What this step is about

Most value comes from combining data products. This step focuses on interoperability so users and AI agents can join, compare, and reuse data without rewriting logic every time.

Common failure modes

• No shared join keys, inconsistent identifiers
• Time zones, currencies, and units vary across products
• Definitions drift ("active customer" means different things)
• Metrics are recalculated differently in each dashboard

Enabling interoperability between data products

While individual data products are valuable, greater impact comes from their combination. To enable seamless interoperability, it is essential to establish standardized join keys, common dimensions, and consistent data formats. Without such standards—such as time zones or date formatting—even technically accessible data can become analytically unreliable.

Implementing standardized APIs, shared schemas, and integration-ready datasets makes data products easier to join, analyze, and automate. In some cases, offering pre-integrated datasets can significantly reduce friction for business users, allowing them to plug data directly into tools and begin analysis immediately.

Flattening complexity through integration

Producing curated, integrated datasets for high-priority analytical needs simplifies the user experience. These datasets combine essential elements—such as customers, products, and sales—into ready-to-use formats. This "plug-and-play" approach eliminates barriers, speeds up insight generation, and increases business impact.

Outcome

Data products become building blocks. Combining them is the default, not a special project.