“Data becomes an asset not when it is collected, but when it is cared for, trusted, and delivered with purpose.” — Zhamak Dehghani
Data as a Product: Turning Raw Information into Value
In the early 2000s, companies thought of data as an inconvenient byproduct—something that collected dust in databases or warehouses, primarily used to justify decisions after the fact. It was infrastructure. It was plumbing. The most ambitious organizations built “data lakes” in hopes of future utility, but too often these devolved into “data swamps”—expensive, stagnant, and underused.
Then came the realization: data isn’t just exhaust; it’s an asset. And more than that, it can be a product—something designed, packaged, and managed with the same rigor we apply to software, hardware, or services.
What Does “Data as a Product” Mean?
“Data as a product” (DaaP) is the practice of treating data like any other offering:
- It has customers (internal teams, executives, external clients).
- It has roadmaps (new features, bug fixes, enhancements).
- It has service-level agreements (SLAs) (availability, freshness, accuracy).
- It has product managers responsible for maximizing its value.
In other words, instead of thinking of data pipelines as IT projects or one-off reports, organizations treat datasets, APIs, and insights as living products that need strategy, governance, and customer success.
Zhamak Dehghani, who pioneered the Data Mesh concept, is one of the most influential voices in this space. Her work highlights that data should be owned by domain teams and managed as products, with clear discoverability, quality, and usability for consumers.
Applying Product Thinking to Data
When we apply product theory to data, several concepts translate directly:
- Product Development
- Like software, data products have lifecycles. You start with discovery (what problem are we solving?), design (how will the data be structured and consumed?), development (ETL/ELT pipelines, modeling, APIs), and delivery.
- Example: A marketing team needs a “customer 360 dataset.” The data product goes through requirements gathering, MVP release (basic customer identity resolution), and iterative improvements (adding engagement signals, churn prediction, etc.).
- Product Management
- Data product managers must balance stakeholder needs, prioritize requests, and measure success. Metrics might include adoption rate, NPS from data consumers, query performance, or time-to-insight.
- Just as product managers say “no” to feature bloat, data PMs say “no” to poorly defined requests that erode quality or scalability.
- Product Operations (ProdOps for Data)
- Once in production, data products need monitoring (data observability), documentation (data catalogs), and customer support.
- SLAs for freshness (e.g., “daily by 6 AM EST”) or quality (“<1% null values in customer ID”) become the equivalent of uptime guarantees.
Internal Customers: Data Products Within the Organization
Done Well:
Netflix exemplifies treating data as a product internally. Every team—from content to streaming optimization—consumes well-defined data products via APIs and self-service dashboards. Data sets have owners, SLAs, and clear documentation, enabling product managers and engineers to build personalization features at scale.
Done Poorly:
Many enterprises still operate with shadow IT and ad-hoc reporting. Finance pulls numbers from one warehouse, Marketing from another, and Engineering has its own logs. None agree. Data is delivered as a one-off project, not a maintained product. The result: trust erodes, adoption lags, and every meeting begins with “whose numbers are right?”
External Customers: Data Products in SaaS B2B and B2C
B2B Example (Done Well):
Salesforce treats data as a product by offering APIs, analytics dashboards, and integrations with SLAs and customer support. The data layer isn’t just an afterthought—it’s a value proposition. Customers trust that the reports and APIs are accurate, timely, and evolving.
B2C Example (Done Well):
Spotify offers wrapped insights, personalized playlists, and listening analytics. These aren’t just “nice-to-have dashboards”—they’re packaged data products designed for end-user delight, with product managers iterating on new ways to make the data valuable and sticky.
Done Poorly:
Consider consumer-facing apps that provide “analytics” as an afterthought—slow, inaccurate, or opaque. A fitness app that misreports calories or fails to sync with wearables isn’t just annoying—it breaks trust. Customers leave, and competitors with well-managed data products win.
How to Get Started with Data as a Product
- Define Ownership
- Every dataset should have a clear owner. No more “central data team” black boxes.
- Establish Standards and SLAs
- Quality metrics, refresh rates, and discoverability should be explicit.
- Build Feedback Loops
- Like any product, data products need customer feedback, prioritization, and iteration.
- Measure Adoption and Impact
- Are teams actually using the dataset? Is it driving decisions or revenue?
- Invest in Product Management for Data
- Appoint dedicated data product managers who act as the bridge between business needs and technical delivery.
Wrapping up…
Treating data as a product is more than a slogan—it’s a mindset shift. It requires applying decades of proven product theory to the raw material of the digital age. When done well, it transforms internal decision-making, enables trusted external offerings, and turns data from an afterthought into a competitive advantage.Companies that succeed will look less like “data hoarders” and more like data product companies, delivering value continuously, iteratively, and intentionally.
