Core idea: Data quality means that inputs are accurate enough, consistent and fit for the decision being made.
When this model is useful
Use a data-quality review when options rely on different source systems, vendor claims, national averages or estimates collected for another purpose.
The method is most useful when the question, alternatives and time horizon are stated before calculations begin. It should clarify tradeoffs and identify which assumptions deserve attention, not merely produce a score.
Inputs and evidence
Source, date, scope, definitions, units, missing observations, measurement method, incentives and uncertainty ranges.
Use consistent units, definitions and periods across options. Mark estimates clearly, record their source and use ranges when precision is not supported. Evidence should be proportionate to the cost, risk and reversibility of the choice.
Step-by-step method
Normalize units and periods, verify definitions, identify missing or biased observations, prefer direct evidence for sensitive inputs and round results to the precision the data supports.
- Write the decision question and accountable owner.
- List realistic alternatives and eliminate any that fail hard constraints.
- Collect evidence and separate verified facts from assumptions.
- Run a base case and at least one downside test.
- Record the chosen option, accepted tradeoffs and review triggers.
Practical example
A national average failure rate may be accurate but irrelevant to one older facility operating under different conditions.
The point of the example is not the exact numbers. It is the discipline of using the same boundaries for every option and making the decision drivers visible.
Common mistake and limitation
More data is not automatically better. Use sensitivity analysis to identify which inputs deserve validation.
Review whether the result changes under reasonable alternative assumptions. A close or fragile ranking should be presented as such rather than converted into false certainty.
Questions to ask before deciding
- What evidence would change the preferred option?
- Which consequence is missing because it is difficult to measure?
- Could a threshold or constraint override the numerical result?
- Who receives the benefits and who bears the costs or risk?
- When should the decision be reviewed?
Professional context: Legal, tax, investment, safety, medical, engineering and regulated decisions require qualified, jurisdiction-specific advice.