A Practical Guide to Data Products and Data Product Owners
In today’s data-driven world, more organizations are recognizing the immense value of treating data not just as a byproduct of operations, but as a strategic asset. This shift has given rise to the concept of “data products” and the critical role of the “Data Product Owner” (DPO).
This guide explores what data products are, what makes them valuable, the evolving role of the DPO, and key lessons for scaling their impact across the business.
What Are Data Products?
A data product is more than just raw data — it’s a curated, reusable package that includes data and its related components, designed to deliver specific value to a defined set of users. IBM describes data products as assets that are:
- Easily discoverable
- Well-documented and structured
- Secure and access-controlled
- Reusable and interoperable
- Tailored for business needs
Data products range from APIs and dashboards to curated datasets and predictive models. The core philosophy is to apply a product mindset to data, identifying user needs, building with intent, delivering iteratively, and continuously improving.
The Role of the Data Product Owner (DPO)
At the heart of successful data products is the Data Product Owner, a strategic leader who connects technical teams, business stakeholders, and end-users. The DPO ensures that data products align with organizational goals and deliver measurable value.
Key Responsibilities:
- Vision and Strategy: Define the product roadmap in alignment with business goals.
- Stakeholder Engagement: Translate business needs into product features and maintain strong communication across teams.
- Backlog Management: Prioritize user stories and guide development with a focus on value creation.
- Data Governance: Ensure data quality, regulatory compliance, and proper metadata documentation.
- Adoption and Impact: Drive user engagement through training and support, while tracking KPIs to measure business impact.
- Technical Fluency: Understand data technologies well enough to translate business requirements into technical terms and collaborate effectively with engineers and data scientists.
Strategies for Scaling Data Products
According to McKinsey’s “The missing data link: Five practical lessons to scale your data products,” scaling data products requires both strategic alignment and operational excellence. Here are five key lessons:
Focus on Business Value: Prioritize use cases that deliver tangible, measurable outcomes. Create reusable products that support multiple scenarios.
Treat Data as an Economic Asset: Design for efficiency, reusability, and quick time-to-value. Reduce redundancy and accelerate impact.
Build Scalable Infrastructure:
- Design for change (new data sources, evolving needs).
- Use standard APIs and connectors.
- Create user-friendly marketplaces for data discovery.
- Automate DataOps for governance and scalability.
Empower DPOs: Equip DPOs with business insight and technical know-how. Engage them early and give them ownership to drive adoption and outcomes.
Leverage Generative AI: Use GenAI to speed up development cycles — from writing user stories and generating code to automated testing — enhancing productivity and reducing time to market.
Conclusion
The rise of data products marks a turning point in how organizations generate and leverage value from data. By adopting a product-oriented approach, empowering DPOs, investing in scalable infrastructure, and integrating emerging technologies like generative AI, businesses can transform data into a competitive advantage.
This guide is a roadmap for organizations ready to bridge the gap between data potential and business impact — unlocking innovation, improving decision-making, and accelerating growth.
Author: Stephanie Thompson
References:
- Acceldata. “Unlocking Data Value: The Critical Role of the Data Product Owner.” (Undated). Retrieved from https://www.acceldata.io/blog/data-product-owners-bridging-the-gap-between-data-and-business
- IBM. “What Is a Data Product?” (Undated). Retrieved from https://www.ibm.com/think/topics/data-product
- McKinsey & Company. “The missing data link: Five practical lessons to scale your data products.” April 23, 2025. Retrieved from https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-missing-data-link-five-practical-lessons-to-scale-your-data-products
- Tamr. “Data Product Owner: Job Description & Responsibilities.” (Undated). Retrieved from https://www.tamr.com/blog/data-product-owner-role-responsibilities
- datascience-pm.com. “7 Core Functions of a Data Product Owner.” (Undated). Retrieved from https://www.datascience-pm.com/data-product-owner/
- Zeenea. “What is a Data Product Owner? Role, Skills & Responsibilities.” (Undated). Retrieved from https://zeenea.com/what-is-a-data-product-owner-role-skills-responsibilities/
