Information Quality, Assumptions and Uncertainty

Not every available fact is good decision information

Information quality is not the same as information volume. Before using a number or observation, ask: Is it relevant to the decision? How and from what source was it collected? Is it current enough? What is missing? Is the stated precision justified? Data can be accurate but irrelevant, relevant but old, or current but drawn from a narrow sample. A sound decision makes those limits visible rather than presenting data as complete certainty.

Separate observations, estimates, assumptions and unknowns

An observation describes what was measured or seen within a known scope. An estimate is an approximate number or description based on data or a model. An assumption is something treated as temporarily true so work can proceed, but it should be visible. An unknown is something for which no useful estimate is yet available. These labels do not weaken a decision; they prevent an estimate becoming a “fact,” an assumption becoming a “rule,” or an unknown being silently treated as zero.

Uncertainty does not mean stopping

Uncertainty can be represented with a range, a scenario or a clear description rather than a single unjustified number. We might say implementation is likely to take three to five weeks depending on supplier availability, or that future demand may be close to current demand but seasonality has not been tested. The goal is not to create more numbers; it is to expose what could change the choice. If the same alternative remains suitable across a reasonable range, extra precision may not be worth the time. If a small change flips the choice, the information is decision-sensitive.