For years, organizations have invested heavily in dashboards, data warehouses, and centralized reporting platforms. Yet many still struggle to turn their growing volumes of data into real business results. The problem isn’t the data, it’s how we think about it. Business leaders must treat data not merely as an asset to be stored and queried, but as a product – something to be built, maintained, and continuously improved. This approach, known as data as a product (DaaP), is gaining momentum as companies seek more scalable and reliable ways to drive business decisions with data.

In this blog post, we’ll share some core components and highlights of data as a product.

Moving Beyond Dashboards

Traditional business intelligence tools often deliver snapshots in time. They’re useful, but not built for flexibility or reuse. Business teams still wait days or weeks for access to new insights, and IT departments are stretched thin trying to meet competing demands.

The data-as-a-product approach changes that. Instead of managing one-off data requests or building reports from scratch, organizations create standardized, reusable data products, like a customer 360 view or a supply chain health score, that can be consumed across multiple teams. These products are designed with business users in mind, not just technical specs. They’re documented, quality-checked, and delivered through self-service platforms that make data easier to find and use.

Structure and Ownership Matter

One of the key components of the data-as-a-product approach is that successful data products need clear ownership. That’s where the role of the data product owner comes in: someone responsible for understanding the needs of users, managing priorities, and working with data engineering teams to deliver value. Like any good product manager, they gather feedback, track performance, and ensure the product evolves with business needs.

An operating model can help support this structure. Horizontal teams focus on infrastructure – pipelines, storage, observability – while vertical teams build data products aligned to specific domains. That separation allows each team to specialize without losing sight of the bigger picture.

Not Just Faster, but Better

Data as a product isn’t just about speed. This approach improves the quality and impact of data initiatives in several ways:

• Faster access to actionable insights, with pre-built data products ready for use across departments
• Stronger data quality and governance embedded directly into each product 
• Lower costs, thanks to shared infrastructure and fewer redundant projects
• Improved agility, with teams able to adapt quickly as needs change

Perhaps most importantly, it shifts the conversation from “What data do we have?” to “What do we need to know to make more informed decisions?”

The Role of AI in Data as a Product

Artificial intelligence (AI) plays a growing role in this model. AI can help automate parts of the data product lifecycle, like metadata generation or anomaly detection, but it also has potential as a built-in feature of the product itself. Think recommendation engines, predictive scoring, or conversational interfaces that let users ask questions in plain language.

AI doesn’t just help build the product. It makes it smarter, more accessible, and more useful.

A More Sustainable Way to Work with Data

As organizations mature in their use of data, the limitations of project-based, dashboard-driven models are becoming more obvious. The DaaP approach offers an alternative: one that emphasizes usability, accountability, and long-term value.

It’s not a silver bullet. It takes coordination, investment, and a shift in mindset. But for companies looking to make data a more integrated part of how they operate, rather than a service to be requested, it’s a step in the right direction.