Let me support my question with a quick scenario.
We're writing an app for family meal planning. We'll produce daily plans with a target calorie goal and meals to achieve it for our nuclear family. Our calorie goal will be calculated for each person from their attributes (gender, age, weight, activity level).
The weight attribute is the simplest example here.
When Dad (the fascist nerd who is inflicting this on his family) first uses the application he throws approximate values into it for Daughter. He thinks she is 5'2" (157 cm) and 125 lbs (56kg). The next day Mom sits down to generate the menu and looks back over what the bumbling Dad did, quietly fumes that he can never recall anything about the family, and says the value is really 118 lbs!
This is the first introduction of the discord. It seems, in this scenario, Mom is probably more correct that Dad. Though both are only an approximation of the actual value.
The next day the dear Daughter decides to use the program and sees her weight listed. With the vanity only a teenager could muster she changes the weight to 110 lbs. Later that day the Mom returns home from a doctor's visit the Daughter needed and decides that it would be a good idea to update her Daughter's weight in the program. Hooray, another value, this time 117 lbs.
Now how do you reconcile these data points? Measurement error, confidence in parties, bias, and more all confound the data. In some idealized world we'd have a weight authority of some nature providing the one and only truth. How about in our world though?
And the icing on the cake is that this single data point changes over time.
How have you guys solved or managed this conflict?