data, clear ownership, and user-focused design.
The takeaway is clear: think of data not just as raw material, but as something to be crafted, packaged, and served to deliver value. By treating data as a user-friendly product, organizations democratize insights and drive competitive advantage.
Referenced Sources
- Data products vs. data as a product | dbt Labs
- What are data products? | SAP
- What Is Data as a Product (DaaP)? | IBM
- How Different Industries Develop & Use Data Products | Acceldata Blog
- How To Treat Your Data As A Product | Monte Carlo Data
- And other industry reports on data strategy, governance, and architecture.