Data as a Product: Applying a Product Mindset to Data at Netflix

By Tomasz Magdanski

Introduction:
What if we treated data with the same care and intentionality as a consumer-facing product? Adopting a “data as a product” mindset means viewing data not as an incidental byproduct of systems, but as a core product in its own right. In practice, this means each data product is intentionally designed, built, maintained, and measured to create value. A data product has a clear purpose tied to a business decision, is created for a defined audience, and is continuously evaluated for utility, reliability, and accessibility. It is thoughtfully designed, guided by deliberate lifecycle management that includes both innovation, maintenance and retirement. Each product has explicit ownership, ensuring accuracy and availability, and it earns trust through consistency and quality.

At Netflix, data products — from high leverage telemetry, warehouse tables, key metrics and dashboards to ML models — power critical decisions in areas such as personalized recommendations, content strategy, product feature experimentation, customer acquisition, internal knowledge base, and even financial reporting. This deep reliance on data is how we internally create more value from data products — by using them to inform business decisions that enhance the member experience and drive the company forward. To maximize this value, we are advocating for a formal data product framework to ensure our data assets are managed with the same rigor and strategic focus as traditional products.

Why Treat Data as a Product?
Treating data as a product allows us to apply established product management principles to our data assets. Just as product teams define a clear purpose, target their users, measure success, and iterate on features, we can do the same for data. This shift in perspective yields several benefits: it clarifies the purpose and audience for each dataset, encourages thoughtful design and documentation for usability, enforces quality and reliability standards to build trust, and institutes a lifecycle with innovation and continuous improvement (and eventual retirement) rather than a “set and forget” approach. In short, a product mindset brings strategic alignment, usability, accountability, and better governance to our data investments. Treating data as a product means putting data users first: they are viewed as customers, and their needs guide the design and evolution of data solutions. With this context in mind, below are the key principles that define a data product at Netflix.

Key Principles of Data Products

• Clear Purpose: Every data product should exist for a reason — it should solve a problem or enable a specific business use case. If a data product doesn’t have a clear problem to solve or question to answer, it’s likely not worth creating. Owners of a data product must be able to articulate what value it delivers: for example, what decision does it inform or what insight does it provide? Defining a strong purpose prevents the creation of “data for data’s sake” and keeps our efforts focused on high-impact needs. In practice, this reflects the mantra of starting with business value when developing any new data asset.
• Defined Users (Audience): Just as consumer products have target customers, data products need clearly defined users. Identifying the who — the analysts, data scientists, engineers, tools, or business teams that will consume the data — helps shape the design. If no real users or use cases can be identified, the data product might be redundant. Thinking of data users as customers means understanding their needs and pain points upfront, so we can build data solutions that are actually useful and adopted. A data product designed without a specific audience in mind is likely to miss the mark or go unused.
• Measurable Value and Quality: A data product’s value is best demonstrated through its health, trustworthiness, and ability to support long-term innovation rather than prescriptive metrics. Netflix’s Data Health initiative frames this effort: a cross-functional push to reduce complexity and data debt, ensure adherence to standards, and make intentional tradeoffs about how data is treated. This focus is essential as generative AI models, automation, and new analytics use cases demand higher-quality, well-documented data. Measuring and improving data health across domains — rather than targeting hard-to-quantify impact — helps teams prioritize investments, simplify systems, and strengthen trust in our data ecosystem. Healthy data products enable faster discovery, better reliability, and a stronger foundation for AI-driven innovation.
  | Stay tuned: we’ll publish a dedicated blog post diving deeper into the Data Health program soon.
• Thoughtful Design and Documentation: A good data product is designed with the user in mind. This means data assets should be well-structured, intuitive to understand, and easy to access. Poorly designed or undocumented datasets can frustrate users just as a badly designed app would frustrate customers. To avoid this, we consider aspects like useful schema design, consistent definitions, and convenient access patterns when building data products. We also recommend documenting our data products — describing what the data represents, how to use it, and any known limitations — so that users can quickly trust and leverage the data without guesswork. In essence, we aim to make consuming a data product as straightforward as using a well-designed software product. Just as product design focuses on user experience, data product design should minimize friction and confusion for data consumers.
• Lifecycle Management: A data product’s evolution doesn’t stop at launch. Just as software products undergo updates and eventual deprecation, data products have a lifecycle that requires ongoing evaluation and maintenance. They should be designed with flexibility in mind, allowing extension or adaptation to new needs without costly rewrites. This balance of adaptability and discipline ensures we can add enhancements, adjust to emerging use cases, or retire assets when they become obsolete or lose value. Proactive lifecycle management prevents the accumulation of “data debt” — for example, stale tables or reports that linger without clear ownership or purpose. By planning for the full lifecycle, including eventual sunsetting, we keep our data ecosystem relevant, cost-effective, and resilient. Data products should not live forever by default: if a metric or report is outdated, we treat it like outdated software and phase it out to make room for better solutions.
• Clear Ownership: Every data product needs a clearly designated owner responsible for its success. Ownership entails ensuring the data product’s accuracy, availability, and usability on an ongoing basis. Just as a product manager or product team is accountable for a software service, a data product owner is accountable for the quality and reliability of a dataset or analytic product. Having explicit ownership prevents the common problem of “everyone and no one” being responsible for data issues. It provides a point of contact for questions or problems and drives accountability for maintenance. At Netflix, this often means assigning data products to the teams most closely aligned with their content — for example, a content data product is owned by a content data science and engineering teams. Clear ownership also facilitates faster issue resolution and continuous improvement, because someone is empowered to act when the data product needs attention.
• Trust and Reliability: Users will only treat data as a product if they can trust it. Therefore, reliability isn’t just a technical concern — it’s a core product feature. Data products must be dependable (e.g. correct, up-to-date, consistent) to earn and keep the trust of their users. If a data product is frequently wrong or unavailable, users quickly lose confidence and abandon it (just as they would uninstall a buggy app). Building trust means instituting strong data quality controls, validation tests, monitoring, and alerts as part of the product’s design. It also means being transparent about the data’s limitations and lineage. An excellent data product provides an exceptional user experience and trustworthiness by design. In fact, these two traits — great user experience and trust — are often cited as the defining characteristics of a true data product. By prioritizing reliability, we treat data quality issues as product issues, addressing root causes and preventing bad data from eroding stakeholder confidence.

Conclusion:
In summary, treating data as a product reframes how we build and manage data at Netflix. We produce data products to create value — meaning every important dataset, metric, or report is developed with purpose and maintained with rigor throughout its lifecycle. By recognizing data’s users, use cases, and lifecycle as fundamental attributes, we elevate data to a first-class entity in our organization’s thinking. This product-oriented approach emphasizes reliability, clear ownership, proper governance, and proactive management of data debt. Ultimately, the data-as-a-product mindset ensures that our data products are valuable, trusted, and aligned with Netflix’s strategic goals — just like any successful consumer product would be. We’re excited to adopt this framework across our teams, continuing to improve how we deliver insight and impact through data.