Designing a Basic Data Model
A data model names the product's entities, attributes, identities, relationships, constraints, and lifecycle. Begin with business facts and rules, then test the model against representative workflows and changes.
What You Will Be Able to Decide
- Explain designing a basic data model in product and business terms.
- Apply this decision: Begin with business facts and rules, then test the model against representative workflows and changes.
- Recognise this material risk: the schema mirrors current forms rather than the durable concepts the business depends on.
- Ask a consultant for evidence rather than reassurance.
A founder is deciding how the product should remember information and preserve its meaning over time.
A data model names the product's entities, attributes, identities, relationships, constraints, and lifecycle.
A consultant can recommend and implement the technical approach. The founder still needs to decide which outcome matters, which risk is acceptable, and what evidence is sufficient.
The Founder Situation
A founder is deciding how the product should remember information and preserve its meaning over time.
The immediate question is designing a basic data model. The technical label matters only because it changes a product decision, a responsibility, or the evidence required before launch.
Technical term
Designing a Basic Data Model
A data model names the product's entities, attributes, identities, relationships, constraints, and lifecycle.
Treat it like a clause in a commercial agreement: its value comes from making expectations and consequences clear, not from sounding formal.
What Matters in Practice
Start with the product consequence, then choose the simplest technical treatment that protects it. A longer tool list is not a stronger plan.
For this decision, the useful standard is that the data model can represent the real business rules without ambiguity or silent corruption.
- Make the decision explicit: Begin with business facts and rules, then test the model against representative workflows and changes.
- Ask what evidence would show that the chosen approach works.
- Name the person or provider responsible when the approach fails.
- Record the result in the data model and recovery plan.
A Proportionate Decision
Begin with business facts and rules, then test the model against representative workflows and changes.
The principal risk is that the schema mirrors current forms rather than the durable concepts the business depends on. This does not require the most expensive possible solution. It requires the consequence to be understood and the control to match it.
- Describe the user or business outcome that must be protected.
- Identify the most credible failure and its consequence.
- Compare the simplest adequate approach with one realistic alternative.
- Set a review point for when the decision may need to change.
Strong Evidence and Weak Reassurance
Warning Signs
- Nobody can explain how designing a basic data model changes a user or business outcome.
- The proposal does not address this risk: the schema mirrors current forms rather than the durable concepts the business depends on.
- The only evidence is a successful demonstration of the easiest path.
- The decision has no named owner, boundary, or review point.
- A provider-specific feature is being mistaken for a permanent product requirement.
Questions to Ask a Consultant
- What decision are we making about designing a basic data model?
- Which user or business outcome does the recommendation protect?
- How have we reduced or accepted this risk: the schema mirrors current forms rather than the durable concepts the business depends on.
- What evidence can I review without relying on the original implementer?
- What is deliberately deferred, and when will it be reconsidered?
- Who owns the accounts, data, documentation, and recovery process?
Key takeaway
Key Takeaway
A data model names the product's entities, attributes, identities, relationships, constraints, and lifecycle. The founder's job is to make the consequence explicit; the consultant's job is to recommend and demonstrate a proportionate implementation.