Preparing Your First Data Product

Why most data products fail before they start

The problem is rarely technical. Most data products fail because they were built without a clear consumer, a defined scope, or an owner who cares about them beyond the initial project. This guide walks you through the five steps that prevent those failures.

What's inside

• Step 1: Start with the question, not the data — how to validate demand before you write a single line of code
• Step 2: Define scope and grain — the single most important design decision, and how to get it right
• Step 3: Assign ownership and governance — why "the data team" is not an owner
• Step 4: Build, test, and document — the implementation pattern that works across platforms
• Step 5: Publish and iterate — ship the 80%, get feedback, improve

Plus: a readiness checklist

A one-page checklist covering scope, ownership, build quality, metadata, and launch readiness. Use it as a gate before publishing any data product.

Who it's for

Data leaders, product owners, architects, and analysts building their first data product — on any platform. The principles are platform-agnostic; the examples lean toward SAP Datasphere because that's where the author has spent the most time.