The first step in deriving data product areas from strategic goals is to define your customers, the value you offer, and your capabilities. Not all customers are data experts, so presenting data in a way they understand is crucial.

Once you know your customers and value proposition, connect them to your strategic goals to define product areas. Aim for 1 to 5 product areas, with a maximum of 7, to maintain focus.

Break down your company goals and translate them into data product areas. Remember that not everything is GenAI; consider other forms of data, analytics & AI products when prioritizing initiatives.

The Data Product Hierarchy: From Foundation to Value

Data products exist at different levels of abstraction, each building on the layer below to deliver increasingly direct business value:

• Platform products form the foundation, providing the infrastructure and capabilities that enable all other data products
• Data products sit on top of the platform, delivering curated datasets that serve as the data foundation for downstream consumption
• Metric products transform data into measurable information, tracking KPIs and business metrics
• Analytics products generate insights by analyzing patterns, trends, and correlations in the data
• AI products deliver direct business value through intelligent automation, predictions, and recommendations

When defining your product areas, consider which layers are most critical to your strategic goals. Not every organization needs to operate at all five levels simultaneously—start where the business value is clearest and build your capabilities over time.

Overall, establishing clear product areas and organizational structure, prioritizing business value, and fostering collaboration are crucial for effective data product management and achieving data-driven success.

Key Considerations for Defining Data Product Areas and Organizational Setup