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Minimum viable data model

As explained in The Well-Grounded Data Analyst

A minimum viable data model is an initial data model that provides useful analytical capability with only the essential structure and processing.

A minimum viable data model delivers a useful first analytical solution with essential structure, while deferring complex refinement until stakeholders can react. It makes unresolved business questions visible instead of allowing the analyst to decide them alone. section 3.3.1

Use the first iteration to:

  • Expose questions about entity identity, such as whether Jane Smith and JS Motors represent the same customer. section 3.3.1
  • Reach a tangible solution quickly, potentially stopping before meaningful deduplication. section 3.3.1
  • Use feedback and business value to determine whether further iterations are worthwhile. section 3.3.1

How The Well-Grounded Data Analyst covers it

How deep it goes
Builds a tangible first solution but stops short of meaningful deduplication when unresolved business decisions require wider input.
Examples
Uses customers split across company and individual databases, including Jane Smith and JS Motors, to expose identity questions.
What it assumes
Assumes stakeholder input for entity-resolution decisions rather than leaving those judgments solely to the analyst.
What you can do after
Evaluate an initial model with stakeholder feedback and decide whether further improvement has business value.
How it connects
Connects data modeling with stakeholder feedback, customer identity, and deduplication.
Cover of The Well-Grounded Data Analyst
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Related concepts

This explainer was generated by Manning's knowledge graph from the text of The Well-Grounded Data Analyst. The section links point to the parts of the book it draws on.