Validation and Data Integrity
Validation rejects unacceptable input, while data integrity means stored information remains accurate, consistent, and related according to product rules. Enforce critical invariants at the strongest reliable boundary, including the database where appropriate.
What You Will Be Able to Decide
- Explain validation and data integrity in product and business terms.
- Apply this decision: Enforce critical invariants at the strongest reliable boundary, including the database where appropriate.
- Recognise this material risk: different clients accept impossible or contradictory states into permanent storage.
- Ask a consultant for evidence rather than reassurance.
A founder is deciding how the product should remember information and preserve its meaning over time.
Validation rejects unacceptable input, while data integrity means stored information remains accurate, consistent, and related according to product rules.
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.
Why This Decision Appears
A founder is deciding how the product should remember information and preserve its meaning over time.
The immediate question is validation and data integrity. The technical label matters only because it changes a product decision, a responsibility, or the evidence required before launch.
Technical term
Validation and Data Integrity
Validation rejects unacceptable input, while data integrity means stored information remains accurate, consistent, and related according to product rules.
Treat it like a clause in a commercial agreement: its value comes from making expectations and consequences clear, not from sounding formal.
The Working Principles
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: Enforce critical invariants at the strongest reliable boundary, including the database where appropriate.
- 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.
How to Choose Without Overbuilding
Enforce critical invariants at the strongest reliable boundary, including the database where appropriate.
The principal risk is that different clients accept impossible or contradictory states into permanent storage. 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.
A Useful Proposal and an Impressive-sounding One
Warning Signs
- Nobody can explain how validation and data integrity changes a user or business outcome.
- The proposal does not address this risk: different clients accept impossible or contradictory states into permanent storage.
- 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 validation and data integrity?
- Which user or business outcome does the recommendation protect?
- How have we reduced or accepted this risk: different clients accept impossible or contradictory states into permanent storage.
- 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
Validation rejects unacceptable input, while data integrity means stored information remains accurate, consistent, and related according to product rules. The founder's job is to make the consequence explicit; the consultant's job is to recommend and demonstrate a proportionate implementation.