.png)
Before you can redesign your operating model, you need to understand what you're organizing around. Too many data leaders jump straight to organizational charts—functional versus product-based, centralized versus federated—without first defining the product areas that will determine how teams should actually work. The structure should follow the products, not the other way around.
Data product areas emerge from your strategic objectives. They define the natural boundaries for team responsibilities, create clear ownership, and provide the framework for how value flows through your organization. Without this clarity, any organizational structure becomes arbitrary—a reshuffling of boxes on a chart that doesn't fundamentally change how work gets done.
Your strategic goals should translate directly into data product areas. If your company aims to grow direct sales by 30%, that suggests customer intelligence and sales analytics products. If leadership wants €3M in new revenue from AI, you're looking at AI-driven recommendations or automation products. When the goal is 25% operational efficiency improvement, you need process optimization and monitoring products.
This translation isn't about finding every possible data use case. It's about identifying 3 to 7 product areas that align with what the business actually needs to achieve. More than that and you lose focus. Fewer than that and you're probably not covering critical ground.
The challenge is that not everything fits neatly into a single product area. Data and AI capabilities exist in layers, and understanding these layers helps you think about where to invest and how to organize.
At the foundation, you have data and AI platform products—the infrastructure, tools, and foundations that enable others to build. Think data catalogs, feature stores, ML platforms. These are the plumbing. Unglamorous, but essential. When platform products fail, everything built on top of them struggles.
One layer up are foundational data products—the core data assets that multiple use cases depend on. Customer 360 views, product master data, operational metrics. These are the "golden records" that need to be right because everyone uses them. Get these wrong and every dashboard, every model, every analysis built on top is compromised.
Then come analytical data products—the reports, dashboards, and insights that support decision-making. Sales performance dashboards, customer segmentation analyses, operational metrics. This is where most traditional BI teams have focused their energy, and where self-service analytics aims to shift ownership to business users.
A layer above that are AI and ML model products—predictive or generative models that automate decisions. Demand forecasting, churn prediction, recommendation engines. These are products that actively do work, not just inform decisions. They operate in production systems, often without human review, which raises the stakes considerably.
Finally, at the top, are AI and data-infused business products—customer-facing products with embedded AI and data capabilities. Personalized shopping experiences, dynamic pricing, intelligent search. These are the products that your customers actually see and interact with.
Not every organization needs to operate at all five levels simultaneously. A company early in its data journey might focus entirely on foundational data products and analytics. A mature tech company might invest heavily in AI-infused business products while taking platform capabilities for granted. Start where the business value is clearest.
.png)
For each product area you identify, four questions need clear answers.
Who are the customers? Are they business users who need insights? Data teams who need infrastructure? End consumers who experience AI in your products? The answer determines everything from how you measure success to what skills your team needs.
What value does it deliver? Is this about cost savings through automation? Revenue growth through better decisions? Improved customer experience? Regulatory compliance? If you can't articulate the value in terms leadership cares about, you'll struggle to secure resources.
Who owns it? Should a product manager own this end-to-end? A domain lead? The platform team? Ownership isn't just about accountability—it's about having someone who wakes up thinking about how to make this product better, not just keep it running.