Strategic Definition

Defining the Data Product

Moving beyond "data as an asset" to "data as a product." Explore the characteristics, archetypes, and value drivers that transform raw information into a consumable, valuable business utility.

The Paradigm Shift

A Data Product is not just a table in a database. It requires a fundamental shift in mindset from managing data as a static technical asset (Project Mindset) to managing it as a thriving, evolving product (Product Mindset). Use the toggle below to compare the approaches.

Project Mindset
Product Mindset

Core Characteristics (DATSIS)

For data to function as a product, it must adhere to specific standards. We use the DATSIS framework to define these non-negotiable attributes. Click any card below to dive deeper.

Archetypes of Data Products

Data products come in various shapes and sizes depending on who consumes them and how. Explore the three primary archetypes below.

Source-Aligned Data Products

These reflect the domain operational systems closely. They represent the "truth" of a specific business capability (e.g., Users, Orders) but cleaned and standardized for consumption.

Key Use Case

Providing a clean 'Orders' dataset to be used by finance, marketing, and logistics teams simultaneously.

The Value Equation

Why adopt a product approach? It reduces friction and increases reuse. The metrics below highlight the typical impact of mature data product adoption.

Time-to-Insight Reduction

Comparison of time spent (in weeks) on data discovery and prep vs. analysis.

70% reduction in setup time

Cumulative Value via Reuse

As products are reused, the cost per use drops while value scales linearly.

High reuse drives ROI exponentially